{ "divisions": [ { "id": "monitoring", "name": "Monitoring and decision support", "short": "Monitoring", "description": "Sensors, IoT nodes, GNSS and vegetation indices, and how their readings become management decisions." }, { "id": "perception", "name": "Perception and AI models", "short": "Perception", "description": "Detection, segmentation, tracking, yield and variety models that read vineyard images, and how they run on limited hardware." }, { "id": "mapping", "name": "3D mapping and reconstruction", "short": "3D mapping", "description": "SLAM, semantic 3D maps, LiDAR and viewpoint planning that give a spatial model of the vineyard." }, { "id": "uav", "name": "UAV platforms and flight", "short": "UAV platforms", "description": "Building and flying the drone: platforms, payloads, imaging set-ups, mission and path planning, obstacle avoidance." }, { "id": "control", "name": "Manipulation and control", "short": "Control", "description": "Acting on the plant: picking points, grasping, aerial manipulation and field robots for pruning, spraying and harvest." }, { "id": "deployment", "name": "Deployment, economics and adoption", "short": "Deployment", "description": "Cost, profitability, adoption barriers, data reuse and what decides whether a solution transfers to another vineyard." }, { "id": "datasets", "name": "Datasets", "short": "Datasets", "description": "Public data for training and testing. Datasets that are also in the reconstruction catalog link to it." }, { "id": "methods", "name": "General methods", "short": "Methods", "description": "Algorithms the paper cites as background, such as detectors, trackers, SLAM, clustering and search. Not specific to agriculture.", "background": true } ], "facets": { "type": [ "Review", "Method", "System", "Dataset", "Economics or adoption", "Guidelines" ], "crop": [ "Grapevine", "Other crops", "Agriculture in general", "Not crop-specific" ], "platform": [ "UAV", "Ground robot or vehicle", "Handheld or ground camera", "Fixed sensors and IoT", "Laboratory", "Several platforms", "Not platform-specific", "Unspecified" ] }, "papers": [ { "n": 1, "key": "Bono:2026", "title": "A perspective analysis of imaging-based monitoring systems in precision viticulture: Technologies, intelligent data analyses and research challenges", "authors": [ "Annaclaudia Bono", "Cataldo Guaragnella" ], "authors_short": "Bono and Guaragnella", "year": 2026, "venue": "Artificial Intelligence in Agriculture", "doi": "10.1016/j.aiia.2025.08.001", "url": "https://doi.org/10.1016/j.aiia.2025.08.001", "division": "monitoring", "also": [ "perception" ], "type": "Review", "crop": "Grapevine", "platform": "Several platforms", "in_catalog": false, "note": "Imaging-based monitoring in viticulture. RGB cameras are the most common sensor and grape bunches the most common target. Notes that new methods are rarely compared with traditional agronomic techniques.", "cited_in": [ "Introduction", "Technologies for Precision Viticulture", "Related work" ], "bibtex": "@article{Bono:2026,\nauthor = {Bono, Annaclaudia and Guaragnella, Cataldo},\nyear = {2026},\nmonth = {03},\npages = {62-84},\ntitle = {A perspective analysis of imaging-based monitoring systems in precision viticulture: Technologies, intelligent data analyses and research challenges},\nvolume = {16},\njournal = {Artificial Intelligence in Agriculture},\ndoi = {10.1016/j.aiia.2025.08.001}\n}" }, { "n": 2, "key": "Vineyard1:2025", "title": "Unleashing profitability of vineyards through the adoption of unmanned aerial vehicles technology systems: the case of two Italian wineries", "authors": [ "Serena Sofia", "Martina Agosta", "Antonio Asciuto", "Maria Crescimanno", "Antonino Galati" ], "authors_short": "Sofia et al.", "year": 2025, "venue": "Precision Agriculture", "doi": "10.1007/s11119-025-10236-2", "url": "https://doi.org/10.1007/s11119-025-10236-2", "division": "deployment", "also": [ "uav" ], "type": "Economics or adoption", "crop": "Grapevine", "platform": "UAV", "in_catalog": false, "note": "Four-year study of two Italian vineyards before and after UAV adoption. Vigour maps supported treatment planning and selective harvest, and the benefit depended on vineyard size and context.", "cited_in": [ "Introduction", "Related work" ], "bibtex": "@article{Vineyard1:2025,\nauthor = {Sofia, Serena and Agosta, Martina and Asciuto, Antonio and Crescimanno, Maria and Galati, Antonino},\nyear = {2025},\nmonth = {03},\npages = {1-28},\ntitle = {Unleashing profitability of vineyards through the adoption of unmanned aerial vehicles technology systems: the case of two Italian wineries},\nvolume = {26},\njournal = {Precision Agriculture},\ndoi = {10.1007/s11119-025-10236-2}\n}" }, { "n": 3, "key": "DronesPA:2024", "title": "Drones in Precision Agriculture: A Comprehensive Review of Applications, Technologies, and Challenges", "authors": [ "Ridha Guebsi", "Sonia Mami", "Karem Chokmani" ], "authors_short": "Guebsi et al.", "year": 2024, "venue": "Drones", "doi": "10.3390/drones8110686", "url": "https://doi.org/10.3390/drones8110686", "division": "uav", "also": [ "deployment" ], "type": "Review", "crop": "Agriculture in general", "platform": "UAV", "in_catalog": false, "note": "End-to-end review of agricultural UAV systems, from payloads and mission planning to decision support. Cited for its adoption barriers: regulation, cost, rural infrastructure, operator skills, autonomy and payload.", "cited_in": [ "Introduction", "Technologies for Precision Viticulture", "Related work" ], "bibtex": "@article{DronesPA:2024,\nauthor = {Guebsi, Ridha and Mami, Sonia and Chokmani, Karem},\nyear = {2024},\nmonth = {11},\npages = {686},\ntitle = {Drones in Precision Agriculture: A Comprehensive Review of Applications, Technologies, and Challenges},\nvolume = {8},\njournal = {Drones},\ndoi = {10.3390/drones8110686}\n}" }, { "n": 4, "key": "DIMITRI2024e31648", "title": "Precision agriculture for wine production: A machine learning approach to link weather conditions and wine quality", "authors": [ "Giovanna Maria Dimitri", "Alberto Trambusti" ], "authors_short": "Dimitri and Trambusti", "year": 2024, "venue": "Heliyon", "doi": "10.1016/j.heliyon.2024.e31648", "url": "https://doi.org/10.1016/j.heliyon.2024.e31648", "division": "monitoring", "also": [], "type": "Method", "crop": "Grapevine", "platform": "Not platform-specific", "in_catalog": false, "note": "Chianti Classico case study linking climate, bioclimatic indices and vineyard characteristics to wine quality with machine learning. Its reliance on Tuscan data limits generalisation.", "cited_in": [ "Introduction", "Related work" ], "bibtex": "@article{DIMITRI2024e31648,\ntitle = {Precision agriculture for wine production: A machine learning approach to link weather conditions and wine quality},\njournal = {Heliyon},\nvolume = {10},\nnumber = {11},\npages = {e31648},\nyear = {2024},\nissn = {2405-8440},\ndoi = {10.1016/j.heliyon.2024.e31648},\nurl = {https://www.sciencedirect.com/science/article/pii/S2405844024076795},\nauthor = {Giovanna Maria Dimitri and Alberto Trambusti},\nkeywords = {Precision agriculture, Machine learning, Clustering, Wine production, Climate change},\nabstract = {The agricultural sector, in particular viticulture, is highly susceptible to variations in the environment, crop conditions, and operational factors. Effectively managing these variables in the field necessitates observation, measurement, and responsive actions. Leveraging new technologies within the realm of precision agriculture, vineyards can enhance their long-term efficiency, productivity, and profitability. In our work we propose a novel analysis of the impact of pedoclimatic factors on wine, with a case study focusing on the Denomination of Controlled and Guaranteed Origin Chianti Classico (DOCG), a prime wine-producing region located in Tuscany, between the provinces of Siena and Florence. We first collected a novel dataset, where geographic information as well as wine quality information were collected, using publicly available sources. Using such geographic information retrieved and an unsupervised machine learning approach, we conducted an in-depth examination of pedoclimatic and production data. To collect the whole set of possibly relevant features, we first assessed the region's morphological attributes, including altitude, exposure, and slopes, while pinpointing individual wineries. Subsequently we then calculated crucial viticultural indices such as the Winkler, Huglin, Fregoni, and Freshness Index by utilizing daily temperature records from Chianti Classico, and we further related them to an assessment of wine quality. In addition to this, we designed and distributed a survey conducted among a sample of wineries situated in the Chianti Classico area, obtaining valuable insights into local data. The primary goal of this study is to elucidate the interrelationships between various parameters associated with the region, considering influential factors such as the environment, viticulture, and field operations that significantly impact wine production. By doing so, wineries could potentially unlock the full potential of their resources. In fact, through the unsupervised and correlation analysis we could elucidate the relationships existing between the pedoclimatic parameters of the region, considering the most important factors such as viticulture and field operations, and relate them to wine quality as for instance using the survey data collected. This study represents an unprecedent in the literature, and it could pave the path for future studies focusing on the importance of climatic factors into production and quality of wines.}\n}" }, { "n": 5, "key": "UAVAI:2018", "title": "UAV and Artificial Intelligence in Precision Viticulture", "authors": [ "Filippo Gambella" ], "authors_short": "Gambella", "year": 2018, "venue": "JOJ Horticulture & Arboriculture", "doi": "10.19080/JOJHA.2018.01.555569", "url": "https://doi.org/10.19080/JOJHA.2018.01.555569", "division": "uav", "also": [ "perception" ], "type": "Review", "crop": "Grapevine", "platform": "UAV", "in_catalog": false, "note": "Overview of UAV and artificial intelligence use in precision viticulture.", "cited_in": [ "Technologies for Precision Viticulture" ], "bibtex": "@article{UAVAI:2018,\nauthor = {Gambella, Filippo},\nyear = {2018},\nmonth = {05},\npages = {},\ntitle = {UAV and Artificial Intelligence in Precision Viticulture},\nvolume = {1},\njournal = {JOJ Horticulture \\& Arboriculture},\ndoi = {10.19080/JOJHA.2018.01.555569}\n}" }, { "n": 6, "key": "Wang:2025", "title": "A survey of unmanned aerial vehicles and deep learning in precision agriculture", "authors": [ "Dashuai Wang", "Minghu Zhao", "Zhuolin Li", "Sheng Xu", "Xiaohu Wu", "Xuan Ma", "Xiaoguang Liu" ], "authors_short": "Wang et al.", "year": 2025, "venue": "European Journal of Agronomy", "doi": "10.1016/j.eja.2024.127477", "url": "https://doi.org/10.1016/j.eja.2024.127477", "division": "perception", "also": [ "uav" ], "type": "Review", "crop": "Agriculture in general", "platform": "UAV", "in_catalog": false, "note": "Survey of UAVs combined with deep learning across precision agriculture.", "cited_in": [ "Technologies for Precision Viticulture" ], "bibtex": "@article{Wang:2025,\nauthor = {Wang, Dashuai and Zhao, Minghu and Li, Zhuolin and Xu, Sheng and Wu, Xiaohu and Ma, Xuan and Liu, Xiaoguang},\nyear = {2025},\nmonth = {03},\npages = {127477},\ntitle = {A survey of unmanned aerial vehicles and deep learning in precision agriculture},\nvolume = {164},\njournal = {European Journal of Agronomy},\ndoi = {10.1016/j.eja.2024.127477}\n}" }, { "n": 7, "key": "Molnar:2025", "title": "Close proximity aerial image for precision viticulture. A review", "authors": [ "Szilárd Molnár", "Levente Tamas" ], "authors_short": "Molnár and Tamas", "year": 2025, "venue": "Journal of Plant Diseases and Protection", "doi": "10.1007/s41348-024-01050-5", "url": "https://doi.org/10.1007/s41348-024-01050-5", "division": "uav", "also": [ "monitoring" ], "type": "Review", "crop": "Grapevine", "platform": "UAV", "in_catalog": false, "note": "Review of close-range aerial imaging in viticulture. Cited on why models need validation in each new vineyard.", "cited_in": [ "Technologies for Precision Viticulture" ], "bibtex": "@article{Molnar:2025,\nauthor = {Molnár, Szilárd and Tamas, Levente},\nyear = {2025},\nmonth = {01},\npages = {},\ntitle = {Close proximity aerial image for precision viticulture. A review},\nvolume = {132},\njournal = {Journal of Plant Diseases and Protection},\ndoi = {10.1007/s41348-024-01050-5}\n}" }, { "n": 8, "key": "GALATI2025487", "title": "Economics and barriers of precision viticulture technologies: A comprehensive systematic literature review", "authors": [ "Antonino Galati", "Serena Sofia", "Maria Crescimanno" ], "authors_short": "Galati et al.", "year": 2025, "venue": "Information Processing in Agriculture", "doi": "10.1016/j.inpa.2025.04.001", "url": "https://doi.org/10.1016/j.inpa.2025.04.001", "division": "deployment", "also": [], "type": "Review", "crop": "Grapevine", "platform": "Several platforms", "in_catalog": false, "note": "Systematic review of the economics of precision viticulture technologies and the barriers to their adoption.", "cited_in": [ "Technologies for Precision Viticulture", "Deployment considerations" ], "bibtex": "@article{GALATI2025487,\ntitle = {Economics and barriers of precision viticulture technologies: A comprehensive systematic literature review},\njournal = {Information Processing in Agriculture},\nvolume = {12},\nnumber = {4},\npages = {487-500},\nyear = {2025},\nissn = {2214-3173},\ndoi = {10.1016/j.inpa.2025.04.001},\nurl = {https://www.sciencedirect.com/science/article/pii/S2214317325000198},\nauthor = {Antonino GALATI and Serena SOFIA and Maria CRESCIMANNO}\n}" }, { "n": 9, "key": "agronomy15112569", "title": "UAV-Based Spectral and Thermal Indices in Precision Viticulture: A Review of NDVI, NDRE, SAVI, GNDVI, and CWSI", "authors": [ "Adrián Vera-Esmeraldas", "Sebastián Pizarro-Oteíza", "Mariela Labbé", "Francisco Rojo", "Fernando Salazar" ], "authors_short": "Vera-Esmeraldas et al.", "year": 2025, "venue": "Agronomy", "doi": "10.3390/agronomy15112569", "url": "https://doi.org/10.3390/agronomy15112569", "division": "monitoring", "also": [ "uav" ], "type": "Review", "crop": "Grapevine", "platform": "UAV", "in_catalog": false, "note": "Review of UAV spectral and thermal indices (NDVI, NDRE, SAVI, GNDVI, CWSI) for vigour, stress and water status.", "cited_in": [ "Technologies for Precision Viticulture" ], "bibtex": "@Article{agronomy15112569,\nAUTHOR = {Vera-Esmeraldas, Adrián and Pizarro-Oteíza, Sebastián and Labbé, Mariela and Rojo, Francisco and Salazar, Fernando},\nTITLE = {UAV-Based Spectral and Thermal Indices in Precision Viticulture: A Review of NDVI, NDRE, SAVI, GNDVI, and CWSI},\nJOURNAL = {Agronomy},\nVOLUME = {15},\nYEAR = {2025},\nNUMBER = {11},\nARTICLE-NUMBER = {2569},\nURL = {https://www.mdpi.com/2073-4395/15/11/2569},\nISSN = {2073-4395},\nDOI = {10.3390/agronomy15112569}\n}" }, { "n": 10, "key": "IoTinPV", "title": "A technical survey on practical applications and guidelines for IoT sensors in precision agriculture and viticulture", "authors": [ "David Pascoal", "Nuno Silva", "Telmo Adão", "Rui Lopes", "Emanuel Peres", "Raul Morais" ], "authors_short": "Pascoal et al.", "year": 2024, "venue": "Scientific Reports", "doi": "10.1038/s41598-024-80924-y", "url": "https://doi.org/10.1038/s41598-024-80924-y", "division": "monitoring", "also": [], "type": "Review", "crop": "Grapevine", "platform": "Fixed sensors and IoT", "in_catalog": false, "note": "Technical survey and practical guidelines for IoT sensors in agriculture and viticulture.", "cited_in": [ "Technologies for Precision Viticulture" ], "bibtex": "@article{IoTinPV,\nauthor = {Pascoal, David and Silva, Nuno and Adão, Telmo and Lopes, Rui and Peres, Emanuel and Morais, Raul},\nyear = {2024},\nmonth = {11},\npages = {},\ntitle = {A technical survey on practical applications and guidelines for IoT sensors in precision agriculture and viticulture},\nvolume = {14},\njournal = {Scientific Reports},\ndoi = {10.1038/s41598-024-80924-y}\n}" }, { "n": 11, "key": "8276822", "title": "An IoT Monitoring System for Precision Viticulture", "authors": [ "Josman P. Pérez-Expósito", "Tiago M. Fernández-Caramés", "Paula Fraga-Lamas", "Luis Castedo" ], "authors_short": "Pérez-Expósito et al.", "year": 2017, "venue": "2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)", "doi": "10.1109/iThings-GreenCom-CPSCom-SmartData.2017.104", "url": "https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2017.104", "division": "monitoring", "also": [], "type": "System", "crop": "Grapevine", "platform": "Fixed sensors and IoT", "in_catalog": false, "note": "IoT monitoring system built for precision viticulture.", "cited_in": [ "Technologies for Precision Viticulture" ], "bibtex": "@INPROCEEDINGS{8276822,\n author={Pérez-Expósito, Josman P. and Fernández-Caramés, Tiago M. and Fraga-Lamas, Paula and Castedo, Luis},\n booktitle={2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)}, \n title={An IoT Monitoring System for Precision Viticulture}, \n year={2017},\n volume={},\n number={},\n pages={662-669},\n keywords={Diseases;Monitoring;Pipelines;Wireless sensor networks;Meteorology;Temperature measurement;Agriculture;wireless sensor networks;IoT;vineyards;smart viticulture;precision agriculture},\n doi={10.1109/iThings-GreenCom-CPSCom-SmartData.2017.104}}" }, { "n": 12, "key": "GPS_PA", "title": "Global Navigation Satellite Systems as State-of-the-Art Solutions in Precision Agriculture: A Review of Studies Indexed in the Web of Science", "authors": [ "Dorijan Radocaj", "Ivan Plaščak", "Mladen Jurišić" ], "authors_short": "Radocaj et al.", "year": 2023, "venue": "Agriculture", "doi": "10.3390/agriculture13071417", "url": "https://doi.org/10.3390/agriculture13071417", "division": "monitoring", "also": [ "uav" ], "type": "Review", "crop": "Agriculture in general", "platform": "Several platforms", "in_catalog": false, "note": "Review of GNSS for mapping, georeferencing and guiding precision agriculture equipment.", "cited_in": [ "Technologies for Precision Viticulture" ], "bibtex": "@article{GPS_PA,\nauthor = {Radocaj, Dorijan and Plaščak, Ivan and Jurišić, Mladen},\nyear = {2023},\nmonth = {07},\npages = {1417},\ntitle = {Global Navigation Satellite Systems as State-of-the-Art Solutions in Precision Agriculture: A Review of Studies Indexed in the Web of Science},\nvolume = {13},\njournal = {Agriculture},\ndoi = {10.3390/agriculture13071417}\n}" }, { "n": 13, "key": "su15043866", "title": "Assessing the Economic Performance of Multipurpose Collaborative Robots toward Skillful and Sustainable Viticultural Practices", "authors": [ "Emmanouil Tziolas", "Eleftherios Karapatzak", "Ioannis Kalathas", "Aikaterini Karampatea", "Antonios Grigoropoulos", "Aadil Bajoub", "Theodore Pachidis", "Vassilis G. Kaburlasos" ], "authors_short": "Tziolas et al.", "year": 2023, "venue": "Sustainability", "doi": "10.3390/su15043866", "url": "https://doi.org/10.3390/su15043866", "division": "deployment", "also": [ "control" ], "type": "Economics or adoption", "crop": "Grapevine", "platform": "Ground robot or vehicle", "in_catalog": false, "note": "Economic performance of multipurpose collaborative robots in viticulture. Cited on the economic return of robotics depending on the task.", "cited_in": [ "Technologies for Precision Viticulture" ], "bibtex": "@Article{su15043866,\nAUTHOR = {Tziolas, Emmanouil and Karapatzak, Eleftherios and Kalathas, Ioannis and Karampatea, Aikaterini and Grigoropoulos, Antonios and Bajoub, Aadil and Pachidis, Theodore and Kaburlasos, Vassilis G.},\nTITLE = {Assessing the Economic Performance of Multipurpose Collaborative Robots toward Skillful and Sustainable Viticultural Practices},\nJOURNAL = {Sustainability},\nVOLUME = {15},\nYEAR = {2023},\nNUMBER = {4},\nARTICLE-NUMBER = {3866},\nURL = {https://www.mdpi.com/2071-1050/15/4/3866},\nISSN = {2071-1050},\nDOI = {10.3390/su15043866}\n}" }, { "n": 14, "key": "AIreview:2025", "title": "Artificial Intelligence in Precision Agriculture: A Review", "authors": [ "Seri Mustaza", "Nurul Pauzi", "Nasharuddin Zainal", "Asraf M Moubark", "Mohd Hairi Mohd Zaman" ], "authors_short": "Mustaza et al.", "year": 2025, "venue": "Jurnal Kejuruteraan", "doi": "10.17576/jkukm-2025-37(3)-34", "url": "https://doi.org/10.17576/jkukm-2025-37(3)-34", "division": "perception", "also": [], "type": "Review", "crop": "Agriculture in general", "platform": "Not platform-specific", "in_catalog": false, "note": "Review of artificial intelligence in precision agriculture.", "cited_in": [ "Technologies for Precision Viticulture" ], "bibtex": "@article{AIreview:2025,\nauthor = {Mustaza, Seri and Pauzi, Nurul and Zainal, Nasharuddin and M Moubark, Asraf and Mohd Zaman, Mohd Hairi},\nyear = {2025},\nmonth = {05},\npages = {1515-1538},\ntitle = {Artificial Intelligence in Precision Agriculture: A Review},\nvolume = {37},\njournal = {Jurnal Kejuruteraan},\ndoi = {10.17576/jkukm-2025-37(3)-34}\n}" }, { "n": 15, "key": "Gupta2025ApplicationsOA", "title": "Applications of AI in precision agriculture", "authors": [ "Garima Gupta", "Sudhir Kumar Pal" ], "authors_short": "Gupta and Pal", "year": 2025, "venue": "Discover Agriculture", "doi": "", "url": "https://api.semanticscholar.org/CorpusID:278080574", "division": "perception", "also": [], "type": "Review", "crop": "Agriculture in general", "platform": "Not platform-specific", "in_catalog": false, "note": "Review of AI applications in precision agriculture.", "cited_in": [ "Technologies for Precision Viticulture" ], "bibtex": "@article{Gupta2025ApplicationsOA,\n title={Applications of AI in precision agriculture},\n author={Garima Gupta and Sudhir Kumar Pal},\n journal={Discover Agriculture},\n year={2025},\n volume={3},\n url={https://api.semanticscholar.org/CorpusID:278080574}\n}" }, { "n": 16, "key": "agronomy12102463", "title": "Computer Vision and Deep Learning for Precision Viticulture", "authors": [ "Lucas Mohimont", "François Alin", "Marine Rondeau", "Nathalie Gaveau", "Luiz Angelo Steffenel" ], "authors_short": "Mohimont et al.", "year": 2022, "venue": "Agronomy", "doi": "10.3390/agronomy12102463", "url": "https://doi.org/10.3390/agronomy12102463", "division": "perception", "also": [], "type": "Review", "crop": "Grapevine", "platform": "Several platforms", "in_catalog": false, "note": "Computer vision and deep learning for viticulture. Cited on model performance changing across cultivars, canopy structures, illumination and phenological stages.", "cited_in": [ "Technologies for Precision Viticulture", "Deployment considerations" ], "bibtex": "@Article{agronomy12102463,\nAUTHOR = {Mohimont, Lucas and Alin, François and Rondeau, Marine and Gaveau, Nathalie and Steffenel, Luiz Angelo},\nTITLE = {Computer Vision and Deep Learning for Precision Viticulture},\nJOURNAL = {Agronomy},\nVOLUME = {12},\nYEAR = {2022},\nNUMBER = {10},\nARTICLE-NUMBER = {2463},\nURL = {https://www.mdpi.com/2073-4395/12/10/2463},\nISSN = {2073-4395},\nDOI = {10.3390/agronomy12102463}\n}" }, { "n": 17, "key": "practical2025frugal", "title": "Practical Considerations for Deploying Frugal AI in the IoT-Edge-Cloud Continuum", "authors": [ "Katarzyna Wasielewska-Michniewska", "Przemysław Hołda", "Marcin Paprzycki", "Maria Ganzha", "Ignacio Lacalle", "Jorge Jiménez García" ], "authors_short": "Wasielewska-Michniewska et al.", "year": 2026, "venue": "Big Data Analytics in Astronomy, Science, and Engineering", "doi": "10.1007/978-3-032-23241-0_21", "url": "https://doi.org/10.1007/978-3-032-23241-0_21", "division": "deployment", "also": [ "perception" ], "type": "Guidelines", "crop": "Not crop-specific", "platform": "Not platform-specific", "in_catalog": false, "note": "Placing AI models along the IoT, edge and cloud continuum to match resource, latency, cost and quality constraints.", "cited_in": [ "Technologies for Precision Viticulture" ], "bibtex": "@InProceedings{practical2025frugal,\nauthor=\"Wasielewska-Michniewska, Katarzyna\nand Ho{\\l}da, Przemys{\\l}aw\nand Paprzycki, Marcin\nand Ganzha, Maria\nand Lacalle, Ignacio\nand Garc{\\'i}a, Jorge Jim{\\'e}nez\",\neditor=\"Sachdeva, Shelly\nand Watanobe, Yutaka\nand Bhalla, Subhash\",\ntitle=\"Practical Considerations for Deploying Frugal AI in the IoT-Edge-Cloud Continuum\",\nbooktitle=\"Big Data Analytics in Astronomy, Science, and Engineering\",\nyear=\"2026\",\npublisher=\"Springer Nature Switzerland\",\naddress=\"Cham\",\npages=\"343--358\",\ndoi=\"10.1007/978-3-032-23241-0_21\",\nabstract=\"The proliferation of IoT devices and IoT-Edge-Cloud infrastructures has created the need to deploy artificial intelligence applications on resource-constrained devices to achieve efficient decision-making closer to the edge. Frugal AI is an approach that focuses on optimizing machine learning and AI models to be resource-efficient, cost-effective, and scalable, particularly in environments with limited computational resources or energy constraints. Applying frugal techniques to AI models is challenging because of complex trade-off to consider and the lack of silver-bullet approaches. This contribution covers practical considerations when deploying frugal AI and discusses quality metrics that can be used to ensure its effective application. These are considered in the analysis of results of an experimental evaluation of compression techniques, applied to AI-based operation of, real-life anchored scenarios materializing in smart buildings. Results show how frugal methods balance model quality and size compression. Following discussion overviews opportunities for effective deployment on the edge by reducing model size and accelerating inference.\",\nisbn=\"978-3-032-23241-0\"\n}" }, { "n": 18, "key": "frugal2026case", "title": "A case study of artificial neural network compression methods for resource-constrained multi-label classification", "authors": [ "Przemysław Hołda", "Katarzyna Wasielewska-Michniewska", "Maria Ganzha", "Marcin Paprzycki" ], "authors_short": "Hołda et al.", "year": 2026, "venue": "Informatica", "doi": "", "url": "", "division": "perception", "also": [ "deployment" ], "type": "Method", "crop": "Not crop-specific", "platform": "Not platform-specific", "in_catalog": false, "note": "Case study of neural network compression for resource-constrained classification. The trade-off between size and quality varies by technique, model and runtime platform.", "cited_in": [ "Technologies for Precision Viticulture" ], "bibtex": "@article{frugal2026case,\n title={A case study of artificial neural network compression methods for resource-constrained multi-label classification},\n author={Ho{\\l}da, Przemys{\\l}aw and Wasielewska-Michniewska, Katarzyna and Ganzha, Maria and Paprzycki, Marcin},\n journal={Informatica},\n volume={37},\n number={1},\n pages={87--107},\n year={2026},\n publisher={Vilnius University Institute of Data Science and Digital Technologies}\n}" }, { "n": 19, "key": "Franczyk:2020", "title": "Deep learning for grape variety recognition", "authors": [ "Bogdan Franczyk", "Marcin Hernes", "Adrianna Kozierkiewicz-Hetmańska", "Agata Kozina", "Marcin Pietranik", "Ingolf Römer", "Martin Schieck" ], "authors_short": "Franczyk et al.", "year": 2020, "venue": "Procedia Computer Science", "doi": "10.1016/j.procs.2020.09.117", "url": "https://doi.org/10.1016/j.procs.2020.09.117", "division": "perception", "also": [], "type": "Method", "crop": "Grapevine", "platform": "Unspecified", "in_catalog": false, "note": "Deep learning for grape variety recognition. Cited on the need to validate models across cultivars.", "cited_in": [ "Technologies for Precision Viticulture" ], "bibtex": "@article{Franczyk:2020,\nauthor = {Franczyk, Bogdan and Hernes, Marcin and Kozierkiewicz-Hetmańska, Adrianna and Kozina, Agata and Pietranik, Marcin and Römer, Ingolf and Schieck, Martin},\nyear = {2020},\nmonth = {01},\npages = {1211-1220},\ntitle = {Deep learning for grape variety recognition},\nvolume = {176},\njournal = {Procedia Computer Science},\ndoi = {10.1016/j.procs.2020.09.117}\n}" }, { "n": 20, "key": "VELEZ2023109686", "title": "VineLiDAR: High-resolution UAV-LiDAR vineyard dataset acquired over two years in northern Spain", "authors": [ "Sergio Vélez", "Mar Ariza-Sentís", "João Valente" ], "authors_short": "Vélez et al.", "year": 2023, "venue": "Data in Brief", "doi": "10.1016/j.dib.2023.109686", "url": "https://doi.org/10.1016/j.dib.2023.109686", "division": "datasets", "also": [ "mapping" ], "type": "Dataset", "crop": "Grapevine", "platform": "UAV", "in_catalog": true, "note": "VineLiDAR. Ten UAV-LiDAR point clouds with embedded RGB over two vineyards in Tomiño, Spain, flown at 20, 30 and 50 m at three development stages.", "cited_in": [ "Technologies for Precision Viticulture", "Available datasets" ], "bibtex": "@article{VELEZ2023109686,\ntitle = {VineLiDAR: High-resolution UAV-LiDAR vineyard dataset acquired over two years in northern Spain},\njournal = {Data in Brief},\nvolume = {51},\npages = {109686},\nyear = {2023},\nissn = {2352-3409},\ndoi = {10.1016/j.dib.2023.109686},\nurl = {https://www.sciencedirect.com/science/article/pii/S2352340923007655},\nauthor = {Sergio Vélez and Mar Ariza-Sentís and João Valente},\nkeywords = {Viticulture, Precision agriculture, LiDAR, Drone, Path planning, Woody crops, Photogrammetry, Structure From Motion (SfM)}\n}" }, { "n": 21, "key": "VELEZ2024110497", "title": "EscaYard: Precision viticulture multimodal dataset of vineyards affected by Esca disease consisting of geotagged smartphone images, phytosanitary status, UAV 3D point clouds and Orthomosaics", "authors": [ "Sergio Vélez", "Mar Ariza-Sentís", "João Valente" ], "authors_short": "Vélez et al.", "year": 2024, "venue": "Data in Brief", "doi": "10.1016/j.dib.2024.110497", "url": "https://doi.org/10.1016/j.dib.2024.110497", "division": "datasets", "also": [ "monitoring" ], "type": "Dataset", "crop": "Grapevine", "platform": "Several platforms", "in_catalog": true, "note": "EscaYard. Geotagged smartphone images, phytosanitary status, UAV point clouds, orthomosaics and RTK GNSS from Esca-affected Loureiro vineyards in Tomiño, Spain.", "cited_in": [ "Technologies for Precision Viticulture", "Available datasets" ], "bibtex": "@article{VELEZ2024110497,\ntitle = {EscaYard: Precision viticulture multimodal dataset of vineyards affected by Esca disease consisting of geotagged smartphone images, phytosanitary status, UAV 3D point clouds and Orthomosaics},\nauthor = {Sergio Vélez and Mar Ariza-Sentís and João Valente},\njournal = {Data in Brief},\nvolume = {54},\npages = {110497},\nyear = {2024},\nissn = {2352-3409},\ndoi = {10.1016/j.dib.2024.110497}\n}" }, { "n": 22, "key": "iot1010002", "title": "Towards a Low-Cost Precision Viticulture System Using Internet of Things Devices", "authors": [ "Petros Spachos" ], "authors_short": "Spachos", "year": 2020, "venue": "IoT", "doi": "10.3390/iot1010002", "url": "https://doi.org/10.3390/iot1010002", "division": "monitoring", "also": [ "deployment" ], "type": "System", "crop": "Grapevine", "platform": "Fixed sensors and IoT", "in_catalog": false, "note": "Low-cost IoT system for precision viticulture. Cited as an example of a minimal-cost setup.", "cited_in": [ "Technologies for Precision Viticulture" ], "bibtex": "@Article{iot1010002,\nAUTHOR = {Spachos, Petros},\nTITLE = {Towards a Low-Cost Precision Viticulture System Using Internet of Things Devices},\nJOURNAL = {IoT},\nVOLUME = {1},\nYEAR = {2020},\nNUMBER = {1},\nPAGES = {5--20},\nURL = {https://www.mdpi.com/2624-831X/1/1/2},\nISSN = {2624-831X},\nABSTRACT = {Precision Agriculture (PA) is an ever-expanding field that takes modern technological advancements and applies it to farming practices to reduce waste and increase output. One advancement that can play a significant role in achieving precision agriculture is wireless technology, and specifically the Internet of Things (IoT) devices. Small, inch scale and low-cost devices can be used to monitor great agricultural areas. In this paper, a system for precision viticulture which uses IoT devices for real-time monitoring is proposed. The different components of the system are programmed properly and the interconnection between them is designed to minimize energy consumption. Wireless sensor nodes measure soil moisture and soil temperature in the field and transmit the information to a base station. If the conditions are optimal for a disease or pest to occur, a drone flies towards the area. When the drone is over the node, pictures are captured and then it returns to the base station for further processing. The feasibility of the system is examined through experimentation in a realistic scenario.},\nDOI = {10.3390/iot1010002}\n}" }, { "n": 23, "key": "s141223388", "title": "An Open-Source and Low-Cost Monitoring System for Precision Enology", "authors": [ "Salvatore Filippo Di Gennaro", "Alessandro Matese", "Mirko Mancin", "Jacopo Primicerio", "Alberto Palliotti" ], "authors_short": "Di Gennaro et al.", "year": 2014, "venue": "Sensors", "doi": "10.3390/s141223388", "url": "https://doi.org/10.3390/s141223388", "division": "monitoring", "also": [ "deployment" ], "type": "System", "crop": "Grapevine", "platform": "Fixed sensors and IoT", "in_catalog": false, "note": "Open-source, low-cost monitoring system for precision enology. Cited as an example of a minimal-cost setup.", "cited_in": [ "Technologies for Precision Viticulture" ], "bibtex": "@Article{s141223388,\nAUTHOR = {Di Gennaro, Salvatore Filippo and Matese, Alessandro and Mancin, Mirko and Primicerio, Jacopo and Palliotti, Alberto},\nTITLE = {An Open-Source and Low-Cost Monitoring System for Precision Enology},\nJOURNAL = {Sensors},\nVOLUME = {14},\nYEAR = {2014},\nNUMBER = {12},\nPAGES = {23388--23397},\nURL = {https://www.mdpi.com/1424-8220/14/12/23388},\nPubMedID = {25490596},\nISSN = {1424-8220},\nABSTRACT = {Winemaking is a dynamic process, where microbiological and chemical effects may strongly differentiate products from the same vineyard and even between wine vats. This high variability means an increase in work in terms of control and process management. The winemaking process therefore requires a site-specific approach in order to optimize cellar practices and quality management, suggesting a new concept of winemaking, identified as Precision Enology. The Institute of Biometeorology of the Italian National Research Council has developed a wireless monitoring system, consisting of a series of nodes integrated in barrel bungs with sensors for the measurement of wine physical and chemical parameters in the barrel. This paper describes an open-source evolution of the preliminary prototype, using Arduino-based technology. Results have shown good performance in terms of data transmission and accuracy, minimal size and power consumption. The system has been designed to create a low-cost product, which allows a remote and real-time control of wine evolution in each barrel, minimizing costs and time for sampling and laboratory analysis. The possibility of integrating any kind of sensors makes the system a flexible tool that can satisfy various monitoring needs.},\nDOI = {10.3390/s141223388}\n}" }, { "n": 24, "key": "fi18010016", "title": "A Versatile and Low-Cost IoT Solution for Bioclimatic Monitoring in Precision Viticulture", "authors": [ "António Vieira", "Nuno Silva", "David Pascoal", "Raul Morais" ], "authors_short": "Vieira et al.", "year": 2026, "venue": "Future Internet", "doi": "10.3390/fi18010016", "url": "https://doi.org/10.3390/fi18010016", "division": "monitoring", "also": [ "deployment" ], "type": "System", "crop": "Grapevine", "platform": "Fixed sensors and IoT", "in_catalog": false, "note": "Low-cost IoT solution for bioclimatic monitoring in vineyards. Cited as an example of a minimal-cost setup.", "cited_in": [ "Technologies for Precision Viticulture" ], "bibtex": "@Article{fi18010016,\nAUTHOR = {Vieira, António and Silva, Nuno and Pascoal, David and Morais, Raul},\nTITLE = {A Versatile and Low-Cost IoT Solution for Bioclimatic Monitoring in Precision Viticulture},\nJOURNAL = {Future Internet},\nVOLUME = {18},\nYEAR = {2026},\nNUMBER = {1},\nARTICLE-NUMBER = {16},\nURL = {https://www.mdpi.com/1999-5903/18/1/16},\nISSN = {1999-5903},\nABSTRACT = {Bioclimatic monitoring at vineyard scale is essential for irrigation management and disease-risk assessment, yet many systems rely on expensive commercial stations or generic IoT nodes with limited validation and little focus on small and medium-sized winegrowers. This application-driven engineering work investigates whether decision-support-grade bioclimatic data for precision viticulture can be obtained from a low-cost station, by proposing a solar-powered proximal node that integrates soil, plant, and atmospheric sensors on a dedicated PCB that communicates via LoRaWAN. The node operates in a 15-min cycle, with sensing parameters selected to provide the minimum information required for key Precision Viticulture applications. It was deployed in a commercial vineyard side by side with a commercial station, quantifying sensor agreement, communication reliability, and energy consumption. The results show low error rates and consistent agronomic interpretation of environmental conditions, disease risk, precipitation events, and soil and water dynamics. The LoRaWAN link reached a 97% packet-delivery ratio with an average consumption of about 2.5 Wh per day. Material cost is approximately 260 €, one order of magnitude lower than a comparable station. These results indicate that, under real vineyard conditions and compared with a commercial reference, the proposed low-cost system provides agronomically useful, reliable bioclimatic monitoring.},\nDOI = {10.3390/fi18010016}\n}" }, { "n": 25, "key": "iot1020020", "title": "A Proposed Low-Cost Viticulture Stress Framework for Table Grape Varieties", "authors": [ "Sotirios Kontogiannis", "Christodoulos Asiminidis" ], "authors_short": "Kontogiannis and Asiminidis", "year": 2020, "venue": "IoT", "doi": "10.3390/iot1020020", "url": "https://doi.org/10.3390/iot1020020", "division": "monitoring", "also": [ "deployment" ], "type": "System", "crop": "Grapevine", "platform": "Fixed sensors and IoT", "in_catalog": false, "note": "Low-cost stress-monitoring framework for table grape varieties. Cited as an example of a minimal-cost setup.", "cited_in": [ "Technologies for Precision Viticulture" ], "bibtex": "@Article{iot1020020,\nAUTHOR = {Kontogiannis, Sotirios and Asiminidis, Christodoulos},\nTITLE = {A Proposed Low-Cost Viticulture Stress Framework for Table Grape Varieties},\nJOURNAL = {IoT},\nVOLUME = {1},\nYEAR = {2020},\nNUMBER = {2},\nPAGES = {337--359},\nURL = {https://www.mdpi.com/2624-831X/1/2/20},\nISSN = {2624-831X},\nABSTRACT = {Climate change significantly affects viticulture by reducing the production yield and the quality characteristics of its final products. In some observed cases, the consequences of climate outages such as droughts, hail and floods are absolutely devastating for the farmers and the sustained local economies. Hence, it is essential to develop new in implementation monitoring solutions that offer remote real-time surveillance, alert triggering, minimum maintenance and automated generation of incident alerts with precision responses. This paper presents a new framework and a system for vine stress monitoring called Vity-stress. The Vity-stress framework combines field measurements with precise viticulture suggestions and stress avoidance planning. The key points of the proposed framework’s system are that it is easy to develop, easy to maintain and cheap to implement applicability. Focusing on the Mediterranean cultivated table grape varieties that are strongly affected by climate change, we propose a new stress conditions monitoring system to support our framework. The proposition includes distributed field located sensors and a novel camera module implementing deep neural network algorithms to detect stress indicators. Additionally, a new wireless sensor network supported by the iBeacon protocol has been developed. The results of the sensory measurements’ data logging and imposed image detection process’s evaluation shows that the proposed system can successfully detect different stress levels in vineyards, which in turn can allow producers to identify specific areas for irrigation, thereby saving water, energy and time.},\nDOI = {10.3390/iot1020020}\n}" }, { "n": 26, "key": "10.1007/978-3-030-30241-2_20", "title": "Low-Cost IoT LoRa®Solutions for Precision Agriculture Monitoring Practices", "authors": [ "Nuno Silva", "Jorge Mendes", "Renato Silva", "Filipe Neves dos Santos", "Pedro Mestre", "Carlos Serôdio", "Raul Morais" ], "authors_short": "Silva et al.", "year": 2019, "venue": "Progress in Artificial Intelligence", "doi": "10.1007/978-3-030-30241-2_20", "url": "https://doi.org/10.1007/978-3-030-30241-2_20", "division": "monitoring", "also": [ "deployment" ], "type": "System", "crop": "Agriculture in general", "platform": "Fixed sensors and IoT", "in_catalog": false, "note": "Low-cost LoRa IoT nodes for agricultural monitoring. Cited as an example of a minimal-cost setup.", "cited_in": [ "Technologies for Precision Viticulture" ], "bibtex": "@InProceedings{10.1007/978-3-030-30241-2_20,\nauthor=\"Silva, Nuno\nand Mendes, Jorge\nand Silva, Renato\nand dos Santos, Filipe Neves\nand Mestre, Pedro\nand Ser{\\^o}dio, Carlos\nand Morais, Raul\",\neditor=\"Moura Oliveira, Paulo\nand Novais, Paulo\nand Reis, Lu{\\'i}s Paulo \",\ntitle=\"Low-Cost IoT LoRa®Solutions for Precision Agriculture Monitoring Practices\",\nbooktitle=\"Progress in Artificial Intelligence\",\nyear=\"2019\",\npublisher=\"Springer International Publishing\",\naddress=\"Cham\",\npages=\"224--235\",\nabstract=\"Emergent and established paradigms, such as the Internet of Things (IoT), cloud and fog/edge computing, together with increasingly cheaper computing technologies -- with very low power requirements, available to exchange data with increased efficiency -- and intelligent systems, have evolved to a level where it is virtually possible to create and deploy monitoring solutions, even in Precision Agriculture (PA) practices. In this work, LoRa®(Long Range) technology and LoRaWAN{\\texttrademark}protocol, are tested in a Precision Viticulture (PV) scenario, using low-power data acquisition devices deployed in a vineyard in the UTAD University Campus, distanced 400 m away from the nearest gateway. The main goal of this work is to evaluate sensor data integration in the mySense environment, a framework aimed to systematize data acquisition procedures to address common PA/PV issues, using LoRa®technology. mySense builds over a 4-layer technological structure: sensor and sensor nodes, crop field and sensor networks, cloud services and front-end applications. It makes available a set of free tools based on the Do-It-Yourself (DIY) concept and enables the use of low-cost platforms to quickly prototype a complete PA/PV monitoring application.\",\nisbn=\"978-3-030-30241-2\",\ndoi=\"10.1007/978-3-030-30241-2_20\"\n}" }, { "n": 27, "key": "rs16040635", "title": "Effectiveness of Management Zones Delineated from UAV and Sentinel-2 Data for Precision Viticulture Applications", "authors": [ "Bianca Ortuani", "Alice Mayer", "Davide Bianchi", "Giovanna Sona", "Alberto Crema", "Davide Modina", "Martino Bolognini", "Lucio Brancadoro", "Mirco Boschetti", "Arianna Facchi" ], "authors_short": "Ortuani et al.", "year": 2024, "venue": "Remote Sensing", "doi": "10.3390/rs16040635", "url": "https://doi.org/10.3390/rs16040635", "division": "monitoring", "also": [ "uav" ], "type": "Method", "crop": "Grapevine", "platform": "Several platforms", "in_catalog": false, "note": "Compares management zones delineated from UAV and Sentinel-2 data for precision viticulture.", "cited_in": [ "Technologies for Precision Viticulture" ], "bibtex": "@Article{rs16040635,\nAUTHOR = {Ortuani, Bianca and Mayer, Alice and Bianchi, Davide and Sona, Giovanna and Crema, Alberto and Modina, Davide and Bolognini, Martino and Brancadoro, Lucio and Boschetti, Mirco and Facchi, Arianna},\nTITLE = {Effectiveness of Management Zones Delineated from UAV and Sentinel-2 Data for Precision Viticulture Applications},\nJOURNAL = {Remote Sensing},\nVOLUME = {16},\nYEAR = {2024},\nNUMBER = {4},\nARTICLE-NUMBER = {635},\nURL = {https://www.mdpi.com/2072-4292/16/4/635},\nISSN = {2072-4292},\nABSTRACT = {How accurately do Sentinel-2 (S2) images describe vine row spatial variability? Can they produce effective management zones (MZs) for precision viticulture? S2 and UAV datasets acquired over two years for different drip-irrigated vineyards in the Colli Morenici region (northern Italy) were used to assess the actual need to use UAV-NDVI maps instead of S2 images to obtain effective MZ maps. First, the correlation between S2 and UAV-NDVI values was investigated. Secondly, contingency matrices and dichotomous tables (considering UAV-MZ maps as a reference) were developed to compare MZ maps produced using S2 and UAV imagery. Moreover, data on grape production and quality were analyzed through linear discrimination analyses (LDA) to evaluate the effectiveness of S2-MZs and UAV-MZs to explain spatial variability in yield and quality data. The outcomes highlight that S2 images can be quite good tools to manage fertilization based on the within-field vigor variability, of which they capture the main features. Nevertheless, as S2-MZs with low and high vigor were over-estimated, S2-MZ maps cannot be used for high-accuracy input management. From the LDA results, the UAV-MZs appeared slightly more performant than the S2-MZs in explaining the variability in grape quality and yield, especially in the case of low-vigor MZs.},\nDOI = {10.3390/rs16040635}\n}" }, { "n": 28, "key": "TARDAGUILA2021100005", "title": "Smart applications and digital technologies in viticulture: A review", "authors": [ "Javier Tardaguila", "Manfred Stoll", "Salvador Gutiérrez", "Tony Proffitt", "Maria P. Diago" ], "authors_short": "Tardaguila et al.", "year": 2021, "venue": "Smart Agricultural Technology", "doi": "10.1016/j.atech.2021.100005", "url": "https://doi.org/10.1016/j.atech.2021.100005", "division": "monitoring", "also": [], "type": "Review", "crop": "Grapevine", "platform": "Several platforms", "in_catalog": false, "note": "Review of smart applications and digital technologies in viticulture. Cited for fully integrated high-end platforms.", "cited_in": [ "Technologies for Precision Viticulture" ], "bibtex": "@article{TARDAGUILA2021100005,\ntitle = {Smart applications and digital technologies in viticulture: A review},\njournal = {Smart Agricultural Technology},\nvolume = {1},\npages = {100005},\nyear = {2021},\nissn = {2772-3755},\ndoi = {10.1016/j.atech.2021.100005},\nurl = {https://www.sciencedirect.com/science/article/pii/S2772375521000058},\nauthor = {Javier Tardaguila and Manfred Stoll and Salvador Gutiérrez and Tony Proffitt and Maria P. Diago},\nkeywords = {Digital viticulture, Precision viticulture, Vineyard monitoring, Remote and proximal sensing, Vineyard app, Vineyard robotics, Artificial intelligence},\nabstract = {It is important to continuously monitor the long-term impact of viticultural management practices and assess opportunities for improving the environmental footprint of vineyard operations. This is particularly relevant to the wine industry as growers face disruptive challenges caused by climate change, shortages of labour and escalating production costs. In recent years there has been considerable development and testing of non-invasive digital technologies, some of which have already demonstrated an improvement in the way that wine grapes are grown, managed and harvested to produce quality wines in a manner that is both environmentally and economically sustainable. In this paper, we describe a number of sensing technologies including spectroscopy, multispectral and hyperspectral imaging, chlorophyll fluorescence, thermography, electrical resistivity, laser imaging detection and ranging, and computer vision and the platforms where they are generally mounted or embedded for either proximal or remote monitoring. Artificial intelligence is also discussed as it is useful as a means of transforming data into different pieces of information used by the grape grower for making informed decisions. A key objective of using these technologies is to obtain and supply data and information to grape growers and wine producers as a basis for improving land and vine management through a more-informed decision-making process. The current and future application of these technologies and artificial intelligence in vineyards are discussed in relation to soil properties and topography, vegetative growth, canopy architecture, nutrient and water status, pests and diseases, crop forecasting, yield and fruit composition, vineyard sampling, targeted management and selective harvesting. The principles behind how these technologies operate are also described. While the technologies have enormous potential for growers, their adoption and use will depend on user-friendly software and devices, together with affordable costs, at the field scale.}\n}" }, { "n": 29, "key": "agriculture11030201", "title": "State of the Art of Monitoring Technologies and Data Processing for Precision Viticulture", "authors": [ "Marco Ammoniaci", "Simon-Paolo Kartsiotis", "Rita Perria", "Paolo Storchi" ], "authors_short": "Ammoniaci et al.", "year": 2021, "venue": "Agriculture", "doi": "10.3390/agriculture11030201", "url": "https://doi.org/10.3390/agriculture11030201", "division": "monitoring", "also": [ "uav" ], "type": "Review", "crop": "Grapevine", "platform": "Several platforms", "in_catalog": false, "note": "State of the art of monitoring technologies and data processing in viticulture. Cited on flight planning and integrated IoT platforms.", "cited_in": [ "Technologies for Precision Viticulture", "Deployment considerations" ], "bibtex": "@Article{agriculture11030201,\nAUTHOR = {Ammoniaci, Marco and Kartsiotis, Simon-Paolo and Perria, Rita and Storchi, Paolo},\nTITLE = {State of the Art of Monitoring Technologies and Data Processing for Precision Viticulture},\nJOURNAL = {Agriculture},\nVOLUME = {11},\nYEAR = {2021},\nNUMBER = {3},\nARTICLE-NUMBER = {201},\nURL = {https://www.mdpi.com/2077-0472/11/3/201},\nISSN = {2077-0472},\nABSTRACT = {Precision viticulture (PV) aims to optimize vineyard management, reducing the use of resources, the environmental impact and maximizing the yield and quality of the production. New technologies as UAVs, satellites, proximal sensors and variable rate machines (VRT) are being developed and used more and more frequently in recent years thanks also to informatics systems able to read, analyze and process a huge number of data in order to give the winegrowers a decision support system (DSS) for making better decisions at the right place and time. This review presents a brief state of the art of precision viticulture technologies, focusing on monitoring tools, i.e., remote/proximal sensing, variable rate machines, robotics, DSS and the wireless sensor network.},\nDOI = {10.3390/agriculture11030201}\n}" }, { "n": 30, "key": "Gano:2024", "title": "Drone‐based imaging sensors, techniques, and applications in plant phenotyping for crop breeding: A comprehensive review", "authors": [ "Boubacar Gano", "Sourav Bhadra", "Justin Vilbig", "Nurzaman Ahmed", "Vasit Sagan", "Nadia Shakoor" ], "authors_short": "Gano et al.", "year": 2024, "venue": "The Plant Phenome Journal", "doi": "10.1002/ppj2.20100", "url": "https://doi.org/10.1002/ppj2.20100", "division": "uav", "also": [ "monitoring" ], "type": "Review", "crop": "Agriculture in general", "platform": "UAV", "in_catalog": false, "note": "Review of drone imaging for high-throughput phenotyping in crop breeding: platforms, sensors, acquisition, processing and machine learning for trait extraction.", "cited_in": [ "Related work" ], "bibtex": "@article{Gano:2024,\nauthor = {Gano, Boubacar and Bhadra, Sourav and Vilbig, Justin and Ahmed, Nurzaman and Sagan, Vasit and Shakoor, Nadia},\nyear = {2024},\nmonth = {04},\npages = {},\ntitle = {Drone‐based imaging sensors, techniques, and applications in plant phenotyping for crop breeding: A comprehensive review},\nvolume = {7},\njournal = {The Plant Phenome Journal},\ndoi = {10.1002/ppj2.20100}\n}" }, { "n": 31, "key": "Orlandi:2025", "title": "Automated yield prediction in vineyard using RGB images acquired by a UAV prototype platform", "authors": [ "Giorgia Orlandi", "Alessandro Matese", "Alessandro Ulrici", "Rosalba Calvini", "Andrea Berton", "Salvatore Di Gennaro" ], "authors_short": "Orlandi et al.", "year": 2025, "venue": "OENO One", "doi": "10.20870/oeno-one.2025.59.1.8133", "url": "https://doi.org/10.20870/oeno-one.2025.59.1.8133", "division": "perception", "also": [ "uav" ], "type": "Method", "crop": "Grapevine", "platform": "UAV", "in_catalog": false, "note": "Automated yield quantification from UAV images with colour tags of known size, on different slopes. Tested on a second season in a local Sangiovese setting.", "cited_in": [ "Related work" ], "bibtex": "@article{Orlandi:2025,\nauthor = {Orlandi, Giorgia and Matese, Alessandro and Ulrici, Alessandro and Calvini, Rosalba and Berton, Andrea and Di Gennaro, Salvatore},\nyear = {2025},\nmonth = {03},\npages = {},\ntitle = {Automated yield prediction in vineyard using RGB images acquired by a UAV prototype platform},\nvolume = {59},\njournal = {OENO One},\ndoi = {10.20870/oeno-one.2025.59.1.8133}\n}" }, { "n": 32, "key": "GrapeDetection:2025", "title": "Framework for Smartphone-based Grape Detection and Vineyard Management using UAV-Trained AI", "authors": [ "Sergio Vélez Martín", "Mar Ariza", "Mario Triviño", "Carlos Cob", "Miquel Mila", "João Valente" ], "authors_short": "Vélez Martín et al.", "year": 2025, "venue": "Heliyon", "doi": "10.1016/j.heliyon.2025.e42525", "url": "https://doi.org/10.1016/j.heliyon.2025.e42525", "division": "perception", "also": [], "type": "Method", "crop": "Grapevine", "platform": "Several platforms", "in_catalog": false, "note": "Models trained on UAV images detect grape bunches in farmers' smartphone photos in Spanish vineyards. Needs initial UAV data and labour-intensive smartphone collection.", "cited_in": [ "Related work" ], "bibtex": "@article{GrapeDetection:2025,\nauthor = {Vélez Martín, Sergio and Ariza, Mar and Triviño, Mario and Cob, Carlos and Mila, Miquel and Valente, João},\nyear = {2025},\nmonth = {02},\npages = {e42525},\ntitle = {Framework for Smartphone-based Grape Detection and Vineyard Management using UAV-Trained AI},\nvolume = {11},\njournal = {Heliyon},\ndoi = {10.1016/j.heliyon.2025.e42525}\n}" }, { "n": 33, "key": "Epifany:2024", "title": "A Survey on Deep Learning in UAV imagery for Precision Agriculture and Wild Flora Monitoring: Datasets, Models and Challenges", "authors": [ "Lorenzo Epifani", "Antonio Caruso" ], "authors_short": "Epifani and Caruso", "year": 2024, "venue": "Smart Agricultural Technology", "doi": "10.1016/j.atech.2024.100625", "url": "https://doi.org/10.1016/j.atech.2024.100625", "division": "perception", "also": [ "uav" ], "type": "Review", "crop": "Agriculture in general", "platform": "UAV", "in_catalog": false, "note": "Survey of deep learning on UAV imagery for agriculture and wild flora: architectures, datasets, practical solutions and model sizing.", "cited_in": [ "Related work" ], "bibtex": "@article{Epifany:2024,\nauthor = {Epifani, Lorenzo and Caruso, Antonio},\nyear = {2024},\nmonth = {10},\npages = {100625},\ntitle = {A Survey on Deep Learning in UAV imagery for Precision Agriculture and Wild Flora Monitoring: Datasets, Models and Challenges},\nvolume = {9},\njournal = {Smart Agricultural Technology},\ndoi = {10.1016/j.atech.2024.100625}\n}" }, { "n": 34, "key": "jimaging11020034", "title": "GCNet: A Deep Learning Framework for Enhanced Grape Cluster Segmentation and Yield Estimation Incorporating Occluded Grape Detection with a Correction Factor for Indoor Experimentation", "authors": [ "Rubi Quiñones", "Syeda Mariah Banu", "Eren Gultepe" ], "authors_short": "Quiñones et al.", "year": 2025, "venue": "Journal of Imaging", "doi": "10.3390/jimaging11020034", "url": "https://doi.org/10.3390/jimaging11020034", "division": "perception", "also": [ "datasets" ], "type": "Method", "crop": "Grapevine", "platform": "Laboratory", "in_catalog": true, "note": "GCNet combines U-Net segmentation with an occlusion correction factor for yield estimation and introduces the indoor GrapeSet dataset. Transfer to outdoor vineyards is limited.", "cited_in": [ "Related work", "Available datasets" ], "bibtex": "@Article{jimaging11020034,\nAUTHOR = {Quiñones, Rubi and Banu, Syeda Mariah and Gultepe, Eren},\nTITLE = {GCNet: A Deep Learning Framework for Enhanced Grape Cluster Segmentation and Yield Estimation Incorporating Occluded Grape Detection with a Correction Factor for Indoor Experimentation},\nJOURNAL = {Journal of Imaging},\nVOLUME = {11},\nYEAR = {2025},\nNUMBER = {2},\nARTICLE-NUMBER = {34},\nURL = {https://www.mdpi.com/2313-433X/11/2/34},\nPubMedID = {39997536},\nISSN = {2313-433X},\nDOI = {10.3390/jimaging11020034}\n}" }, { "n": 35, "key": "ronneberger2015u", "title": "U-Net: Convolutional Networks for Biomedical Image Segmentation", "authors": [ "Olaf Ronneberger", "Philipp Fischer", "Thomas Brox" ], "authors_short": "Ronneberger et al.", "year": 2015, "venue": "Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015", "doi": "", "url": "", "division": "methods", "also": [], "type": "Method", "crop": "Not crop-specific", "platform": "Not platform-specific", "in_catalog": false, "note": "U-Net segmentation network, cited as a component of GCNet.", "cited_in": [ "Related work" ], "bibtex": "@InProceedings{ronneberger2015u,\nauthor=\"Ronneberger, Olaf\nand Fischer, Philipp\nand Brox, Thomas\",\neditor=\"Navab, Nassir\nand Hornegger, Joachim\nand Wells, William M.\nand Frangi, Alejandro F.\",\ntitle=\"U-Net: Convolutional Networks for Biomedical Image Segmentation\",\nbooktitle=\"Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015\",\nyear=\"2015\",\npublisher=\"Springer International Publishing\",\naddress=\"Cham\",\npages=\"234--241\",\nabstract=\"There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net.\",\nisbn=\"978-3-319-24574-4\"\n}" }, { "n": 36, "key": "wang_incoherent_2025", "title": "Incoherent Region-Aware Occlusion Instance Synthesis for Grape Amodal Detection", "authors": [ "Yihan Wang", "Shide Xiao", "Xiangyin Meng" ], "authors_short": "Wang et al.", "year": 2025, "venue": "Sensors", "doi": "10.3390/s25051546", "url": "https://doi.org/10.3390/s25051546", "division": "perception", "also": [], "type": "Method", "crop": "Grapevine", "platform": "Handheld or ground camera", "in_catalog": false, "note": "Synthetic occlusion for amodal grape detection on proximal ground imagery. Links occlusion handling to better detection.", "cited_in": [ "Related work" ], "bibtex": "@article{wang_incoherent_2025,\n\ttitle = {Incoherent {Region}-{Aware} {Occlusion} {Instance} {Synthesis} for {Grape} {Amodal} {Detection}},\n\tvolume = {25},\n\tissn = {1424-8220},\n\turl = {https://www.mdpi.com/1424-8220/25/5/1546},\n\tdoi = {10.3390/s25051546},\n\tabstract = {Occlusion presents a significant challenge in grape phenotyping detection, where predicting occluded content (amodal detection) can greatly enhance detection accuracy. Recognizing that amodal detection performance is heavily influenced by the segmentation quality between occluder and occluded grape instances, we propose a grape instance segmentation model designed to precisely predict error-prone regions caused by mask size transformations during segmentation, with a particular focus on overlapping regions. To address the limitations of current occlusion synthesis methods in amodal detection, a novel overlapping cover strategy is introduced to replace the existing random cover strategy. This approach ensures that synthetic grape instances better align with real-world occlusion scenarios. Quantitative comparison experiments conducted on the grape amodal detection dataset demonstrate that the proposed grape instance segmentation model achieves superior amodal detection performance, with an IoU score of 0.7931. Additionally, the proposed overlapping cover strategy significantly outperforms the random cover strategy in amodal detection performance.},\n\tlanguage = {en},\n\tnumber = {5},\n\turldate = {2026-02-25},\n\tjournal = {Sensors},\n\tauthor = {Wang, Yihan and Xiao, Shide and Meng, Xiangyin},\n\tmonth = {03},\n\tyear = {2025},\n\tpages = {1546},\n}" }, { "n": 37, "key": "li_amodal_2016", "title": "Amodal Instance Segmentation", "authors": [ "Ke Li", "Jitendra Malik" ], "authors_short": "Li and Malik", "year": 2016, "venue": "arXiv", "doi": "10.48550/arXiv.1604.08202", "url": "https://doi.org/10.48550/arXiv.1604.08202", "division": "methods", "also": [], "type": "Method", "crop": "Not crop-specific", "platform": "Not platform-specific", "in_catalog": false, "note": "Amodal instance segmentation: predicting the full extent of partly hidden objects.", "cited_in": [ "Related work" ], "bibtex": "@misc{li_amodal_2016,\n\ttitle = {Amodal {Instance} {Segmentation}},\n\turl = {http://arxiv.org/abs/1604.08202},\n\tdoi = {10.48550/arXiv.1604.08202},\n\tabstract = {We consider the problem of amodal instance segmentation, the objective of which is to predict the region encompassing both visible and occluded parts of each object. Thus far, the lack of publicly available amodal segmentation annotations has stymied the development of amodal segmentation methods. In this paper, we sidestep this issue by relying solely on standard modal instance segmentation annotations to train our model. The result is a new method for amodal instance segmentation, which represents the first such method to the best of our knowledge. We demonstrate the proposed method’s effectiveness both qualitatively and quantitatively.},\n\tlanguage = {en},\n\turldate = {2026-02-25},\n\tpublisher = {arXiv},\n\tauthor = {Li, Ke and Malik, Jitendra},\n\tmonth = {08},\n\tyear = {2016},\n\tnote = {arXiv:1604.08202 [cs]},\n\tkeywords = {Computer Science - Computer Vision and Pattern Recognition},\n}" }, { "n": 38, "key": "wang_occlusion-aware_2026", "title": "Occlusion-Aware Multi-Object Tracking in Vineyards via SAM-Based Visibility Modeling", "authors": [ "Yanan Wang", "Hagsong Kim", "Muhammad Fayaz", "Lien Minh Dang", "Hyeonjoon Moon", "Kang-Won Lee" ], "authors_short": "Wang et al.", "year": 2026, "venue": "Electronics", "doi": "10.3390/electronics15030621", "url": "https://doi.org/10.3390/electronics15030621", "division": "perception", "also": [ "datasets" ], "type": "Method", "crop": "Grapevine", "platform": "Unspecified", "in_catalog": false, "note": "OATSAM-Track: occlusion-aware tracking of grape clusters with YOLO, StrongSORT and MobileSAM. Introduces the GrapeOcclusionMOTS dataset.", "cited_in": [ "Related work" ], "bibtex": "@article{wang_occlusion-aware_2026,\n\ttitle = {Occlusion-{Aware} {Multi}-{Object} {Tracking} in {Vineyards} via {SAM}-{Based} {Visibility} {Modeling}},\n\tvolume = {15},\n\tissn = {2079-9292},\n\turl = {https://www.mdpi.com/2079-9292/15/3/621},\n\tdoi = {10.3390/electronics15030621},\n\tabstract = {Multi-object tracking (MOT) in vineyard environments remains challenging due to frequent and long-term occlusions caused by dense foliage, overlapping grape clusters, and complex plant structures. These characteristics often result in identity switches and fragmented trajectories when using conventional tracking methods. This paper proposes OATSAMTrack, an occlusion-aware multi-object tracking framework designed for vineyard fruit monitoring. The framework integrates lightweight MobileSAM-assisted instance segmentation to estimate target visibility and occlusion severity. Occlusion-state reasoning is further incorporated into temporal association, appearance memory updating, and identity recovery. An adaptive temporal memory mechanism selectively updates appearance features according to predicted occlusion states, reducing identity drift under partial and severe occlusions. To facilitate occlusion-aware evaluation, an extended vineyard multiobject tracking dataset (GrapeOcclusionMOTS) with SAM-refined instance masks and fine-grained occlusion annotations is constructed. The experimental results demonstrate that OATSAM-Track improves identity consistency and tracking robustness compared to representative baseline trackers, particularly under medium and severe occlusion scenarios. These results indicate that explicit occlusion modeling is beneficial for reliable fruit monitoring in precision agriculture.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2026-02-25},\n\tjournal = {Electronics},\n\tauthor = {Wang, Yanan and Kim, Hagsong and Fayaz, Muhammad and Dang, Lien Minh and Moon, Hyeonjoon and Lee, Kang-Won},\n\tmonth = {02},\n\tyear = {2026},\n\tpages = {621},\n}" }, { "n": 39, "key": "redmon2016you", "title": "You only look once: Unified, real-time object detection", "authors": [ "Joseph Redmon", "Santosh Divvala", "Ross Girshick", "Ali Farhadi" ], "authors_short": "Redmon et al.", "year": 2016, "venue": "Proceedings of the IEEE conference on computer vision and pattern recognition", "doi": "", "url": "", "division": "methods", "also": [], "type": "Method", "crop": "Not crop-specific", "platform": "Not platform-specific", "in_catalog": false, "note": "YOLO real-time object detector.", "cited_in": [ "Related work" ], "bibtex": "@inproceedings{redmon2016you,\n title={You only look once: Unified, real-time object detection},\n author={Redmon, Joseph and Divvala, Santosh and Girshick, Ross and Farhadi, Ali},\n booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},\n pages={779--788},\n year={2016}\n}" }, { "n": 40, "key": "du2023strongsort", "title": "Strongsort: Make deepsort great again", "authors": [ "Yunhao Du", "Zhicheng Zhao", "Yang Song", "Yanyun Zhao", "Fei Su", "Tao Gong", "Hongying Meng" ], "authors_short": "Du et al.", "year": 2023, "venue": "IEEE Transactions on Multimedia", "doi": "10.1109/TMM.2023.3240881", "url": "https://doi.org/10.1109/TMM.2023.3240881", "division": "methods", "also": [], "type": "Method", "crop": "Not crop-specific", "platform": "Not platform-specific", "in_catalog": false, "note": "StrongSORT multi-object tracker.", "cited_in": [ "Related work" ], "bibtex": "@article{du2023strongsort,\n title={Strongsort: Make deepsort great again},\n author={Du, Yunhao and Zhao, Zhicheng and Song, Yang and Zhao, Yanyun and Su, Fei and Gong, Tao and Meng, Hongying},\n journal={IEEE Transactions on Multimedia},\n volume={25},\n pages={8725--8737},\n year={2023},\n publisher={IEEE},\n keywords={Tracking;Task analysis;Computational modeling;Trajectory;Interpolation;Training;Kalman filters;Multi-object tracking;baseline;AFLink;GSI},\n doi={10.1109/TMM.2023.3240881}}" }, { "n": 41, "key": "zhang2023sam", "title": "Faster Segment Anything: Towards Lightweight SAM for Mobile Applications", "authors": [ "Chaoning Zhang", "Dongshen Han", "Yu Qiao", "Jung Uk Kim", "Sung-Ho Bae", "Seungkyu Lee", "Choong Seon Hong" ], "authors_short": "Zhang et al.", "year": 2023, "venue": "arXiv", "doi": "10.48550/arXiv.2306.14289", "url": "https://doi.org/10.48550/arXiv.2306.14289", "division": "methods", "also": [], "type": "Method", "crop": "Not crop-specific", "platform": "Not platform-specific", "in_catalog": false, "note": "MobileSAM, a lightweight Segment Anything model for mobile devices.", "cited_in": [ "Related work" ], "bibtex": "@misc{zhang2023sam,\n title={Faster Segment Anything: Towards Lightweight SAM for Mobile Applications}, \n author={Chaoning Zhang and Dongshen Han and Yu Qiao and Jung Uk Kim and Sung-Ho Bae and Seungkyu Lee and Choong Seon Hong},\n year={2023},\n eprint={2306.14289},\n archivePrefix={arXiv},\n primaryClass={cs.CV},\n url={https://arxiv.org/abs/2306.14289}, \n doi = {10.48550/arXiv.2306.14289}\n}" }, { "n": 42, "key": "kirillov2023segment", "title": "Segment anything", "authors": [ "Alexander Kirillov", "Eric Mintun", "Nikhila Ravi", "Hanzi Mao", "Chloe Rolland", "Laura Gustafson", "Tete Xiao", "Spencer Whitehead", "Alexander C Berg", "Wan-Yen Lo", "et al." ], "authors_short": "Kirillov et al.", "year": 2023, "venue": "Proceedings of the IEEE/CVF international conference on computer vision", "doi": "", "url": "", "division": "methods", "also": [], "type": "Method", "crop": "Not crop-specific", "platform": "Not platform-specific", "in_catalog": false, "note": "Segment Anything, a foundation model for promptable segmentation.", "cited_in": [ "Related work" ], "bibtex": "@inproceedings{kirillov2023segment,\n title={Segment anything},\n author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C and Lo, Wan-Yen and others},\n booktitle={Proceedings of the IEEE/CVF international conference on computer vision},\n pages={4015--4026},\n year={2023}\n}" }, { "n": 43, "key": "yang_real-time_2026", "title": "A real-time semantic 3D vineyard mapping and fruit localization system for yield estimation in dynamic vineyards", "authors": [ "Runbin Yang", "Xilong Zhang", "Yanyang Chen", "Xiaotao Huang", "Sang Feng" ], "authors_short": "Yang et al.", "year": 2026, "venue": "Smart Agricultural Technology", "doi": "10.1016/j.atech.2026.101784", "url": "https://doi.org/10.1016/j.atech.2026.101784", "division": "mapping", "also": [ "perception" ], "type": "System", "crop": "Grapevine", "platform": "Ground robot or vehicle", "in_catalog": false, "note": "Real-time semantic 3D mapping with ORB-SLAM3 and EFSegNet to localise and count grape bunches in dynamic vineyards, from a ground-based platform.", "cited_in": [ "Related work" ], "bibtex": "@article{yang_real-time_2026,\n\ttitle = {A real-time semantic {3D} vineyard mapping and fruit localization system for yield estimation in dynamic vineyards},\n\tvolume = {13},\n\tissn = {27723755},\n\turl = {https://linkinghub.elsevier.com/retrieve/pii/S2772375526000080},\n\tdoi = {10.1016/j.atech.2026.101784},\n\tabstract = {In precision viticulture, autonomous systems are increasingly deployed for inspection and yield estimation. However, mapping 3D vineyard environments and monitoring fruit distribution presents challenges due to fruit occlusion, moving objects, and unstructured backgrounds. To address these issues, this study presents a semantic 3D mapping and fruit localization system designed for yield estimation in dynamic environments. The proposed framework integrates the ORB-SLAM3 backend with a lightweight semantic segmentation model, named Edge Fusion Segmentation Network (EFSegNet). Derived from the PIDNet baseline, EFSegNet introduces a Gradient Feature Fusion Block (GFFM) and a Deformable Spatial Attention (DSA) module. These components are designed to refine fruit boundaries and separate overlapping grape bunches, a requirement for individual counting. The generated semantic masks guide the removal of dynamic feature points to maintain map consistency, while enabling semantic mapping of fruit clusters. Additionally, a three-stage point cloud filtering strategy is applied to refine the global map, followed by DBSCAN clustering and PCA-based modeling to extract the 3D location and quantity of grape bunches. Validation experiments in a real-world dynamic vineyard indicate that the system operated at 23.1 FPS, with an average 3D localization error of 21.02 mm. The average counting error was 5.95\\%, demonstrating the system’s applicability for automated yield estimation.},\n\tlanguage = {en},\n\turldate = {2026-02-25},\n\tjournal = {Smart Agricultural Technology},\n\tauthor = {Yang, Runbin and Zhang, Xilong and Chen, Yanyang and Huang, Xiaotao and Feng, Sang},\n\tmonth = {03},\n\tyear = {2026},\n\tpages = {101784},\n}" }, { "n": 44, "key": "campos2021orb", "title": "ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual–Inertial, and Multimap SLAM", "authors": [ "Carlos Campos", "Richard Elvira", "Juan J. Gómez Rodríguez", "José M. M. Montiel", "Juan D. Tardós" ], "authors_short": "Campos et al.", "year": 2021, "venue": "IEEE Transactions on Robotics", "doi": "10.1109/TRO.2021.3075644", "url": "https://doi.org/10.1109/TRO.2021.3075644", "division": "methods", "also": [], "type": "Method", "crop": "Not crop-specific", "platform": "Not platform-specific", "in_catalog": false, "note": "ORB-SLAM3 library for visual, visual-inertial and multi-map SLAM.", "cited_in": [ "Related work" ], "bibtex": "@ARTICLE{campos2021orb,\n author={Campos, Carlos and Elvira, Richard and Rodríguez, Juan J. Gómez and M. Montiel, José M. and D. Tardós, Juan},\n journal={IEEE Transactions on Robotics}, \n title={ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual–Inertial, and Multimap SLAM}, \n year={2021},\n volume={37},\n number={6},\n pages={1874-1890},\n keywords={Simultaneous localization and mapping;Computer vision;Feature extraction;Inertial navigation;Robustness;Optimization;Computer vision;inertial navigation;simult- aneous localization and mapping},\n doi={10.1109/TRO.2021.3075644}}" }, { "n": 45, "key": "ester1996density", "title": "A density-based algorithm for discovering clusters in large spatial databases with noise", "authors": [ "Martin Ester", "Hans-Peter Kriegel", "Jörg Sander", "Xiaowei Xu" ], "authors_short": "Ester et al.", "year": 1996, "venue": "Proceedings of the Second International Conference on Knowledge Discovery and Data Mining", "doi": "", "url": "", "division": "methods", "also": [], "type": "Method", "crop": "Not crop-specific", "platform": "Not platform-specific", "in_catalog": false, "note": "DBSCAN density-based clustering.", "cited_in": [ "Related work" ], "bibtex": "@inproceedings{ester1996density,\nauthor = {Ester, Martin and Kriegel, Hans-Peter and Sander, J{\\\"o}rg and Xu, Xiaowei},\ntitle = {A density-based algorithm for discovering clusters in large spatial databases with noise},\nyear = {1996},\npublisher = {AAAI Press},\nabstract = {Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, we present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. We performed an experimental evaluation of the effectiveness and efficiency of DBSCAN using synthetic data and real data of the SEQUOIA 2000 benchmark. The results of our experiments demonstrate that (1) DBSCAN is significantly more effective in discovering clusters of arbitrary shape than the well-known algorithm CLAR-ANS, and that (2) DBSCAN outperforms CLARANS by a factor of more than 100 in terms of efficiency.},\nbooktitle = {Proceedings of the Second International Conference on Knowledge Discovery and Data Mining},\npages = {226–231},\nnumpages = {6},\nkeywords = {handling nlj4-275oise, efficiency on large spatial databases, clustering algorithms, arbitrary shape of clusters},\nlocation = {Portland, Oregon},\nseries = {KDD'96}\n}" }, { "n": 46, "key": "hotelling1933analysis", "title": "Analysis of a complex of statistical variables into principal components.", "authors": [ "Harold Hotelling" ], "authors_short": "Hotelling", "year": 1933, "venue": "Journal of educational psychology", "doi": "10.1037/h0071325", "url": "https://doi.org/10.1037/h0071325", "division": "methods", "also": [], "type": "Method", "crop": "Not crop-specific", "platform": "Not platform-specific", "in_catalog": false, "note": "Principal component analysis.", "cited_in": [ "Related work" ], "bibtex": "@article{hotelling1933analysis,\n title={Analysis of a complex of statistical variables into principal components.},\n author={Hotelling, Harold},\n journal={Journal of educational psychology},\n volume={24},\n number={6},\n pages={417-441},\n year={1933},\n publisher={Warwick \\& York},\n doi = {10.1037/h0071325}\n}" }, { "n": 47, "key": "huang_review_2025", "title": "A review of visual perception technology for intelligent fruit harvesting robots", "authors": [ "Yikun Huang", "Shuyan Xu", "Hao Chen", "Gang Li", "Heng Dong", "Jie Yu", "Xi Zhang", "Riqing Chen" ], "authors_short": "Huang et al.", "year": 2025, "venue": "Frontiers in Plant Science", "doi": "10.3389/fpls.2025.1646871", "url": "https://doi.org/10.3389/fpls.2025.1646871", "division": "control", "also": [ "perception" ], "type": "Review", "crop": "Other crops", "platform": "Ground robot or vehicle", "in_catalog": false, "note": "Review of visual perception for fruit harvesting robots: cameras, detection, picking-point localisation, active vision, visual servoing, V-SLAM and path planning.", "cited_in": [ "Related work" ], "bibtex": "@article{huang_review_2025,\n\ttitle = {A review of visual perception technology for intelligent fruit harvesting robots},\n\tvolume = {16},\n\tissn = {1664-462X},\n\turl = {https://www.frontiersin.org/articles/10.3389/fpls.2025.1646871/full},\n\tdoi = {10.3389/fpls.2025.1646871},\n\tabstract = {With the development of smart agriculture, fruit picking robots have attracted widespread attention as one of the key technologies to improve agricultural productivity. Visual perception technology plays a crucial role in fruit picking robots, involving precise fruit identification, localization, and grasping operations. This paper reviews the research progress in the visual perception technology for fruit picking robots, focusing on key technologies such as camera types used in picking robots, object detection techniques, picking point recognition and localization, active vision, and visual servoing. First, the paper introduces the application characteristics and selection criteria of different camera types in the fruit picking process. Then, it analyzes how object detection techniques help robots accurately recognize fruits and achieve efficient fruit classification. Next, it discusses the picking point recognition and localization technologies, including vision-based 3D reconstruction and depth sensing methods. Subsequently, it elaborates on the adaptability of active vision technology in dynamic environments and how visual servoing technology achieves precise localization. Additionally, the review explores robot mobility perception technologies, focusing on V-SLAM, mobile path planning, and task scheduling. These technologies enhance harvesting efficiency across the entire orchard and facilitate better collaboration among multiple robots. Finally, the paper summarizes the challenges in current research and the future development trends, aiming to provide references for the optimization and promotion of fruit picking robot technology.},\n\tlanguage = {en},\n\turldate = {2026-02-12},\n\tjournal = {Frontiers in Plant Science},\n\tauthor = {Huang, Yikun and Xu, Shuyan and Chen, Hao and Li, Gang and Dong, Heng and Yu, Jie and Zhang, Xi and Chen, Riqing},\n\tmonth = {08},\n\tyear = {2025},\n\tpages = {1646871},\n}" }, { "n": 48, "key": "taketomi2017visual", "title": "Visual SLAM algorithms: A survey from 2010 to 2016", "authors": [ "Takafumi Taketomi", "Hideaki Uchiyama", "Sei Ikeda" ], "authors_short": "Taketomi et al.", "year": 2017, "venue": "IPSJ transactions on computer vision and applications", "doi": "10.1186/s41074-017-0027-2", "url": "https://doi.org/10.1186/s41074-017-0027-2", "division": "methods", "also": [], "type": "Review", "crop": "Not crop-specific", "platform": "Not platform-specific", "in_catalog": false, "note": "Survey of visual SLAM algorithms from 2010 to 2016.", "cited_in": [ "Related work" ], "bibtex": "@article{taketomi2017visual,\n title={Visual SLAM algorithms: A survey from 2010 to 2016},\n author={Taketomi, Takafumi and Uchiyama, Hideaki and Ikeda, Sei},\n journal={IPSJ transactions on computer vision and applications},\n volume={9},\n number={1},\n pages={16},\n year={2017},\n publisher={Springer},\n doi = {10.1186/s41074-017-0027-2}\n}" }, { "n": 49, "key": "tan_review_2025", "title": "A Review of Research on Fruit and Vegetable Picking Robots Based on Deep Learning", "authors": [ "Yarong Tan", "Xin Liu", "Jinmeng Zhang", "Yigang Wang", "Yanxiang Hu" ], "authors_short": "Tan et al.", "year": 2025, "venue": "Sensors", "doi": "10.3390/s25123677", "url": "https://doi.org/10.3390/s25123677", "division": "control", "also": [ "perception" ], "type": "Review", "crop": "Other crops", "platform": "Ground robot or vehicle", "in_catalog": false, "note": "Review of deep learning for picking robots: target recognition, path planning and motion control, and end-effector control.", "cited_in": [ "Related work" ], "bibtex": "@article{tan_review_2025,\n\ttitle = {A {Review} of {Research} on {Fruit} and {Vegetable} {Picking} {Robots} {Based} on {Deep} {Learning}},\n\tvolume = {25},\n\tissn = {1424-8220},\n\turl = {https://www.mdpi.com/1424-8220/25/12/3677},\n\tdoi = {10.3390/s25123677},\n\tabstract = {Fruit and vegetable picking robots are considered an important way to promote agricultural modernization due to their high efficiency, precision, and intelligence. However, most of the existing research has sporadically involved single application areas, such as object detection, classification, and path planning, and has not yet comprehensively sorted out the core applications of deep learning technology in fruit and vegetable picking robots, the current technological bottlenecks faced, and future development directions. This review summarizes the key technologies and applications of deep learning in the visual perception and target recognition, path planning and motion control, and intelligent control of end effectors of fruit and vegetable picking robots. It focuses on the optimization strategies and common problems related to deep learning and explores the challenges and development trends of deep learning in improving the perception accuracy, multi-sensor collaboration, multimodal data fusion, adaptive control, and human–computer interaction of fruit and vegetable picking robots in the future. The aim is to provide theoretical support and practical guidance for the practical application of deep learning technology in fruit and vegetable picking robots.},\n\tlanguage = {en},\n\tnumber = {12},\n\turldate = {2026-02-04},\n\tjournal = {Sensors},\n\tauthor = {Tan, Yarong and Liu, Xin and Zhang, Jinmeng and Wang, Yigang and Hu, Yanxiang},\n\tmonth = {06},\n\tyear = {2025},\n\tpages = {3677},\n}" }, { "n": 50, "key": "burusa_attention-driven_2024", "title": "Attention-driven Next-best-view Planning for Efficient Reconstruction of Plants and Targeted Plant Parts", "authors": [ "Akshay K. Burusa", "Eldert J. van Henten", "Gert Kootstra" ], "authors_short": "Burusa et al.", "year": 2024, "venue": "Biosystems Engineering", "doi": "10.1016/j.biosystemseng.2024.08.002", "url": "https://doi.org/10.1016/j.biosystemseng.2024.08.002", "division": "mapping", "also": [ "control" ], "type": "Method", "crop": "Other crops", "platform": "Unspecified", "in_catalog": false, "note": "Attention-driven next-best-view planning improves reconstruction of tomato plant parts. Relevant to choosing viewpoints for grape sampling.", "cited_in": [ "Related work" ], "bibtex": "@article{burusa_attention-driven_2024,\n\ttitle = {Attention-driven {Next}-best-view {Planning} for {Efficient} {Reconstruction} of {Plants} and {Targeted} {Plant} {Parts}},\n\tvolume = {246},\n\tissn = {15375110},\n\turl = {http://arxiv.org/abs/2206.10274},\n\tdoi = {10.1016/j.biosystemseng.2024.08.002},\n\tabstract = {Robots in tomato greenhouses need to perceive the plant and plant parts accurately to automate monitoring, harvesting, and deleafing tasks. Existing perception systems struggle with the high levels of occlusion in plants and often result in poor perception accuracy. One reason for this is because they use fixed cameras or predefined camera movements. Next-best-view (NBV) planning presents a alternate approach, in which the camera viewpoints are reasoned and strategically planned such that the perception accuracy is improved. However, existing NBV-planning algorithms are agnostic to the task-at-hand and give equal importance to all the plant parts. This strategy is inefficient for greenhouse tasks that require targeted perception of specific plant parts, such as the perception of leaf nodes for de-leafing. To improve targeted perception in complex greenhouse environments, NBV planning algorithms need an attention mechanism to focus on the task-relevant plant parts. In this paper, we investigated the role of attention in improving targeted perception using an attention-driven NBV planning strategy. Through simulation experiments using plants with high levels of occlusion and structural complexity, we showed that focusing attention on task-relevant plant parts can significantly improve the speed and accuracy of 3D reconstruction. Further, with real-world experiments, we showed that these benefits extend to complex greenhouse conditions with natural variation and occlusion, natural illumination, sensor noise, and uncertainty in camera poses. Our results clearly indicate that using attention-driven NBV planning in greenhouses can significantly improve the efficiency of perception and enhance the performance of robotic systems in greenhouse crop production.},\n\tlanguage = {en},\n\turldate = {2026-02-25},\n\tjournal = {Biosystems Engineering},\n\tauthor = {Burusa, Akshay K. and Henten, Eldert J. van and Kootstra, Gert},\n\tmonth = {08},\n\tyear = {2024},\n\tnote = {arXiv:2206.10274 [cs]},\n\tkeywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics},\n\tpages = {248--262},\n}" }, { "n": 51, "key": "peng_vision-based_2025", "title": "A Vision-Based Information Processing Framework for Vineyard Grape Picking Using Two-Stage Segmentation and Morphological Perception", "authors": [ "Yifei Peng", "Jun Sun", "Zhaoqi Wu", "Jinye Gao", "Lei Shi", "Zhiyan Shi" ], "authors_short": "Peng et al.", "year": 2025, "venue": "Horticulturae", "doi": "10.3390/horticulturae11091039", "url": "https://doi.org/10.3390/horticulturae11091039", "division": "control", "also": [ "perception" ], "type": "Method", "crop": "Grapevine", "platform": "Unspecified", "in_catalog": false, "note": "Two-stage segmentation (improved YOLOv8s-seg, then OTSU on the hue channel) finds the picking point and cutting axis on grape stems. Works in 2D without depth.", "cited_in": [ "Related work" ], "bibtex": "@article{peng_vision-based_2025,\n\ttitle = {A {Vision}-{Based} {Information} {Processing} {Framework} for {Vineyard} {Grape} {Picking} {Using} {Two}-{Stage} {Segmentation} and {Morphological} {Perception}},\n\tvolume = {11},\n\tissn = {2311-7524},\n\turl = {https://www.mdpi.com/2311-7524/11/9/1039},\n\tdoi = {10.3390/horticulturae11091039},\n\tabstract = {To achieve efficient vineyard grape picking, a vision-based information processing framework integrating two-stage segmentation with morphological perception is proposed. In the first stage, an improved YOLOv8s-seg model is employed for coarse segmentation, incorporating two key enhancements: first, a dynamic deformation feature aggregation module (DDFAM), which facilitates the extraction of complex structural and morphological features; and second, an efficient asymmetric decoupled head (EADHead), which improves boundary awareness while reducing parameter redundancy. Compared with mainstream segmentation models, the improved model achieves superior performance, attaining the highest mAP@0.5 of 86.75\\%, a lightweight structure with 10.34 M parameters, and a realtime inference speed of 10.02 ms per image. In the second stage, the fine segmentation of fruit stems is performed using an improved OTSU thresholding algorithm, which is applied to a single-channel image derived from the hue component of the HSV color space, thereby enhancing robustness under complex lighting conditions. Morphological features extracted from the preprocessed fruit stem, including centroid coordinates and a skeleton constructed via medial axis transform (MAT), are further utilized to establish the spatial relationships with a picking point and cutting axis. The visualization analysis confirms the high feasibility and adaptability of the proposed framework, providing essential technical support for the automation of grape harvesting.},\n\tlanguage = {en},\n\tnumber = {9},\n\turldate = {2026-02-25},\n\tjournal = {Horticulturae},\n\tauthor = {Peng, Yifei and Sun, Jun and Wu, Zhaoqi and Gao, Jinye and Shi, Lei and Shi, Zhiyan},\n\tmonth = {09},\n\tyear = {2025},\n\tpages = {1039},\n}" }, { "n": 52, "key": "liu_visionassisted_2025", "title": "Vision‐Assisted Avocado Harvesting with Aerial Bimanual Manipulation", "authors": [ "Zhichao Liu", "Jingzong Zhou", "Caio Mucchiani", "Konstantinos Karydis" ], "authors_short": "Liu et al.", "year": 2025, "venue": "Advanced Robotics Research", "doi": "10.1002/adrr.202500003", "url": "https://doi.org/10.1002/adrr.202500003", "division": "control", "also": [ "uav" ], "type": "System", "crop": "Other crops", "platform": "UAV", "in_catalog": false, "note": "Aerial bimanual manipulation for avocado harvesting with a DJI platform, YOLOv8n segmentation and depth-based pose. Reports limits in reach, wind sensitivity, payload, energy and flight time.", "cited_in": [ "Related work" ], "bibtex": "@article{liu_visionassisted_2025,\n\ttitle = {Vision‐{Assisted} {Avocado} {Harvesting} with {Aerial} {Bimanual} {Manipulation}},\n\tissn = {2943-9973, 2943-9973},\n\turl = {https://advanced.onlinelibrary.wiley.com/doi/10.1002/adrr.202500003},\n\tdoi = {10.1002/adrr.202500003},\n\tabstract = {Robotic fruit harvesting holds potential in precision agriculture to improve harvesting efficiency. While ground mobile robots are mostly employed in fruit harvesting, certain crops, like avocado trees, cannot be harvested efficiently from the ground alone. This is because of unstructured ground and planting arrangement and high-to-reach fruits. In such cases, aerial robots integrated with manipulation capabilities can pave new ways in robotic harvesting. This paper outlines the design and implementation of a bimanual aerial robot that employs visual perception and learning to detect avocados, reach, and harvest them autonomously. The dualarm system comprises a gripper and a fixer arm, to address a key challenge when harvesting avocados: once grasped, applying a rotational motion is the most mechanically efficient way to detach the avocado from the peduncle; however, the peduncle may store elastic energy preventing the avocado from being harvested. The fixer arm aims to stabilize the peduncle, allowing the gripper arm to harvest. The integrated visual perception process enables the detection of avocados and the determination of their pose; the latter is then used to determine target points for a bimanual manipulation planner. Several experiments are conducted in controlled indoor and outdoor settings to assess the efficacy of each component individually. Further, an integrated experiment in outdoor semicontrolled settings is used for feasibility assessment of the overall system. Results demonstrate that all different components can work synergistically to enable robotic avocado harvesting in (semi-)controlled settings. Results also highlight limitations of an airborne harvesting solution and reveal tradeoffs to be considered in the selection of a harvesting robot.},\n\tlanguage = {en},\n\turldate = {2026-02-12},\n\tjournal = {Advanced Robotics Research},\n\tauthor = {Liu, Zhichao and Zhou, Jingzong and Mucchiani, Caio and Karydis, Konstantinos},\n\tmonth = {05},\n\tyear = {2025},\n\tpages = {202500003},\n}" }, { "n": 53, "key": "samoili_2025_17748130", "title": "Drone-Enabled Data Collection for Sustainable Viticulture: A Practical Framework for Precision Viticulture", "authors": [ "Sofia Samoili", "João Gaspar", "Renato Ferreira", "Maria da Graça Vieira Duque Carreira Monteiro", "Pedro Sebastião", "Sofia Kalakou" ], "authors_short": "Samoili et al.", "year": 2025, "venue": "Springer", "doi": "10.5281/zenodo.17748130", "url": "https://doi.org/10.5281/zenodo.17748130", "division": "uav", "also": [ "monitoring" ], "type": "Guidelines", "crop": "Grapevine", "platform": "UAV", "in_catalog": false, "note": "Practical drone-operation process from the SmartVitiNet project: equipment setup, pre-flight route planning and post-flight data management.", "cited_in": [ "Related work" ], "bibtex": "@misc{samoili_2025_17748130,\n author = {Samoili, Sofia and\n Gaspar, João and\n Ferreira, Renato and\n Vieira Duque Carreira Monteiro, Maria da Graça and\n Sebastião, Pedro and\n Kalakou, Sofia},\n title = {Drone-Enabled Data Collection for Sustainable\n Viticulture: A Practical Framework for Precision\n Viticulture\n },\n month = {11},\n year = 2025,\n publisher = {Springer},\n doi = {10.5281/zenodo.17748130},\n url = {https://doi.org/10.5281/zenodo.17748130},\n}" }, { "n": 54, "key": "debnath_review_2024", "title": "A Review of UAV Path-Planning Algorithms and Obstacle Avoidance Methods for Remote Sensing Applications", "authors": [ "Dipraj Debnath", "Fernando Vanegas", "Juan Sandino", "Ahmad Faizul Hawary", "Felipe Gonzalez" ], "authors_short": "Debnath et al.", "year": 2024, "venue": "Remote Sensing", "doi": "10.3390/rs16214019", "url": "https://doi.org/10.3390/rs16214019", "division": "uav", "also": [], "type": "Review", "crop": "Not crop-specific", "platform": "UAV", "in_catalog": false, "note": "Review of UAV path planning and obstacle avoidance for remote sensing. Classical planners suit static settings, adaptive ones suit dynamic settings at higher computational cost.", "cited_in": [ "Related work" ], "bibtex": "@article{debnath_review_2024,\n\ttitle = {A {Review} of {UAV} {Path}-{Planning} {Algorithms} and {Obstacle} {Avoidance} {Methods} for {Remote} {Sensing} {Applications}},\n\tvolume = {16},\n\tissn = {2072-4292},\n\turl = {https://www.mdpi.com/2072-4292/16/21/4019},\n\tdoi = {10.3390/rs16214019},\n\tabstract = {The rapid development of uncrewed aerial vehicles (UAVs) has significantly increased their usefulness in various fields, particularly in remote sensing. This paper provides a comprehensive review of UAV path planning, obstacle detection, and avoidance methods, with a focus on its utilisation in both single and multiple UAV platforms. The paper classifies the algorithms into two main categories: (1) global and local path-planning approaches in single UAVs; and (2) multi-UAV path-planning methods. It further analyses obstacle detection and avoidance methods, as well as their capacity to adapt, optimise, and compute efficiently in different operational environments. The outcomes highlight the advantages and limitations of each method, offering valuable information regarding their suitability for remote sensing applications, such as precision agriculture, urban mapping, and ecological surveillance. Additionally, this review also identifies limitations in the existing research, specifically in multi-UAV frameworks, and provides recommendations for future developments to improve the adaptability and effectiveness of UAV operations in dynamic and complex situations.},\n\tlanguage = {en},\n\tnumber = {21},\n\turldate = {2026-02-04},\n\tjournal = {Remote Sensing},\n\tauthor = {Debnath, Dipraj and Vanegas, Fernando and Sandino, Juan and Hawary, Ahmad Faizul and Gonzalez, Felipe},\n\tmonth = {10},\n\tyear = {2024},\n\tpages = {4019},\n}" }, { "n": 55, "key": "hart1968formal", "title": "A formal basis for the heuristic determination of minimum cost paths", "authors": [ "Peter E Hart", "Nils J Nilsson", "Bertram Raphael" ], "authors_short": "Hart et al.", "year": 1968, "venue": "IEEE transactions on Systems Science and Cybernetics", "doi": "10.1109/TSSC.1968.300136", "url": "https://doi.org/10.1109/TSSC.1968.300136", "division": "methods", "also": [], "type": "Method", "crop": "Not crop-specific", "platform": "Not platform-specific", "in_catalog": false, "note": "A* heuristic search for minimum-cost paths.", "cited_in": [ "Related work" ], "bibtex": "@article{hart1968formal,\n title={A formal basis for the heuristic determination of minimum cost paths},\n author={Hart, Peter E and Nilsson, Nils J and Raphael, Bertram},\n journal={IEEE transactions on Systems Science and Cybernetics},\n volume={4},\n number={2},\n pages={100--107},\n year={1968},\n publisher={IEEE},\n keywords={Costs;Mathematical programming;Minimization methods;Functional programming;Automatic control;Minimax techniques;Gradient methods;Chemical technology;Automatic programming;Instruction sets},\n doi={10.1109/TSSC.1968.300136}\n}" }, { "n": 56, "key": "shapiro1999genetic", "title": "Genetic Algorithms in Machine Learning", "authors": [ "Jonathan Shapiro" ], "authors_short": "Shapiro", "year": 2001, "venue": "Machine Learning and Its Applications: Advanced Lectures", "doi": "10.1007/3-540-44673-7_7", "url": "https://doi.org/10.1007/3-540-44673-7_7", "division": "methods", "also": [], "type": "Method", "crop": "Not crop-specific", "platform": "Not platform-specific", "in_catalog": false, "note": "Genetic algorithms in machine learning.", "cited_in": [ "Related work" ], "bibtex": "@Inbook{shapiro1999genetic,\nauthor=\"Shapiro, Jonathan\",\neditor=\"Paliouras, Georgios\nand Karkaletsis, Vangelis\nand Spyropoulos, Constantine D.\",\ntitle=\"Genetic Algorithms in Machine Learning\",\nbookTitle=\"Machine Learning and Its Applications: Advanced Lectures\",\nyear=\"2001\",\npublisher=\"Springer Berlin Heidelberg\",\naddress=\"Berlin, Heidelberg\",\npages=\"146--168\",\nabstract=\"Genetic algorithms are stochastic search algorithms which act on a population of possible solutions. They are loosely based on the mechanics of population genetics and selection. The potential solutions are encoded as `genes' --- strings of characters from some alphabet. New solutions can be produced by `mutating' members of the current population, and by `mating' two solutions together to form a new solution. The better solutions are selected to breed and mutate and the worse ones are discarded. They are probabilistic search methods; this means that the states which they explore are not determined solely by the properties of the problems. A random process helps to guide the search. Genetic algorithms are used in artificial intelligence like other search algorithms are used in artificial intelligence --- to search a space of potential solutions to find one which solves the problem.\",\nisbn=\"978-3-540-44673-6\",\ndoi=\"10.1007/3-540-44673-7_7\",\nurl=\"https://doi.org/10.1007/3-540-44673-7_7\"\n}" }, { "n": 57, "key": "kennedy1995particle", "title": "Particle swarm optimization", "authors": [ "J. Kennedy", "R. Eberhart" ], "authors_short": "Kennedy and Eberhart", "year": 1995, "venue": "Proceedings of ICNN'95 - International Conference on Neural Networks", "doi": "10.1109/ICNN.1995.488968", "url": "https://doi.org/10.1109/ICNN.1995.488968", "division": "methods", "also": [], "type": "Method", "crop": "Not crop-specific", "platform": "Not platform-specific", "in_catalog": false, "note": "Particle swarm optimisation.", "cited_in": [ "Related work" ], "bibtex": "@INPROCEEDINGS{kennedy1995particle,\n author={Kennedy, J. and Eberhart, R.},\n booktitle={Proceedings of ICNN'95 - International Conference on Neural Networks}, \n title={Particle swarm optimization}, \n year={1995},\n volume={4},\n number={},\n pages={1942-1948 vol.4},\n keywords={Particle swarm optimization;Birds;Educational institutions;Marine animals;Testing;Humans;Genetic algorithms;Optimization methods;Artificial neural networks;Performance evaluation},\n doi={10.1109/ICNN.1995.488968}}" }, { "n": 58, "key": "thiago_santos_2019_3361736", "title": "Embrapa Wine Grape Instance Segmentation Dataset – Embrapa WGISD", "authors": [ "Thiago Santos", "Leonardo de Souza", "dos Santos Andreza", "Sandra Avila" ], "authors_short": "Santos et al.", "year": 2019, "venue": "Zenodo", "doi": "10.5281/zenodo.3361736", "url": "https://doi.org/10.5281/zenodo.3361736", "division": "datasets", "also": [ "perception" ], "type": "Dataset", "crop": "Grapevine", "platform": "Handheld or ground camera", "in_catalog": true, "note": "Embrapa WGISD. 300 images with 4,431 grape clusters of five varieties from Guaspari Winery, Brazil, for detection and instance segmentation. CC BY-NC 4.0.", "cited_in": [ "Available datasets" ], "bibtex": "@misc{thiago_santos_2019_3361736,\n author = {Thiago Santos and\n de Souza, Leonardo and\n dos Santos Andreza and\n Avila, Sandra},\n title = {Embrapa Wine Grape Instance Segmentation Dataset –\n Embrapa WGISD\n },\n month = {07},\n year = 2019,\n publisher = {Zenodo},\n version = {1.0.0},\n doi = {10.5281/zenodo.3361736},\n url = {https://doi.org/10.5281/zenodo.3361736},\n}" }, { "n": 59, "key": "s24092953", "title": "MultiFuseYOLO: Redefining Wine Grape Variety Recognition through Multisource Information Fusion", "authors": [ "Jialiang Peng", "Cheng Ouyang", "Hao Peng", "Wenwu Hu", "Yi Wang", "Ping Jiang" ], "authors_short": "Peng et al.", "year": 2024, "venue": "Sensors", "doi": "10.3390/s24092953", "url": "https://doi.org/10.3390/s24092953", "division": "perception", "also": [], "type": "Method", "crop": "Grapevine", "platform": "Unspecified", "in_catalog": false, "note": "MultiFuseYOLO for wine grape variety recognition through multisource fusion. Cited as research using Embrapa WGISD.", "cited_in": [ "Available datasets" ], "bibtex": "@Article{s24092953,\nAUTHOR = {Peng, Jialiang and Ouyang, Cheng and Peng, Hao and Hu, Wenwu and Wang, Yi and Jiang, Ping},\nTITLE = {MultiFuseYOLO: Redefining Wine Grape Variety Recognition through Multisource Information Fusion},\nJOURNAL = {Sensors},\nVOLUME = {24},\nYEAR = {2024},\nNUMBER = {9},\nARTICLE-NUMBER = {2953},\nURL = {https://www.mdpi.com/1424-8220/24/9/2953},\nPubMedID = {38733058},\nISSN = {1424-8220},\nDOI = {10.3390/s24092953}\n}" }, { "n": 60, "key": "wgisd_uc2022", "title": "A Novel Lightweight Grape Detection Method", "authors": [ "Shuzhi Su", "Runbin Chen", "Xianjin Fang", "Yanmin Zhu", "Tian Zhang", "Zengbao Xu" ], "authors_short": "Su et al.", "year": 2022, "venue": "Agriculture", "doi": "10.3390/agriculture12091364", "url": "https://doi.org/10.3390/agriculture12091364", "division": "perception", "also": [], "type": "Method", "crop": "Grapevine", "platform": "Unspecified", "in_catalog": false, "note": "Lightweight grape detection method. Cited as research using Embrapa WGISD.", "cited_in": [ "Available datasets" ], "bibtex": "@article{wgisd_uc2022,\nauthor = {Su, Shuzhi and Chen, Runbin and Fang, Xianjin and Zhu, Yanmin and Zhang, Tian and Xu, Zengbao},\nyear = {2022},\nmonth = {09},\npages = {1364},\ntitle = {A Novel Lightweight Grape Detection Method},\nvolume = {12},\njournal = {Agriculture},\ndoi = {10.3390/agriculture12091364}\n}" }, { "n": 61, "key": "ZHAO2022174", "title": "An end-to-end lightweight model for grape and picking point simultaneous detection", "authors": [ "Ruzhun Zhao", "Yuchang Zhu", "Yuanhong Li" ], "authors_short": "Zhao et al.", "year": 2022, "venue": "Biosystems Engineering", "doi": "10.1016/j.biosystemseng.2022.08.013", "url": "https://doi.org/10.1016/j.biosystemseng.2022.08.013", "division": "perception", "also": [ "control" ], "type": "Method", "crop": "Grapevine", "platform": "Unspecified", "in_catalog": false, "note": "End-to-end lightweight model that detects grapes and picking points together. Cited as research using Embrapa WGISD.", "cited_in": [ "Available datasets" ], "bibtex": "@article{ZHAO2022174,\ntitle = {An end-to-end lightweight model for grape and picking point simultaneous detection},\njournal = {Biosystems Engineering},\nvolume = {223},\npages = {174-188},\nyear = {2022},\nissn = {1537-5110},\ndoi = {10.1016/j.biosystemseng.2022.08.013},\nurl = {https://www.sciencedirect.com/science/article/pii/S1537511022001970},\nauthor = {Ruzhun Zhao and Yuchang Zhu and Yuanhong Li},\nkeywords = {Deep learning, Object detection, Computer vision, Grape picking}\n}" }, { "n": 62, "key": "GrapeCS-ML:2019", "title": "Grape image database", "authors": [ "Jasmine Seng", "Kenneth Ang", "Leigh Schmidtke", "Suzy Rogiers" ], "authors_short": "Seng et al.", "year": 2018, "venue": "Charles Sturt University", "doi": "10.26189/5da7a8603c55c", "url": "https://doi.org/10.26189/5da7a8603c55c", "division": "datasets", "also": [], "type": "Dataset", "crop": "Grapevine", "platform": "Handheld or ground camera", "in_catalog": true, "note": "GrapeCS-ML. 2,078 images of grape bunches from three Australian vineyards at different stages, with pH and Brix from chemical analysis.", "cited_in": [ "Available datasets" ], "bibtex": "@misc{GrapeCS-ML:2019,\n author = {Seng, Jasmine and Ang, Kenneth and Schmidtke, Leigh and Rogiers, Suzy},\n title = {Grape image database},\n year = {2018},\n publisher = {Charles Sturt University},\n doi = {10.26189/5da7a8603c55c},\n url = {https://researchoutput.csu.edu.au/en/datasets/grape-image-database/},\n version = {v1},\n note = {Includes GrapeDatabase.zip and Dyostem\\_App\\_Project\\_2017\\_Full\\_Data\\_Set.xlsx}\n}" }, { "n": 63, "key": "s21113908", "title": "In-Field Automatic Detection of Grape Bunches under a Totally Uncontrolled Environment", "authors": [ "Luca Ghiani", "Alberto Sassu", "Francesca Palumbo", "Luca Mercenaro", "Filippo Gambella" ], "authors_short": "Ghiani et al.", "year": 2021, "venue": "Sensors", "doi": "10.3390/s21113908", "url": "https://doi.org/10.3390/s21113908", "division": "perception", "also": [], "type": "Method", "crop": "Grapevine", "platform": "Unspecified", "in_catalog": false, "note": "In-field detection of grape bunches in an uncontrolled environment. Cited as research using GrapeCS-ML.", "cited_in": [ "Available datasets" ], "bibtex": "@Article{s21113908,\nAUTHOR = {Ghiani, Luca and Sassu, Alberto and Palumbo, Francesca and Mercenaro, Luca and Gambella, Filippo},\nTITLE = {In-Field Automatic Detection of Grape Bunches under a Totally Uncontrolled Environment},\nJOURNAL = {Sensors},\nVOLUME = {21},\nYEAR = {2021},\nNUMBER = {11},\nARTICLE-NUMBER = {3908},\nURL = {https://www.mdpi.com/1424-8220/21/11/3908},\nPubMedID = {34198844},\nISSN = {1424-8220},\nDOI = {10.3390/s21113908}\n}" }, { "n": 64, "key": "ARIZASENTIS2023108848", "title": "Dataset on UAV RGB videos acquired over a vineyard including bunch labels for object detection and tracking", "authors": [ "Mar Ariza-Sentís", "Sergio Vélez", "João Valente" ], "authors_short": "Ariza-Sentís et al.", "year": 2023, "venue": "Data in Brief", "doi": "10.1016/j.dib.2022.108848", "url": "https://doi.org/10.1016/j.dib.2022.108848", "division": "datasets", "also": [], "type": "Dataset", "crop": "Grapevine", "platform": "UAV", "in_catalog": true, "note": "Bodegas Terras Gauda. 40 UAV RGB videos over four vineyard rows at an early stage of Botrytis cinerea infection, with cluster annotations for 29 videos. CC BY 4.0.", "cited_in": [ "Available datasets" ], "bibtex": "@article{ARIZASENTIS2023108848,\ntitle = {Dataset on UAV RGB videos acquired over a vineyard including bunch labels for object detection and tracking},\njournal = {Data in Brief},\nvolume = {46},\npages = {108848},\nyear = {2023},\nissn = {2352-3409},\ndoi = {10.1016/j.dib.2022.108848},\nurl = {https://www.sciencedirect.com/science/article/pii/S2352340922010514},\nauthor = {Mar Ariza-Sentís and Sergio Vélez and João Valente},\nkeywords = {Viticulture, Precision agriculture, Object detection, Object tracking, Remote sensing, UAV}\n}" }, { "n": 65, "key": "Polvara:2024", "title": "Bacchus Long-Term (BLT) data set: Acquisition of the agricultural multimodal BLT data set with automated robot deployment", "authors": [ "Riccardo Polvara", "Sergi Molina", "Ibrahim Hroob", "Alexios Papadimitriou", "Konstantinos Tsiolis", "Dimitrios Giakoumis", "Spiridon Likothanassis", "Dimitrios Tzovaras", "Grzegorz Cielniak", "Marc Hanheide" ], "authors_short": "Polvara et al.", "year": 2024, "venue": "Journal of Field Robotics", "doi": "10.1002/rob.22228", "url": "https://doi.org/10.1002/rob.22228", "division": "datasets", "also": [ "mapping" ], "type": "Dataset", "crop": "Grapevine", "platform": "Ground robot or vehicle", "in_catalog": true, "note": "Bacchus Long-Term (BLT). Robot sessions over one vineyard in Greece from March to September 2022, extended to a UK vineyard in 2023 to assess generalisation.", "cited_in": [ "Available datasets" ], "bibtex": "@article{Polvara:2024,\nauthor = {Polvara, Riccardo and Molina, Sergi and Hroob, Ibrahim and Papadimitriou, Alexios and Tsiolis, Konstantinos and Giakoumis, Dimitrios and Likothanassis, Spiridon and Tzovaras, Dimitrios and Cielniak, Grzegorz and Hanheide, Marc},\ntitle = {Bacchus Long-Term (BLT) data set: Acquisition of the agricultural multimodal BLT data set with automated robot deployment},\njournal = {Journal of Field Robotics},\nvolume = {41},\nnumber = {7},\npages = {2280-2298},\nkeywords = {agriculture, autonomous navigation, data set, field robotics, long-term, mobile robots, robot localization},\ndoi = {10.1002/rob.22228},\nurl = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rob.22228},\neprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/rob.22228},\nyear = {2024}\n}" }, { "n": 66, "key": "VELEZ2023108876", "title": "Dataset on unmanned aerial vehicle multispectral images acquired over a vineyard affected by Botrytis cinerea in northern Spain", "authors": [ "Sergio Vélez", "Mar Ariza-Sentís", "João Valente" ], "authors_short": "Vélez et al.", "year": 2023, "venue": "Data in Brief", "doi": "10.1016/j.dib.2022.108876", "url": "https://doi.org/10.1016/j.dib.2022.108876", "division": "datasets", "also": [], "type": "Dataset", "crop": "Grapevine", "platform": "UAV", "in_catalog": true, "note": "Multispectral Botrytis. 16,504 multispectral UAV images of a 1.06 ha Botrytis-affected vineyard in northern Spain, four flights at 30 m with varying camera angles.", "cited_in": [ "Available datasets" ], "bibtex": "@article{VELEZ2023108876,\ntitle = {Dataset on unmanned aerial vehicle multispectral images acquired over a vineyard affected by Botrytis cinerea in northern Spain},\njournal = {Data in Brief},\nvolume = {46},\npages = {108876},\nyear = {2023},\nissn = {2352-3409},\ndoi = {10.1016/j.dib.2022.108876},\nurl = {https://www.sciencedirect.com/science/article/pii/S2352340922010794},\nauthor = {Sergio Vélez and Mar Ariza-Sentís and João Valente},\nkeywords = {Viticulture, Precision agriculture, Disease detection, UAV, Drone, Orthomosaic, Structure from motion, Photogrammetry}\n}" }, { "n": 67, "key": "rs18040659", "title": "A Systematic Review of Available Multispectral UAV Image Datasets for Precision Agriculture Applications", "authors": [ "Andrea Caroppo", "Giovanni Diraco", "Alessandro Leone" ], "authors_short": "Caroppo et al.", "year": 2026, "venue": "Remote Sensing", "doi": "10.3390/rs18040659", "url": "https://doi.org/10.3390/rs18040659", "division": "datasets", "also": [], "type": "Review", "crop": "Agriculture in general", "platform": "UAV", "in_catalog": false, "note": "Systematic review of public multispectral UAV image datasets for precision agriculture. Cited as the broader list beyond viticulture.", "cited_in": [ "Available datasets", "Deployment considerations" ], "bibtex": "@Article{rs18040659,\nAUTHOR = {Caroppo, Andrea and Diraco, Giovanni and Leone, Alessandro},\nTITLE = {A Systematic Review of Available Multispectral UAV Image Datasets for Precision Agriculture Applications},\nJOURNAL = {Remote Sensing},\nVOLUME = {18},\nYEAR = {2026},\nNUMBER = {4},\nARTICLE-NUMBER = {659},\nURL = {https://www.mdpi.com/2072-4292/18/4/659},\nISSN = {2072-4292},\nABSTRACT = {The proliferation of Unmanned Aerial Vehicles (UAVs) equipped with multispectral imaging sensors has revolutionized data collection in precision agriculture. These platforms provide high-resolution, temporally dense data crucial for monitoring crop health, optimizing resource management, and predicting yield. However, the development and validation of robust data-driven algorithms, from vegetation index analysis to complex deep learning models, are contingent upon the availability of high-quality, standardized, and publicly accessible datasets. This review systematically surveys and characterizes the current landscape of available datasets containing multispectral imagery acquired by UAVs in agricultural contexts. Following guidelines for reporting systematic reviews and meta-analyses (PRISMA methodology), 39 studies were selected and analyzed, categorizing them based on key attributes including spectral bands (e.g., RGB, Red Edge, Near-Infrared), spatial and temporal resolution, types of crops studied, presence of complementary ground-truth data (e.g., biomass, nitrogen content, yield maps), and the specific agricultural tasks they support (e.g., disease detection, weed mapping, water stress assessment). However, the review underscores a critical gap in standardization, with significant variability in data formats, annotation quality, and metadata completeness, which hampers reproducibility and comparative analysis. Furthermore, we identify a need for more datasets targeting specific challenges like early-stage disease identification and anomaly detection in complex crop canopies. Finally, we discuss future directions for the creation of more comprehensive, benchmark-ready open datasets that will be instrumental in accelerating research, fostering collaboration, and bridging the gap between algorithmic innovation and practical agricultural deployment. This work serves as a foundational guide for researchers and practitioners seeking suitable data for their work and contributes to the ongoing effort of standardizing open data practices in agricultural remote sensing.},\nDOI = {10.3390/rs18040659}\n}" }, { "n": 68, "key": "BR2025111825", "title": "StarNet: Indian star gooseberries dataset for quality and maturity assessment", "authors": [ "Pushpa B. R.", "Manohar N.", "N. Shobha Rani" ], "authors_short": "R. et al.", "year": 2025, "venue": "Data in Brief", "doi": "10.1016/j.dib.2025.111825", "url": "https://doi.org/10.1016/j.dib.2025.111825", "division": "datasets", "also": [], "type": "Dataset", "crop": "Other crops", "platform": "Unspecified", "in_catalog": false, "note": "StarNet: Indian star gooseberry images for quality and maturity assessment. Cited as an adjacent-fruit dataset that can help generalise solutions.", "cited_in": [ "Available datasets" ], "bibtex": "@article{BR2025111825,\ntitle = {StarNet: Indian star gooseberries dataset for quality and maturity assessment},\njournal = {Data in Brief},\nvolume = {61},\npages = {111825},\nyear = {2025},\nissn = {2352-3409},\ndoi = {10.1016/j.dib.2025.111825},\nurl = {https://www.sciencedirect.com/science/article/pii/S2352340925005529},\nauthor = {Pushpa {B. R.} and Manohar N. and N. Shobha Rani},\nkeywords = {Star gooseberry fruit classification, Deep learning, Machine learning analysis, Quality assessment, Object detection}\n}" }, { "n": 69, "key": "UNNIKRISHNAN2026112545", "title": "Multimodal perishable fruits and vegetables dataset", "authors": [ "Devika Unnikrishnan", "Krishna Deepak", "Yogini Aishwaryaa P T S", "Bagyammal T" ], "authors_short": "Unnikrishnan et al.", "year": 2026, "venue": "Data in Brief", "doi": "10.1016/j.dib.2026.112545", "url": "https://doi.org/10.1016/j.dib.2026.112545", "division": "datasets", "also": [], "type": "Dataset", "crop": "Other crops", "platform": "Unspecified", "in_catalog": false, "note": "Multimodal dataset of perishable fruits and vegetables. Cited as an adjacent-fruit dataset that can help generalise solutions.", "cited_in": [ "Available datasets" ], "bibtex": "@article{UNNIKRISHNAN2026112545,\ntitle = {Multimodal perishable fruits and vegetables dataset},\njournal = {Data in Brief},\nvolume = {65},\npages = {112545},\nyear = {2026},\nissn = {2352-3409},\ndoi = {10.1016/j.dib.2026.112545},\nurl = {https://www.sciencedirect.com/science/article/pii/S2352340926000983},\nauthor = {Devika Unnikrishnan and Krishna Deepak and Yogini Aishwaryaa {P T S} and Bagyammal T},\nkeywords = {Fruits classification, Computer vision, Machine learning, Thermal image, Methane gas sensor, Shelf-life prediction, Spoilage detection, Sustainable agriculture, Agriculture 5.0, SDG 2, SDG 12, SDG 13}\n}" }, { "n": 70, "key": "AgriDataReuse", "title": "Data Reuse in Agricultural Genomics Research: Present Challenges and Future Solutions", "authors": [ "Alenka Hafner", "Victoria DeLeo", "Cecilia Deng", "Christine Elsik", "Damarius Fleming", "Peter Harrison", "Theodore Kalbfleisch", "Bruna Petry", "Boas Pucker", "Elsa Quezada-Rodríguez", "Christopher Tuggle", "James Koltes" ], "authors_short": "Hafner et al.", "year": 2025, "venue": "Gigascience", "doi": "10.20944/preprints202401.0780.v1", "url": "https://doi.org/10.20944/preprints202401.0780.v1", "division": "deployment", "also": [ "datasets" ], "type": "Review", "crop": "Agriculture in general", "platform": "Not platform-specific", "in_catalog": false, "note": "Challenges and solutions for reusing data in agricultural genomics. Cited on the risks of dataset reuse.", "cited_in": [ "Deployment considerations" ], "bibtex": "@article{AgriDataReuse,\nauthor = {Hafner, Alenka and DeLeo, Victoria and Deng, Cecilia and Elsik, Christine and Fleming, Damarius and Harrison, Peter and Kalbfleisch, Theodore and Petry, Bruna and Pucker, Boas and Quezada-Rodríguez, Elsa and Tuggle, Christopher and Koltes, James},\nyear = {2025},\nvolume = {14},\njournal = {Gigascience},\ntitle = {Data Reuse in Agricultural Genomics Research: Present Challenges and Future Solutions},\ndoi = {10.20944/preprints202401.0780.v1}\n}" }, { "n": 71, "key": "DataReuse", "title": "The reuse of public datasets in the life sciences: potential risks and rewards", "authors": [ "Katharina Sielemann", "Alenka Hafner", "Boas Pucker" ], "authors_short": "Sielemann et al.", "year": 2020, "venue": "PeerJ", "doi": "10.7717/peerj.9954", "url": "https://doi.org/10.7717/peerj.9954", "division": "deployment", "also": [ "datasets" ], "type": "Review", "crop": "Not crop-specific", "platform": "Not platform-specific", "in_catalog": false, "note": "Risks and rewards of reusing public datasets in the life sciences.", "cited_in": [ "Deployment considerations" ], "bibtex": "@article{DataReuse,\nauthor = {Sielemann, Katharina and Hafner, Alenka and Pucker, Boas},\nyear = {2020},\nmonth = {09},\npages = {e9954},\ntitle = {The reuse of public datasets in the life sciences: potential risks and rewards},\nvolume = {8},\njournal = {PeerJ},\ndoi = {10.7717/peerj.9954}\n}" }, { "n": 72, "key": "DLinAgri", "title": "Deep Learning in Agriculture: A Survey", "authors": [ "Andreas Kamilaris", "Francesc Prenafeta Boldú" ], "authors_short": "Kamilaris and Prenafeta Boldú", "year": 2018, "venue": "Computers and Electronics in Agriculture", "doi": "10.1016/j.compag.2018.02.016", "url": "https://doi.org/10.1016/j.compag.2018.02.016", "division": "perception", "also": [], "type": "Review", "crop": "Agriculture in general", "platform": "Not platform-specific", "in_catalog": false, "note": "Survey of deep learning in agriculture. Cited on AI needing more data than classical image processing while coping better with lower image quality.", "cited_in": [ "Deployment considerations" ], "bibtex": "@article{DLinAgri,\nauthor = {Kamilaris, Andreas and Prenafeta Boldú, Francesc},\nyear = {2018},\nmonth = {04},\npages = {},\ntitle = {Deep Learning in Agriculture: A Survey},\nvolume = {147},\njournal = {Computers and Electronics in Agriculture},\ndoi = {10.1016/j.compag.2018.02.016}\n}" }, { "n": 73, "key": "agriculture12111773", "title": "Influencing the Success of Precision Farming Technology Adoption—A Model-Based Investigation of Economic Success Factors in Small-Scale Agriculture", "authors": [ "Johannes Munz", "Heinrich Schuele" ], "authors_short": "Munz and Schuele", "year": 2022, "venue": "Agriculture", "doi": "10.3390/agriculture12111773", "url": "https://doi.org/10.3390/agriculture12111773", "division": "deployment", "also": [], "type": "Economics or adoption", "crop": "Agriculture in general", "platform": "Not platform-specific", "in_catalog": false, "note": "Model-based study of the economic success factors for adopting precision farming on small farms.", "cited_in": [ "Deployment considerations" ], "bibtex": "@Article{agriculture12111773,\nAUTHOR = {Munz, Johannes and Schuele, Heinrich},\nTITLE = {Influencing the Success of Precision Farming Technology Adoption—A Model-Based Investigation of Economic Success Factors in Small-Scale Agriculture},\nJOURNAL = {Agriculture},\nVOLUME = {12},\nYEAR = {2022},\nNUMBER = {11},\nARTICLE-NUMBER = {1773},\nURL = {https://www.mdpi.com/2077-0472/12/11/1773},\nISSN = {2077-0472},\nABSTRACT = {Even more than 30 years after the introduction of precision farming technologies and studies of their benefits in terms of productivity gains and environmental improvements, adoption rates, especially for variable-rate technologies, are very low. In particular, in smallholder areas, farm managers are reluctant to adopt these technologies. Therefore, this study identifies factors that hinder or facilitate adoption from an economic perspective. Using a model-based sensitivity analysis with three farms of different sizes (11 ha, 57 ha and 303 ha), it is shown that larger farms have higher resilience to external factors due to economies of scale. In addition, it is clarified that the certainty of obtaining additional benefits with GPS guidance systems can explain the higher adoption rates in farming practice, although the additional benefits (per hectare and year) are much lower for this technology than for variable-rate technologies. Small farms (>30 ha) are by no means excluded from the use of digital technologies, as it is shown that the influence of learning costs on profitability is very low, low subsidies can lead to a drastic reduction in the minimum farm size and the presence of low-cost technologies is an efficient solution which allows small farms to participate in the digital transformation of agriculture.},\nDOI = {10.3390/agriculture12111773}\n}" }, { "n": 74, "key": "Matese:2015", "title": "Technology in precision viticulture: A state of the art review", "authors": [ "Alessandro Matese", "Salvatore Di Gennaro" ], "authors_short": "Matese and Di Gennaro", "year": 2015, "venue": "International Journal of Wine Research", "doi": "10.2147/IJWR.S69405", "url": "https://doi.org/10.2147/IJWR.S69405", "division": "monitoring", "also": [ "deployment" ], "type": "Review", "crop": "Grapevine", "platform": "Several platforms", "in_catalog": false, "note": "State-of-the-art review of technology in precision viticulture. Cited on the efficiency benefits reported in the literature.", "cited_in": [ "Deployment considerations" ], "bibtex": "@article{Matese:2015,\nauthor = {Matese, Alessandro and Di Gennaro, Salvatore},\nyear = {2015},\nmonth = {05},\npages = {},\ntitle = {Technology in precision viticulture: A state of the art review},\nvolume = {7},\njournal = {International Journal of Wine Research},\ndoi = {10.2147/IJWR.S69405}\n}" }, { "n": 75, "key": "horticulturae9030399", "title": "Technologies and Innovative Methods for Precision Viticulture: A Comprehensive Review", "authors": [ "Massimo Vincenzo Ferro", "Pietro Catania" ], "authors_short": "Ferro and Catania", "year": 2023, "venue": "Horticulturae", "doi": "10.3390/horticulturae9030399", "url": "https://doi.org/10.3390/horticulturae9030399", "division": "monitoring", "also": [ "uav" ], "type": "Review", "crop": "Grapevine", "platform": "Several platforms", "in_catalog": false, "note": "Comprehensive review of technologies for precision viticulture. Cited on choosing between drones and robots.", "cited_in": [ "Deployment considerations" ], "bibtex": "@Article{horticulturae9030399,\nAUTHOR = {Ferro, Massimo Vincenzo and Catania, Pietro},\nTITLE = {Technologies and Innovative Methods for Precision Viticulture: A Comprehensive Review},\nJOURNAL = {Horticulturae},\nVOLUME = {9},\nYEAR = {2023},\nNUMBER = {3},\nARTICLE-NUMBER = {399},\nURL = {https://www.mdpi.com/2311-7524/9/3/399},\nISSN = {2311-7524},\nABSTRACT = {The potential of precision viticulture has been highlighted since the first studies performed in the context of viticulture, but especially in the last decade there have been excellent results have been achieved in terms of innovation and simple application. The deployment of new sensors for vineyard monitoring is set to increase in the coming years, enabling large amounts of information to be obtained. However, the large number of sensors developed and the great amount of data that can be collected are not always easy to manage, as it requires cross-sectoral expertise. The preliminary section of the review presents the scenario of precision viticulture, highlighting its potential and possible applications. This review illustrates the types of sensors and their operating principles. Remote platforms such as satellites, unmanned aerial vehicles (UAV) and proximal platforms are also presented. Some supervised and unsupervised algorithms used for object-based image segmentation and classification (OBIA) are then discussed, as well as a description of some vegetation indices (VI) used in viticulture. Photogrammetric algorithms for 3D canopy modelling using dense point clouds are illustrated. Finally, some machine learning and deep learning algorithms are illustrated for processing and interpreting big data to understand the vineyard agronomic and physiological status. This review shows that to perform accurate vineyard surveys and evaluations, it is important to select the appropriate sensor or platform, so the algorithms used in post-processing depend on the type of data collected. Several aspects discussed are fundamental to the understanding and implementation of vineyard variability monitoring techniques. However, it is evident that in the future, artificial intelligence and new equipment will become increasingly relevant for the detection and management of spatial variability through an autonomous approach.},\nDOI = {10.3390/horticulturae9030399}\n}" }, { "n": 76, "key": "BECHAR201694", "title": "Agricultural robots for field operations: Concepts and components", "authors": [ "Avital Bechar", "Clément Vigneault" ], "authors_short": "Bechar and Vigneault", "year": 2016, "venue": "Biosystems Engineering", "doi": "10.1016/j.biosystemseng.2016.06.014", "url": "https://doi.org/10.1016/j.biosystemseng.2016.06.014", "division": "control", "also": [], "type": "Review", "crop": "Agriculture in general", "platform": "Ground robot or vehicle", "in_catalog": false, "note": "Concepts and components of agricultural robots for field operations. Cited on when robots suit plant-level work such as pruning or spraying.", "cited_in": [ "Deployment considerations" ], "bibtex": "@article{BECHAR201694,\ntitle = {Agricultural robots for field operations: Concepts and components},\njournal = {Biosystems Engineering},\nvolume = {149},\npages = {94-111},\nyear = {2016},\nissn = {1537-5110},\ndoi = {10.1016/j.biosystemseng.2016.06.014},\nurl = {https://www.sciencedirect.com/science/article/pii/S1537511015301914},\nauthor = {Avital Bechar and Clément Vigneault},\nkeywords = {Agricultural robots, Robotics, Field operations, Autonomous},\nabstract = {This review investigates the research effort, developments and innovation in agricultural robots for field operations, and the associated concepts, principles, limitations and gaps. Robots are highly complex, consisting of different sub-systems that need to be integrated and correctly synchronised to perform tasks perfectly as a whole and successfully transfer the required information. Extensive research has been conducted on the application of robots and automation to a variety of field operations, and technical feasibility has been widely demonstrated. Agricultural robots for field operations must be able to operate in unstructured agricultural environments with the same quality of work achieved by current methods and means. To assimilate robotic systems, technologies must be developed to overcome continuously changing conditions and variability in produce and environments. Intelligent systems are needed for successful task performance in such environments. The robotic system must be cost-effective, while being inherently safe and reliable—human safety, and preservation of the environment, the crop and the machinery are mandatory. Despite much progress in recent years, in most cases the technology is not yet commercially available. Information-acquisition systems, including sensors, fusion algorithms and data analysis, need to be adjusted to the dynamic conditions of unstructured agricultural environments. Intensive research is needed on integrating human operators into the system control loop for increased system performance and reliability. System sizes should be reduced while improving the integration of all parts and components. For robots to perform in agricultural environments and execute agricultural tasks, research must focus on: fusing complementary sensors for adequate localisation and sensing abilities, developing simple manipulators for each agricultural task, developing path planning, navigation and guidance algorithms suited to environments besides open fields and known a-priori, and integrating human operators in this complex and highly dynamic situation.}\n}" }, { "n": 77, "key": "s22082961", "title": "Heterogeneous Autonomous Robotic System in Viticulture and Mariculture: Vehicles Development and Systems Integration", "authors": [ "Nadir Kapetanović", "Jurica Goričanec", "Ivo Vatavuk", "Ivan Hrabar", "Dario Stuhne", "Goran Vasiljević", "Zdenko Kovačić", "Nikola Mišković", "Nenad Antolović", "Marina Anić", "Bernard Kozina" ], "authors_short": "Kapetanović et al.", "year": 2022, "venue": "Sensors", "doi": "10.3390/s22082961", "url": "https://doi.org/10.3390/s22082961", "division": "control", "also": [ "uav" ], "type": "System", "crop": "Grapevine", "platform": "Several platforms", "in_catalog": false, "note": "Development and integration of heterogeneous autonomous vehicles for viticulture and mariculture.", "cited_in": [ "Deployment considerations" ], "bibtex": "@Article{s22082961,\nAUTHOR = {Kapetanović, Nadir and Goričanec, Jurica and Vatavuk, Ivo and Hrabar, Ivan and Stuhne, Dario and Vasiljević, Goran and Kovačić, Zdenko and Mišković, Nikola and Antolović, Nenad and Anić, Marina and Kozina, Bernard},\nTITLE = {Heterogeneous Autonomous Robotic System in Viticulture and Mariculture: Vehicles Development and Systems Integration},\nJOURNAL = {Sensors},\nVOLUME = {22},\nYEAR = {2022},\nNUMBER = {8},\nARTICLE-NUMBER = {2961},\nURL = {https://www.mdpi.com/1424-8220/22/8/2961},\nPubMedID = {35458946},\nISSN = {1424-8220},\nABSTRACT = {There are activities in viticulture and mariculture that require extreme physical endurance from human workers, making them prime candidates for automation and robotization. This paper presents a novel, practical, heterogeneous, autonomous robotic system divided into two main parts, each dealing with respective scenarios in viticulture and mariculture. The robotic components and the subsystems that enable collaboration were developed as part of the ongoing HEKTOR project, and each specific scenario is presented. In viticulture, this includes vineyard surveillance, spraying and suckering with an all-terrain mobile manipulator (ATMM) and a lightweight autonomous aerial robot (LAAR) that can be used in very steep vineyards where other mechanization fails. In mariculture, scenarios include coordinated aerial and subsurface monitoring of fish net pens using the LAAR, an autonomous surface vehicle (ASV), and a remotely operated underwater vehicle (ROV). All robotic components communicate and coordinate their actions through the Robot Operating System (ROS). Field tests demonstrate the great capabilities of the HEKTOR system for the fully autonomous execution of very strenuous and hazardous work in viticulture and mariculture, while meeting the necessary conditions for the required quality and quantity of the work performed.},\nDOI = {10.3390/s22082961}\n}" } ] }