{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T06:48:18Z","timestamp":1775198898345,"version":"3.50.1"},"reference-count":198,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,21]],"date-time":"2024-12-21T00:00:00Z","timestamp":1734739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Vine&amp;Wine Portugal Project","award":["C644866286-00000011"],"award-info":[{"award-number":["C644866286-00000011"]}]},{"name":"RRP\u2014Recovery and Resilience Plan and the European NextGeneration EU Funds","award":["C644866286-00000011"],"award-info":[{"award-number":["C644866286-00000011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Grapevines (Vitis vinifera L.) are one of the most economically relevant crops worldwide, yet they are highly vulnerable to various diseases, causing substantial economic losses for winegrowers. This systematic review evaluates the application of remote sensing and proximal tools for vineyard disease detection, addressing current capabilities, gaps, and future directions in sensor-based field monitoring of grapevine diseases. The review covers 104 studies published between 2008 and October 2024, identified through searches in Scopus and Web of Science, conducted on 25 January 2024, and updated on 10 October 2024. The included studies focused exclusively on the sensor-based detection of grapevine diseases, while excluded studies were not related to grapevine diseases, did not use remote or proximal sensing, or were not conducted in field conditions. The most studied diseases include downy mildew, powdery mildew, Flavescence dor\u00e9e, esca complex, rots, and viral diseases. The main sensors identified for disease detection are RGB, multispectral, hyperspectral sensors, and field spectroscopy. A trend identified in recent published research is the integration of artificial intelligence techniques, such as machine learning and deep learning, to improve disease detection accuracy. The results demonstrate progress in sensor-based disease monitoring, with most studies concentrating on specific diseases, sensor platforms, or methodological improvements. Future research should focus on standardizing methodologies, integrating multi-sensor data, and validating approaches across diverse vineyard contexts to improve commercial applicability and sustainability, addressing both economic and environmental challenges.<\/jats:p>","DOI":"10.3390\/s24248172","type":"journal-article","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T10:58:55Z","timestamp":1734951535000},"page":"8172","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["A Systematic Review on the Advancements in Remote Sensing and Proximity Tools for Grapevine Disease Detection"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3641-5537","authenticated-orcid":false,"given":"Fernando","family":"Portela","sequence":"first","affiliation":[{"name":"Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"proMetheus\u2014Research Unit in Materials, Energy and Environment for Sustainability, Escola Superior Agr\u00e1ria, Instituto Polit\u00e9cnico de Viana do Castelo, 4900-347 Viana do Castelo, Portugal"},{"name":"Agronomy Department, School of Agrarian and Veterinary Sciences, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4533-930X","authenticated-orcid":false,"given":"Joaquim J.","family":"Sousa","sequence":"additional","affiliation":[{"name":"School of Science and Technology, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"Centre for Robotics in Industry and Intelligent Systems (CRIIS), INESC Technology and Science (INESC-TEC), 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4933-8622","authenticated-orcid":false,"given":"Cl\u00e1udio","family":"Ara\u00fajo-Paredes","sequence":"additional","affiliation":[{"name":"proMetheus\u2014Research Unit in Materials, Energy and Environment for Sustainability, Escola Superior Agr\u00e1ria, Instituto Polit\u00e9cnico de Viana do Castelo, 4900-347 Viana do Castelo, Portugal"},{"name":"CISAS\u2014Center for Research and Development in Agrifood Systems and Sustainability, Instituto Polit\u00e9cnico de Viana do Castelo, 4900-347 Viana do Castelo, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5669-7976","authenticated-orcid":false,"given":"Emanuel","family":"Peres","sequence":"additional","affiliation":[{"name":"Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"School of Science and Technology, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2440-9153","authenticated-orcid":false,"given":"Raul","family":"Morais","sequence":"additional","affiliation":[{"name":"Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"School of Science and Technology, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7570-9773","authenticated-orcid":false,"given":"Lu\u00eds","family":"P\u00e1dua","sequence":"additional","affiliation":[{"name":"Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"School of Science and Technology, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"Institute for Innovation, Capacity Building and Sustainability of Agri-Food Production, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,21]]},"reference":[{"key":"ref_1","unstructured":"OIV (2024, March 12). Database|OIV. Available online: https:\/\/www.oiv.int\/what-we-do\/data-discovery-report?oiv."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sun, Q., Granco, G., Groves, L., Voong, J., and Van Zyl, S. (2023). Viticultural Manipulation and New Technologies to Address Environmental Challenges Caused by Climate Change. Climate, 11.","DOI":"10.3390\/cli11040083"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Cohen, B., Edan, Y., Levi, A., and Alchanatis, V. (2022). Early Detection of Grapevine (Vitis vinifera) Downy Mildew (Peronospora) and Diurnal Variations Using Thermal Imaging. Sensors, 22.","DOI":"10.3390\/s22093585"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1815","DOI":"10.1111\/ppa.13634","article-title":"Summary of the Worldwide Available Crop Disease Risk Simulation Studies That Were Driven by Climate Change Scenarios and Published during the Past 20 Years","volume":"71","author":"Juroszek","year":"2022","journal-title":"Plant Pathol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Frem, M., Petrontino, A., Fucilli, V., Sansiviero, C., and Bozzo, F. (2023). Sustainable Viticulture of Italian Grapevines: Environmental Evaluation and Societal Cost Estimation Using EU Farm Accountancy Data Network Data. Horticulturae, 9.","DOI":"10.3390\/horticulturae9111239"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1017\/jwe.2015.21","article-title":"The Impact of Climate Change on Viticulture and Wine Quality","volume":"11","author":"Darriet","year":"2016","journal-title":"J. Wine Econ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1080\/09571264.2010.530091","article-title":"Climate Change, Viticulture, and Wine: Challenges and Opportunities","volume":"21","author":"Jones","year":"2010","journal-title":"J. Wine Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3814","DOI":"10.1111\/gcb.13406","article-title":"Climate Change Impacts and Adaptive Strategies: Lessons from the Grapevine","volume":"22","author":"Mosedale","year":"2016","journal-title":"Glob. Change Biol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1844","DOI":"10.1016\/j.foodres.2010.05.001","article-title":"Climate Change Associated Effects on Grape and Wine Quality and Production","volume":"43","year":"2010","journal-title":"Food Res. Int."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6907","DOI":"10.1073\/pnas.1210127110","article-title":"Climate Change, Wine, and Conservation","volume":"110","author":"Hannah","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Van Leeuwen, C., Destrac-Irvine, A., Dubernet, M., Duch\u00eane, E., Gowdy, M., Marguerit, E., Pieri, P., Parker, A., de Ress\u00e9guier, L., and Ollat, N. (2019). An Update on the Impact of Climate Change in Viticulture and Potential Adaptations. Agronomy, 9.","DOI":"10.3390\/agronomy9090514"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.wep.2014.08.001","article-title":"The Impact of Climate Change on the Global Wine Industry: Challenges & Solutions","volume":"3","author":"Mozell","year":"2014","journal-title":"Wine Econ. Policy"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mir\u00e1s-Avalos, J.M., and Araujo, E.S. (2021). Optimization of Vineyard Water Management: Challenges, Strategies, and Perspectives. Water, 13.","DOI":"10.3390\/w13060746"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7","DOI":"10.17660\/ActaHortic.2019.1248.2","article-title":"Impact of Grapevine Breeding for Disease Resistance on the Global Wine Industry","volume":"1248","author":"Bavaresco","year":"2019","journal-title":"Acta Hortic."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Meng, B., Martelli, G.P., Golino, D.A., and Fuchs, M. (2017). The Effects of Viruses and Viral Diseases on Grapes and Wine. Grapevine Viruses: Molecular Biology, Diagnostics and Management, Springer International Publishing.","DOI":"10.1007\/978-3-319-57706-7"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/S0269-7491(99)00210-9","article-title":"Climate Change: Potential Impact on Plant Diseases","volume":"108","author":"Chakraborty","year":"2000","journal-title":"Environ. Pollut."},{"key":"ref_17","unstructured":"Reynolds, A.G. (2022). 13\u2014Fungal Contaminants in the Vineyard and Wine Quality and Safety. Managing Wine Quality, Woodhead Publishing. [2nd ed.]."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Griggs, R.G., Steenwerth, K.L., Mills, D.A., Cantu, D., and Bokulich, N.A. (2021). Sources and Assembly of Microbial Communities in Vineyards as a Functional Component of Winegrowing. Front. Microbiol., 12.","DOI":"10.3389\/fmicb.2021.673810"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1094\/PDIS-08-17-1181-FE","article-title":"Grapevine Trunk Diseases: A Review of Fifteen Years of Trials for Their Control with Chemicals and Biocontrol Agents","volume":"102","author":"Mondello","year":"2018","journal-title":"Plant Dis."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13593-014-0246-1","article-title":"Advanced Methods of Plant Disease Detection. A Review","volume":"35","author":"Martinelli","year":"2015","journal-title":"Agron. Sustain. Dev."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"537","DOI":"10.3390\/bios5030537","article-title":"Current and Prospective Methods for Plant Disease Detection","volume":"5","author":"Fang","year":"2015","journal-title":"Biosensors"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s40858-021-00439-z","article-title":"Plant Disease Severity Estimated Visually: A Century of Research, Best Practices, and Opportunities for Improving Methods and Practices to Maximize Accuracy","volume":"47","author":"Bock","year":"2022","journal-title":"Trop. Plant Pathol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"105143","DOI":"10.1016\/j.cropro.2020.105143","article-title":"Detection of Multiple Grapevine Viruses in New England Vineyards","volume":"132","author":"Borges","year":"2020","journal-title":"Crop Prot."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1007\/s00705-016-2776-0","article-title":"High-Throughput-Sequencing-Based Identification of a Grapevine Fanleaf Virus Satellite RNA in Vitis vinifera","volume":"161","author":"Chiumenti","year":"2016","journal-title":"Arch. Virol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1002\/ps.7733","article-title":"Next-Generation Methods for Early Disease Detection in Crops","volume":"80","author":"Trippa","year":"2024","journal-title":"Pest Manag. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Molitor, D., Baus, O., Hoffmann, L., and Beyer, M. (2016). Meteorological Conditions Determine the Thermaltemporal Position of the Annual Botrytis Bunch Rot Epidemic on Vitis vinifera L. Cv. Riesling Grapes. OENO One, 50.","DOI":"10.20870\/oeno-one.2016.50.3.36"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zherdev, A.V., Vinogradova, S.V., Byzova, N.A., Porotikova, E.V., Kamionskaya, A.M., and Dzantiev, B.B. (2018). Methods for the Diagnosis of Grapevine Viral Infections: A Review. Agriculture, 8.","DOI":"10.3390\/agriculture8120195"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5680","DOI":"10.1080\/01431161.2021.1929542","article-title":"Spectral Characterization of Fungal Diseases Downy Mildew, Powdery Mildew, Black-Foot and Petri Disease on Vitis vinifera Leaves","volume":"42","author":"Pithan","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"V\u00e9lez, S., Barajas, E., Rubio, J.A., Pereira-Obaya, D., and Rodr\u00edguez-P\u00e9rez, J.R. (2024). Field-Deployed Spectroscopy from 350 to 2500 Nm: A Promising Technique for Early Identification of Powdery Mildew Disease (Erysiphe Necator) in Vineyards. Agronomy, 14.","DOI":"10.3390\/agronomy14030634"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ganeva, D., Filchev, L., Roumenina, E., Dragov, R., Nedyalkova, S., and Bozhanova, V. (2024). Winter Durum Wheat Disease Severity Detection with Field Spectroscopy in Phenotyping Experiment at Leaf and Canopy Level. Remote Sens., 16.","DOI":"10.3390\/rs16101762"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Nguyen, C., Sagan, V., Maimaitiyiming, M., Maimaitijiang, M., Bhadra, S., and Kwasniewski, M.T. (2021). Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning. Sensors, 21.","DOI":"10.3390\/s21030742"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Rubambiza, G., Romero Galvan, F., Pavlick, R., Weatherspoon, H., and Gold, K.M. (2023). Toward Cloud-Native, Machine Learning Base Detection of Crop Disease with Imaging Spectroscopy. J. Geophys. Res. Biogeosci., 128.","DOI":"10.1029\/2022JG007342"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hruska, J., Adao, T., Padua, L., Marques, P., Peres, E., Sousa, A., Morais, R., and Sousa, J.J. (2018, January 22\u201327). Deep Learning-Based Methodological Approach for Vineyard Early Disease Detection Using Hyperspectral Data. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519136"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"6165","DOI":"10.3390\/s110606165","article-title":"A Wireless Sensor Network for Vineyard Monitoring That Uses Image Processing","volume":"11","author":"Lloret","year":"2011","journal-title":"Sensors"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.compag.2018.10.006","article-title":"Deep Leaning Approach with Colorimetric Spaces and Vegetation Indices for Vine Diseases Detection in UAV Images","volume":"155","author":"Kerkech","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_36","unstructured":"Aruna, M.G., Silvia, E., Al-Fatlawy, R.R., Rao, H.K., and Sowmya, M. (2024, January 15\u201316). Vine Disease Detection UAV Multi Spectral Image Using Segnet and Mobilenet Method. Proceedings of the 2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT), Bengaluru, India."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hnatiuc, M., Ghita, S., Alpetri, D., Ranca, A., Artem, V., Dina, I., Cosma, M., and Abed Mohammed, M. (2023). Intelligent Grapevine Disease Detection Using IoT Sensor Network. Bioengineering, 10.","DOI":"10.3390\/bioengineering10091021"},{"key":"ref_38","first-page":"49","article-title":"Diagnosis of Grapevine Leafroll Disease Severity Infection via UAV Remote Sensing and Deep Learning","volume":"5","author":"Liu","year":"2023","journal-title":"Smart Agric."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Barjaktarovic, M., Santoni, M., Faralli, M., Bertamini, M., and Bruzzone, L. (2022, January 15\u201316). A Multispectral Acquisition System for Potential Detection of Flavescence Dor\u00e9e. Proceedings of the 2022 30th Telecommunications Forum (TELFOR), Belgrade, Serbia.","DOI":"10.1109\/TELFOR56187.2022.9983685"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Liu, E., Gold, K.M., Combs, D., Cadle-Davidson, L., and Jiang, Y. (2021, January 12\u201316). Deep Learning-Based Autonomous Downy Mildew Detection and Severity Estimation in Vineyards. Proceedings of the 2021 ASABE Annual International Virtual Meeting, Online.","DOI":"10.13031\/aim.202100486"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1071\/FP07204","article-title":"Exploring the Sensitivity of Thermal Imaging for Plasmopara Viticola Pathogen Detection in Grapevines under Different Water Status","volume":"35","author":"Stoll","year":"2008","journal-title":"Funct. Plant Biol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1166\/sl.2013.2864","article-title":"Segmentation of Grapevine Leafroll Disease Characteristic Based on Multi-Spectral Image","volume":"11","author":"Sun","year":"2013","journal-title":"Sens. Lett."},{"key":"ref_43","unstructured":"Lamine, S., Srivastava, P.K., Kayad, A., Mu\u00f1oz-Arriola, F., and Pandey, P.C. (2024). Chapter 18\u2014Detection of Grapevine Yellows Using Multispectral Imaging. Remote Sensing in Precision Agriculture, Academic Press. Earth Observation."},{"key":"ref_44","first-page":"73","article-title":"Detecting Red Blotch Disease in Grape Leaves Using Hyperspectral Imaging","volume":"Volume 9840","author":"Mehrubeoglu","year":"2016","journal-title":"Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1094\/PHYTO-02-23-0061-R","article-title":"Non-Destructive Monitoring of Foliar Fungicide Efficacy with Hyperspectral Sensing in Grapevine","volume":"114","author":"Gambhir","year":"2024","journal-title":"Phytopathology"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Sandika, B., Avil, S., Sanat, S., and Srinivasu, P. (2016, January 6\u201310). Random Forest Based Classification of Diseases in Grapes from Images Captured in Uncontrolled Environments. Proceedings of the 2016 IEEE 13th International Conference on Signal Processing (ICSP), Chengdu, China.","DOI":"10.1109\/ICSP.2016.7878133"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"n160","DOI":"10.1136\/bmj.n160","article-title":"PRISMA 2020 Explanation and Elaboration: Updated Guidance and Exemplars for Reporting Systematic Reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"100152","DOI":"10.1016\/j.srs.2024.100152","article-title":"Advancements in High-Resolution Land Surface Satellite Products: A Comprehensive Review of Inversion Algorithms, Products and Challenges","volume":"10","author":"Liang","year":"2024","journal-title":"Sci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"100032","DOI":"10.1016\/j.srs.2021.100032","article-title":"Generating the 30-m Land Surface Temperature Product over Continental China and USA from Landsat 5\/7\/8 Data","volume":"4","author":"Cheng","year":"2021","journal-title":"Sci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"100161","DOI":"10.1016\/j.wasec.2023.100161","article-title":"Remote Sensing of Irrigation: Research Trends and the Direction to next-Generation Agriculture through Data-Driven Scientometric Analysis","volume":"21","author":"Manivasagam","year":"2024","journal-title":"Water Secur."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4011","DOI":"10.1002\/hyp.8408","article-title":"Satellite-Based ET Estimation in Agriculture Using SEBAL and METRIC","volume":"25","author":"Allen","year":"2011","journal-title":"Hydrol. Process."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Islam, M.M. (2024). Unravelling the Complexities of Wetland Agriculture, Climate Change, and Coping Mechanisms: An Integrative Review Using Economics and Satellite Approaches. Environ. Dev. Sustain.","DOI":"10.1007\/s10668-024-05152-w"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2971","DOI":"10.3390\/rs70302971","article-title":"Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture","volume":"7","author":"Matese","year":"2015","journal-title":"Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Marino, S., and Alvino, A. (2019). Detection of Spatial and Temporal Variability of Wheat Cultivars by High-Resolution Vegetation Indices. Agronomy, 9.","DOI":"10.3390\/agronomy9050226"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Hassler, S.C., and Baysal-Gurel, F. (2019). Unmanned Aircraft System (UAS) Technology and Applications in Agriculture. Agronomy, 9.","DOI":"10.3390\/agronomy9100618"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Valasek, J., Lu, H.-H., and Shi, Y. (2017, January 13\u201316). Development and Testing of a Customized Low-Cost Unmanned Aircraft System Based on Multispectral and Thermal Sensing for Precision Agriculture Applications. Proceedings of the 2017 International Conference on Unmanned Aircraft Systems (ICUAS), Miami, FL, USA.","DOI":"10.1109\/ICUAS.2017.7991494"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1439","DOI":"10.1094\/PHYTO-01-23-0030-R","article-title":"Scalable Early Detection of Grapevine Viral Infection with Airborne Imaging Spectroscopy","volume":"113","author":"Galvan","year":"2023","journal-title":"Phytopathology"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Kang, K.K.-K., Hoekstra, M., Foroutan, M., Chegoonian, A.M., Zolfaghari, K., and Duguay, C.R. (August, January 28). Operating Procedures and Calibration of a Hyperspectral Sensor Onboard a Remotely Piloted Aircraft System for Water and Agriculture Monitoring. Proceedings of the IGARSS 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8900128"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s11119-012-9274-5","article-title":"The Application of Small Unmanned Aerial Systems for Precision Agriculture: A Review","volume":"13","author":"Zhang","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Tsouros, D.C., Bibi, S., and Sarigiannidis, P.G. (2019). A Review on UAV-Based Applications for Precision Agriculture. Information, 10.","DOI":"10.3390\/info10110349"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Velusamy, P., Rajendran, S., Mahendran, R.K., Naseer, S., Shafiq, M., and Choi, J.-G. (2022). Unmanned Aerial Vehicles (UAV) in Precision Agriculture: Applications and Challenges. Energies, 15.","DOI":"10.3390\/en15010217"},{"key":"ref_63","first-page":"262","article-title":"Unmanned Aerial Vehicle (UAV)-Based Remote Sensing to Monitor Grapevine Leaf Stripe Disease within a Vineyard Affected by Esca Complex","volume":"55","author":"Gennaro","year":"2016","journal-title":"Phytopathol. Mediterr."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Ara\u00fajo-Paredes, C., Portela, F., Mendes, S., and Val\u00edn, M.I. (2022). Using Aerial Thermal Imagery to Evaluate Water Status in Vitis vinifera Cv. Loureiro. Sensors, 22.","DOI":"10.3390\/s22208056"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Fern\u00e1ndez, M., Sanz-Ablanedo, E., and Rodr\u00edguez-P\u00e9rez, J.R. (2021). High-Resolution Drone-Acquired RGB Imagery to Estimate Spatial Grape Quality Variability. Agronomy, 11.","DOI":"10.3390\/agronomy11040655"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"7963","DOI":"10.1007\/s13369-022-06738-0","article-title":"Recent Advances in Unmanned Aerial Vehicles: A Review","volume":"47","author":"Ahmed","year":"2022","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Morales-Rodr\u00edguez, P.A., Cano Cano, E., Villena, J., and L\u00f3pez-Perales, J.A. (2022). A Comparison between Conventional Sprayers and New UAV Sprayers: A Study Case of Vineyards and Olives in Extremadura (Spain). Agronomy, 12.","DOI":"10.3390\/agronomy12061307"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2082","DOI":"10.1007\/s11119-024-10155-8","article-title":"Unmanned Aerial System Plant Protection Products Spraying Performance Evaluation on a Vineyard","volume":"25","author":"Sassu","year":"2024","journal-title":"Precis. Agric."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"139204","DOI":"10.1016\/j.scitotenv.2020.139204","article-title":"Unmanned Aerial Vehicles for Biodiversity-Friendly Agricultural Landscapes\u2014A Systematic Review","volume":"732","author":"Klaus","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Pascoal, D., Silva, N., Ad\u00e3o, T., Lopes, R.D., Peres, E., and Morais, R. (2024). A Technical Survey on Practical Applications and Guidelines for IoT Sensors in Precision Agriculture and Viticulture. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-80924-y"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Abdelghafour, F., Keresztes, B., Germain, C., and Da Costa, J.-P. (2020). In Field Detection of Downy Mildew Symptoms with Proximal Colour Imaging. Sensors, 20.","DOI":"10.3390\/s20164380"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Ammoniaci, M., Kartsiotis, S.-P., Perria, R., and Storchi, P. (2021). State of the Art of Monitoring Technologies and Data Processing for Precision Viticulture. Agriculture, 11.","DOI":"10.3390\/agriculture11030201"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Moses, J.C., Adibi, S., Wickramasinghe, N., Nguyen, L., Angelova, M., and Islam, S.M.S. (2022). Smartphone as a Disease Screening Tool: A Systematic Review. Sensors, 22.","DOI":"10.3390\/s22103787"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Sassu, A., Gambella, F., Ghiani, L., Mercenaro, L., Caria, M., and Pazzona, A.L. (2021). Advances in Unmanned Aerial System Remote Sensing for Precision Viticulture. Sensors, 21.","DOI":"10.3390\/s21030956"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"101426","DOI":"10.1016\/j.pmpp.2019.101426","article-title":"\u2018A.; Tuan Yusof, T.N.; Gomes, C. Non-Destructive Techniques of Detecting Plant Diseases: A Review","volume":"108","author":"Ali","year":"2019","journal-title":"Physiol. Mol. Plant Pathol."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Kior, A., Yudina, L., Zolin, Y., Sukhov, V., and Sukhova, E. (2024). RGB Imaging as a Tool for Remote Sensing of Characteristics of Terrestrial Plants: A Review. Plants, 13.","DOI":"10.3390\/plants13091262"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"4923","DOI":"10.1080\/01431161.2024.2368933","article-title":"A Systematic Review on the Application of UAV-Based Thermal Remote Sensing for Assessing and Monitoring Crop Water Status in Crop Farming Systems","volume":"45","author":"Ndlovu","year":"2024","journal-title":"Int. J. Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"279","DOI":"10.20870\/oeno-one.2020.54.2.2954","article-title":"Vigor Thresholded NDVI Is a Key Early Risk Indicator of Botrytis Bunch Rot in Vineyards","volume":"54","author":"Roudet","year":"2020","journal-title":"OENO One"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Giovos, R., Tassopoulos, D., Kalivas, D., Lougkos, N., and Priovolou, A. (2021). Remote Sensing Vegetation Indices in Viticulture: A Critical Review. Agriculture, 11.","DOI":"10.3390\/agriculture11050457"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"100005","DOI":"10.1016\/j.atech.2021.100005","article-title":"Smart Applications and Digital Technologies in Viticulture: A Review","volume":"1","author":"Tardaguila","year":"2021","journal-title":"Smart Agric. Technol."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Farooq, M.S., Riaz, S., Abid, A., Umer, T., and Zikria, Y.B. (2020). Role of IoT Technology in Agriculture: A Systematic Literature Review. Electronics, 9.","DOI":"10.3390\/electronics9020319"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1571","DOI":"10.1007\/s00484-020-01938-5","article-title":"BotRisk: Simulating the Annual Bunch Rot Risk on Grapevines (Vitis vinifera L. Cv. Riesling) Based on Meteorological Data","volume":"64","author":"Molitor","year":"2020","journal-title":"Int. J. Biometeorol."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Clippinger, J.I., Dobry, E.P., Laffan, I., Zorbas, N., Hed, B., and Campbell, M.A. (2024). Traditional and Emerging Approaches for Disease Management of Plasmopara Viticola, Causal Agent of Downy Mildew of Grape. Agriculture, 14.","DOI":"10.3390\/agriculture14030406"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Velasquez-Camacho, L., Otero, M., Basile, B., Pijuan, J., and Corrado, G. (2023). Current Trends and Perspectives on Predictive Models for Mildew Diseases in Vineyards. Microorganisms, 11.","DOI":"10.3390\/microorganisms11010073"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1111\/j.1365-3059.2011.02498.x","article-title":"A Nonlinear Model for Temperature-Dependent Development of Erysiphe Necator Chasmothecia on Grapevine Leaves","volume":"61","author":"Legler","year":"2012","journal-title":"Plant Pathol."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.1364-3703.2011.00728.x","article-title":"Grapevine Powdery Mildew (Erysiphe Necator): A Fascinating System for the Study of the Biology, Ecology and Epidemiology of an Obligate Biotroph","volume":"13","author":"Gadoury","year":"2012","journal-title":"Mol. Plant Pathol."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Perria, R., Ciofini, A., Petrucci, W.A., D\u2019Arcangelo, M.E.M., Valentini, P., Storchi, P., Carella, G., Pacetti, A., and Mugnai, L. (2022). A Study on the Efficiency of Sustainable Wine Grape Vineyard Management Strategies. Agronomy, 12.","DOI":"10.3390\/agronomy12020392"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"uhae182","DOI":"10.1093\/hr\/uhae182","article-title":"Grapevine Gray Mold Disease: Infection, Defense and Management","volume":"11","author":"Rahman","year":"2024","journal-title":"Hortic. Res."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Szab\u00f3, M., Csik\u00e1sz-Krizsics, A., Dula, T., Farkas, E., Roznik, D., Kozma, P., and De\u00e1k, T. (2023). Black Rot of Grapes (Guignardia Bidwellii)\u2014A Comprehensive Overview. Horticulturae, 9.","DOI":"10.3390\/horticulturae9020130"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Liu, R., Wang, Y., Li, P., Sun, L., Jiang, J., Fan, X., Liu, C., and Zhang, Y. (2021). Genome Assembly and Transcriptome Analysis of the Fungus Coniella Diplodiella During Infection on Grapevine (Vitis vinifera L.). Front. Microbiol., 11.","DOI":"10.3389\/fmicb.2020.599150"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Del Frari, G., Oliveira, H., and Boavida Ferreira, R. (2021). White Rot Fungi (Hymenochaetales) and Esca of Grapevine: Insights from Recent Microbiome Studies. J. Fungi, 7.","DOI":"10.3390\/jof7090770"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1094\/PHP-10-17-0057-MR","article-title":"Flavescence Dor\u00e9e and Bois Noir Diseases of Grapevine Are Evolving Pathosystems","volume":"19","author":"Tessitori","year":"2018","journal-title":"Plant Health Prog."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1007\/s13593-014-0208-7","article-title":"Biology and Ecology of the Flavescence Dor\u00e9e Vector Scaphoideus Titanus: A Review","volume":"34","author":"Chuche","year":"2014","journal-title":"Agron. Sustain. Dev."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Maree, H.J., Almeida, R.P., Bester, R., Chooi, K.M., Cohen, D., Dolja, V.V., Fuchs, M.F., Golino, D.A., Jooste, A.E., and Martelli, G.P. (2013). Grapevine Leafroll-Associated Virus 3. Front. Microbiol., 4.","DOI":"10.3389\/fmicb.2013.00082"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"1495","DOI":"10.1007\/s00705-011-1071-3","article-title":"The Grapevine-Infecting Vitiviruses, with Particular Reference to Grapevine Virus A","volume":"156","author":"Stephan","year":"2011","journal-title":"Arch. Virol."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1007\/s42161-019-00484-3","article-title":"Believing Is Seeing: Lessons from Emerging Viruses in Grapevine","volume":"102","author":"Cieniewicz","year":"2020","journal-title":"J. Plant Pathol."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12518-013-0120-x","article-title":"UAV for 3D Mapping Applications: A Review","volume":"6","author":"Nex","year":"2014","journal-title":"Appl. Geomat."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Sousa, J.J., Toscano, P., Matese, A., Di Gennaro, S.F., Berton, A., Gatti, M., Poni, S., P\u00e1dua, L., Hru\u0161ka, J., and Morais, R. (2022). UAV-Based Hyperspectral Monitoring Using Push-Broom and Snapshot Sensors: A Multisite Assessment for Precision Viticulture Applications. Sensors, 22.","DOI":"10.3390\/s22176574"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Daniels, L., Eeckhout, E., Wieme, J., Dejaegher, Y., Audenaert, K., and Maes, W.H. (2023). Identifying the Optimal Radiometric Calibration Method for UAV-Based Multispectral Imaging. Remote Sens., 15.","DOI":"10.3390\/rs15112909"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"105695","DOI":"10.1016\/j.compag.2020.105695","article-title":"Real-Time Kinematics Applied at Unmanned Aerial Vehicles Positioning for Orthophotography in Precision Agriculture","volume":"177","author":"Swenson","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"2301","DOI":"10.1016\/j.procs.2024.06.422","article-title":"The Impact of Ground Control Points for the 3D Study of Grapevines in Steep Slope Vineyards","volume":"239","author":"Stolarski","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"167","DOI":"10.5194\/isprs-archives-XLII-4-W12-167-2019","article-title":"Optimization of Ground Control Point (GCP) Configuration for Unmanned Aerial Vehicle (UAV) Survey Using Structure from Motion (SFM)","volume":"XLII-4-W12","author":"Villanueva","year":"2019","journal-title":"Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1139\/cjfr-2020-0125","article-title":"Unmanned Aerial Vehicles (UAV)-Based Canopy Height Modeling under Leaf-on and Leaf-off Conditions for Determining Tree Height and Crown Diameter (Case Study: Hyrcanian Mixed Forest)","volume":"51","author":"Nasiri","year":"2021","journal-title":"Can. J. For. Res."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Zhang, T. (2024). A Comprehensive Review of Assessing Storm Surge Disasters: From Traditional Statistical Methods to Artificial Intelligence-Based Techniques. Atmosphere, 15.","DOI":"10.3390\/atmos15030359"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1093\/ije\/dyi153","article-title":"Regression Models for Twin Studies: A Critical Review","volume":"34","author":"Carlin","year":"2005","journal-title":"Int. J. Epidemiol."},{"key":"ref_106","first-page":"127","article-title":"A Review on Applied Multivariate Statistical Techniques in Agriculture and Plant Science","volume":"4","author":"Fasihfar","year":"2013","journal-title":"Int. J. Agron. Plant Prod."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1080\/00045608.2012.689236","article-title":"Principal Component Analysis on Spatial Data: An Overview","volume":"103","author":"Harris","year":"2013","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1007\/s13042-013-0226-9","article-title":"Linear Discriminant Analysis for the Small Sample Size Problem: An Overview","volume":"6","author":"Sharma","year":"2015","journal-title":"Int. J. Mach. Learn. Cyber."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"105688","DOI":"10.1016\/j.sab.2019.105688","article-title":"Critical Review and Advices on Spectral-Based Normalization Methods for LIBS Quantitative Analysis","volume":"160","author":"Guezenoc","year":"2019","journal-title":"Spectrochim. Acta Part B At. Spectrosc."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1002\/cem.2762","article-title":"The Diversity in the Applications of Partial Least Squares: An Overview","volume":"30","author":"Mehmood","year":"2016","journal-title":"J. Chemom."},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Sharma, R. (2021, January 6\u20138). Artificial Intelligence in Agriculture: A Review. Proceedings of the 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India.","DOI":"10.1109\/ICICCS51141.2021.9432187"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Ray, S. (2019, January 14\u201316). A Quick Review of Machine Learning Algorithms. Proceedings of the 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), Faridabad, India.","DOI":"10.1109\/COMITCon.2019.8862451"},{"key":"ref_113","unstructured":"Hemanth, J., Fernando, X., Lafata, P., and Baig, Z. (2018, January 7\u20138). A Review on Random Forest: An Ensemble Classifier. Proceedings of the International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI), Coimbatore, India."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1002\/widm.12","article-title":"Multivariate Random Forests","volume":"1","author":"Segal","year":"2011","journal-title":"WIREs Data Min. Knowl. Discov."},{"key":"ref_115","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"77308","DOI":"10.1109\/ACCESS.2020.2989052","article-title":"Visual Trunk Detection Using Transfer Learning and a Deep Learning-Based Coprocessor","volume":"8","author":"Aguiar","year":"2020","journal-title":"IEEE Access"},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Sharma, P., and Singh, A. (2017, January 3\u20135). Era of Deep Neural Networks: A Review. Proceedings of the 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Delhi, India.","DOI":"10.1109\/ICCCNT.2017.8203938"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.1016\/j.procs.2022.01.135","article-title":"A Review of Yolo Algorithm Developments","volume":"199","author":"Jiang","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Mezei, I., Luki\u0107, M., Berbakov, L., Pavkovi\u0107, B., and Radovanovi\u0107, B. (2022). Grapevine Downy Mildew Warning System Based on NB-IoT and Energy Harvesting Technology. Electronics, 11.","DOI":"10.3390\/electronics11030356"},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Marcu, I., Dr\u0103gulinescu, A.-M., Oprea, C., Suciu, G., and B\u0103l\u0103ceanu, C. (2022). Predictive Analysis and Wine-Grapes Disease Risk Assessment Based on Atmospheric Parameters and Precision Agriculture Platform. Sustainability, 14.","DOI":"10.3390\/su141811487"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Kleb, M., Merkt, N., and Z\u00f6rb, C. (2022). New Aspects of In Situ Measurements for Downy Mildew Forecasting. Plants, 11.","DOI":"10.3390\/plants11141807"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"012020","DOI":"10.1088\/1755-1315\/275\/1\/012020","article-title":"Influence of Sensor Calibration on Forecasting Models for Vineyard Disease Detection","volume":"275","author":"Sanna","year":"2019","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Cohen, B., Edan, Y., Levi, A., and Alchanatis, V. (2021). 33. Early Detection of Grapevine Downy Mildew Using Thermal Imaging. Precision Agriculture\u201921, Wageningen Academic Publishers.","DOI":"10.3920\/978-90-8686-916-9_33"},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Zia-Khan, S., Kleb, M., Merkt, N., Schock, S., and M\u00fcller, J. (2022). Application of Infrared Imaging for Early Detection of Downy Mildew (Plasmopara Viticola) in Grapevine. Agriculture, 12.","DOI":"10.3390\/agriculture12050617"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1007\/s11119-008-9084-y","article-title":"Early Pathogen Detection under Different Water Status and the Assessment of Spray Application in Vineyards through the Use of Thermal Imagery","volume":"9","author":"Stoll","year":"2008","journal-title":"Precis. Agric."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Liu, E., Gold, K.M., Combs, D., Cadle-Davidson, L., and Jiang, Y. (2022). Deep Semantic Segmentation for the Quantification of Grape Foliar Diseases in the Vineyard. Front. Plant Sci., 13.","DOI":"10.3389\/fpls.2022.978761"},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Poblete-Echeverr\u00eda, C., Hern\u00e1ndez, I., Guti\u00e9rrez, S., I\u00f1iguez, R., Barrio, I., and Tardaguila, J. (2023). Using Artificial Intelligence (AI) for Grapevine Disease Detection Based on Images. BIO Web Conf., 68.","DOI":"10.1051\/bioconf\/20236801021"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"105991","DOI":"10.1016\/j.compag.2021.105991","article-title":"Deep Learning for the Differentiation of Downy Mildew and Spider Mite in Grapevine under Field Conditions","volume":"182","author":"Ceballos","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Padol, P.B., and Yadav, A.A. (2016, January 9\u201311). SVM Classifier Based Grape Leaf Disease Detection. Proceedings of the 2016 Conference on Advances in Signal Processing (CASP), Pune, India.","DOI":"10.1109\/CASP.2016.7746160"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"1807","DOI":"10.1039\/c5pp00121h","article-title":"First Detection of the Presence of Naturally Occurring Grapevine Downy Mildew in the Field by a Fluorescence-Based Method","volume":"14","author":"Latouche","year":"2015","journal-title":"Photochem. Photobiol. Sci."},{"key":"ref_132","first-page":"17","article-title":"Construction of a Grape Quality Index from RGB Images of Crates","volume":"Volume 12749","author":"Lefevre","year":"2023","journal-title":"Proceedings of the Sixteenth International Conference on Quality Control by Artificial Vision"},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Yang, R., Lu, X., Huang, J., Zhou, J., Jiao, J., Liu, Y., Liu, F., Su, B., and Gu, P. (2021). A Multi-Source Data Fusion Decision-Making Method for Disease and Pest Detection of Grape Foliage Based on ShuffleNet V2. Remote Sens., 13.","DOI":"10.3390\/rs13245102"},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Kanaley, K., Combs, D.B., Paul, A., Jiang, Y., Bates, T., and Gold, K.M. (2023). Assessing the Capacity of High-Resolution Commercial Satellite Imagery for Grapevine Downy Mildew Detection and Surveillance in New York State. Phytopathology, The American Phytopathological Society.","DOI":"10.1101\/2023.11.10.566469"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"105446","DOI":"10.1016\/j.compag.2020.105446","article-title":"Vine Disease Detection in UAV Multispectral Images Using Optimized Image Registration and Deep Learning Segmentation Approach","volume":"174","author":"Kerkech","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Kerkech, M., Hafiane, A., and Canals, R. (2020). VddNet: Vine Disease Detection Network Based on Multispectral Images and Depth Map. Remote Sens., 12.","DOI":"10.3390\/rs12203305"},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"032020","DOI":"10.1088\/1755-1315\/981\/3\/032020","article-title":"Intelligent Complex of Monitoring and Diagnostics of Grape Plantations","volume":"981","author":"Kuznetsov","year":"2022","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Balaceanu, C., Streche, R., Roscaneanu, R., Osiac, F., Orza, O., Bosoc, S., and Suciu, G. (2022, January 17\u201319). Diseases Detection System Based on Machine Learning Algorithms and Internet of Things Technology Used in Viticulture. Proceedings of the 2022 E-Health and Bioengineering Conference (EHB), Iasi, Romania.","DOI":"10.1109\/EHB55594.2022.9991324"},{"key":"ref_139","unstructured":"Ro\u015fc\u0103neanu, R., Streche, R., Osiac, F., B\u0103l\u0103ceanu, C., Suciu, G., Dr\u0103gulinescu, A.M., and Marcu, I. (2022, January 18\u201320). Detection of Vineyard Diseases Using the Internet of Things Technology and Machine Learning Algorithms. Proceedings of the \u201c2022 Air and Water\u2014Components of the Environment\u201d Conference Proceedings, Cluj-Napoca, Romania."},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Ouhami, M., Es-saady, Y., Hajj, M.E., Canals, R., and Hafiane, A. (2022, January 17\u201319). Meteorological Data and UAV Images for the Detection and Identification of Grapevine Disease Using Deep Learning. Proceedings of the 2022 E-Health and Bioengineering Conference (EHB), Iasi, Romania.","DOI":"10.1109\/EHB55594.2022.9991443"},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Kontogiannis, S., Konstantinidou, M., Tsioukas, V., and Pikridas, C. (2024). A Cloud-Based Deep Learning Framework for Downy Mildew Detection in Viticulture Using Real-Time Image Acquisition from Embedded Devices and Drones. Information, 15.","DOI":"10.3390\/info15040178"},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"Patil, S.S., and Thorat, S.A. (2016, January 12\u201313). Early Detection of Grapes Diseases Using Machine Learning and IoT. Proceedings of the 2016 Second International Conference on Cognitive Computing and Information Processing (CCIP), Mysuru, India.","DOI":"10.1109\/CCIP.2016.7802887"},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"5","DOI":"10.3390\/iot1010002","article-title":"Towards a Low-Cost Precision Viticulture System Using Internet of Things Devices","volume":"1","author":"Spachos","year":"2020","journal-title":"IoT"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"24021","DOI":"10.1007\/s11042-022-12147-0","article-title":"Plant Leaf Disease Classification and Damage Detection System Using Deep Learning Models","volume":"81","author":"Neeraja","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"3038","DOI":"10.1109\/JSTARS.2023.3339297","article-title":"An Integrated Framework of Two-Stream Deep Learning Models Optimal Information Fusion for Fruits Disease Recognition","volume":"17","author":"Zahra","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"Huang, Z., Qin, A., Lu, J., Menon, A., and Gao, J. (2020, January 2\u20136). Grape Leaf Disease Detection and Classification Using Machine Learning. Proceedings of the 2020 International Conferences 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) and IEEE Congress on Cybermatics (Cybermatics), Rhodes, Greece.","DOI":"10.1109\/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00150"},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.compag.2018.01.009","article-title":"Deep Learning Models for Plant Disease Detection and Diagnosis","volume":"145","author":"Ferentinos","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Miranda, M., Zabawa, L., Kicherer, A., Strothmann, L., Rascher, U., and Roscher, R. (2022). Detection of Anomalous Grapevine Berries Using Variational Autoencoders. Front. Plant Sci., 13.","DOI":"10.3389\/fpls.2022.729097"},{"key":"ref_149","doi-asserted-by":"crossref","unstructured":"Strothmann, L., Rascher, U., and Roscher, R. (August, January 28). Detection of Anomalous Grapevine Berries Using All-Convolutional Autoencoders. Proceedings of the IGARSS 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898366"},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Rist, F., Gabriel, D., Mack, J., Steinhage, V., T\u00f6pfer, R., and Herzog, K. (2019). Combination of an Automated 3D Field Phenotyping Workflow and Predictive Modelling for High-Throughput and Non-Invasive Phenotyping of Grape Bunches. Remote Sens., 11.","DOI":"10.3390\/rs11242953"},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"101542","DOI":"10.1016\/j.softx.2023.101542","article-title":"BBR: An Open-Source Standard Workflow Based on Biophysical Crop Parameters for Automatic Botrytis Cinerea Assessment in Vineyards","volume":"24","author":"Valente","year":"2023","journal-title":"SoftwareX"},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"126691","DOI":"10.1016\/j.eja.2022.126691","article-title":"Mapping the Spatial Variability of Botrytis Bunch Rot Risk in Vineyards Using UAV Multispectral Imagery","volume":"142","author":"Valente","year":"2023","journal-title":"Eur. J. Agron."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"371","DOI":"10.20870\/oeno-one.2022.56.3.5460","article-title":"Two-Stage Automatic Diagnosis of Flavescence Dor\u00e9e Based on Proximal Imaging and Artificial Intelligence: A Multi-Year and Multi-Variety Experimental Study","volume":"56","author":"Tardif","year":"2022","journal-title":"OENO One"},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.5194\/isprs-archives-XLIII-B3-2020-1483-2020","article-title":"Uav Images and Deep-Learning Algorithms for Detecting Flavescence Doree Disease in Grapevine Orchards","volume":"XLIII-B3-2020","author":"Musci","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci."},{"key":"ref_155","doi-asserted-by":"crossref","unstructured":"Barjaktarovic, M., Santoni, M., Faralli, M., Bertamini, M., and Bruzzone, L. (2023, January 19\u201321). Potential Detection of Flavescence Dor\u00e9e in the Vineyard Using Close-Range Hyperspectral Imaging. Proceedings of the 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Tenerife, Spain.","DOI":"10.1109\/ICECCME57830.2023.10252351"},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"2","DOI":"10.5937\/telfor2301002B","article-title":"Data Acquisition for Testing Potential Detection of Flavescence Dor\u00e9e with a Designed, Affordable Multispectral Camera","volume":"15","author":"Santoni","year":"2023","journal-title":"Telfor J."},{"key":"ref_157","doi-asserted-by":"crossref","unstructured":"Zottele, F., Crocetta, P., and Baiocchi, V. (2022, January 3\u20135). How Important Is UAVs RTK Accuracy for the Identification of Certain Vine Diseases?. Proceedings of the 2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Perugia, Italy.","DOI":"10.1109\/MetroAgriFor55389.2022.9964928"},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.biosystemseng.2022.01.009","article-title":"Potential Field Detection of Flavescence Dor\u00e9e and Esca Diseases Using a Ground Sensing Optical System","volume":"215","author":"Daglio","year":"2022","journal-title":"Biosyst. Eng."},{"key":"ref_159","doi-asserted-by":"crossref","unstructured":"AL-Saddik, H., Simon, J.-C., and Cointault, F. (2017). Development of Spectral Disease Indices for \u2018Flavescence Dor\u00e9e\u2019 Grapevine Disease Identification. Sensors, 17.","DOI":"10.3390\/s17122772"},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1007\/s11119-018-9594-1","article-title":"Assessment of the Optimal Spectral Bands for Designing a Sensor for Vineyard Disease Detection: The Case of \u2018Flavescence Dor\u00e9e\u2019","volume":"20","author":"Simon","year":"2019","journal-title":"Precis. Agric."},{"key":"ref_161","unstructured":"Musetti, R., and Pagliari, L. (2019). Protocol for the Definition of a Multi-Spectral Sensor for Specific Foliar Disease Detection: Case of \u201cFlavescence Dor\u00e9e\u201d. Phytoplasmas: Methods and Protocols, Springer."},{"key":"ref_162","doi-asserted-by":"crossref","unstructured":"Albetis, J., Duthoit, S., Guttler, F., Jacquin, A., Goulard, M., Poilv\u00e9, H., F\u00e9ret, J.-B., and Dedieu, G. (2017). Detection of Flavescence Dor\u00e9e Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9040308"},{"key":"ref_163","doi-asserted-by":"crossref","unstructured":"Albetis, J., Jacquin, A., Goulard, M., Poilv\u00e9, H., Rousseau, J., Clenet, H., Dedieu, G., and Duthoit, S. (2018). On the Potentiality of UAV Multispectral Imagery to Detect Flavescence Dor\u00e9e and Grapevine Trunk Diseases. Remote Sens., 11.","DOI":"10.3390\/rs11010023"},{"key":"ref_164","doi-asserted-by":"crossref","unstructured":"Imran, H.A., Zeggada, A., Ianniello, I., Melgani, F., Polverari, A., Baroni, A., Danzi, D., and Goller, R. (2023). Low-Cost Handheld Spectrometry for Detecting Flavescence Dor\u00e9e in Vineyards. Appl. Sci., 13.","DOI":"10.3390\/app13042388"},{"key":"ref_165","first-page":"673","article-title":"New Solutions for the Automatic Early Detection of Diseases in Vineyards through Ground Sensing Approaches Integrating Lidar and Optical Sensors","volume":"58","author":"Gallo","year":"2017","journal-title":"Chem. Eng. Trans."},{"key":"ref_166","doi-asserted-by":"crossref","unstructured":"Elsherbiny, O., Elaraby, A., Alahmadi, M., Hamdan, M., and Gao, J. (2024). Rapid Grapevine Health Diagnosis Based on Digital Imaging and Deep Learning. Plants, 13.","DOI":"10.3390\/plants13010135"},{"key":"ref_167","doi-asserted-by":"crossref","unstructured":"Morellos, A., Pantazi, X.E., Paraskevas, C., and Moshou, D. (2022). Comparison of Deep Neural Networks in Detecting Field Grapevine Diseases Using Transfer Learning. Remote Sens., 14.","DOI":"10.3390\/rs14184648"},{"key":"ref_168","doi-asserted-by":"crossref","unstructured":"Suciu, G., Vulpe, A., Fratu, O., and Suciu, V. (2015, January 24\u201328). M2M Remote Telemetry and Cloud IoT Big Data Processing in Viticulture. Proceedings of the 2015 International Wireless Communications and Mobile Computing Conference (IWCMC), Dubrovnik, Croatia.","DOI":"10.1109\/IWCMC.2015.7289239"},{"key":"ref_169","first-page":"197","article-title":"Weather-Based Models for Predicting Grape Powdery Mildew (Uncinula Necator (Schwein) Burrill) Epidemics","volume":"47","author":"Oriolani","year":"2015","journal-title":"Rev. Fac. Cienc. Agrar. Univ. Nac. Cuyo"},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"2437","DOI":"10.1109\/JSTARS.2023.3345473","article-title":"IoT-Driven Machine Learning for Precision Viticulture Optimization","volume":"17","author":"Pero","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"108277","DOI":"10.1016\/j.compag.2023.108277","article-title":"Identification and Localization of Grape Diseased Leaf Images Captured by UAV Based on CNN","volume":"214","author":"Li","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_172","doi-asserted-by":"crossref","unstructured":"Ran\u00e7on, F., Bombrun, L., Keresztes, B., and Germain, C. (2018). Comparison of SIFT Encoded and Deep Learning Features for the Classification and Detection of Esca Disease in Bordeaux Vineyards. Remote Sens., 11.","DOI":"10.3390\/rs11010001"},{"key":"ref_173","unstructured":"Coppola, A., Di Renzo, G.C., Altieri, G., and D\u2019Antonio, P. (2020). Use of a Multirotor-UAV Equipped with a Multispectral Camera to Detect Vineyard Diseases: A Case Study on Barbera and Dolcetto Cultivars. Innovative Biosystems Engineering for Sustainable Agriculture, Forestry and Food Production, Springer International Publishing."},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1186\/s13007-020-00685-3","article-title":"Evaluating the Suitability of Hyper- and Multispectral Imaging to Detect Foliar Symptoms of the Grapevine Trunk Disease Esca in Vineyards","volume":"16","author":"Bendel","year":"2020","journal-title":"Plant Methods"},{"key":"ref_175","doi-asserted-by":"crossref","unstructured":"Al-Saddik, H., Laybros, A., Billiot, B., and Cointault, F. (2018). Using Image Texture and Spectral Reflectance Analysis to Detect Yellowness and Esca in Grapevines at Leaf-Level. Remote Sens., 10.","DOI":"10.3390\/rs10040618"},{"key":"ref_176","doi-asserted-by":"crossref","unstructured":"Wang, Y.M., Ostendorf, B., and Pagay, V. (2023). Detecting Grapevine Virus Infections in Red and White Winegrape Canopies Using Proximal Hyperspectral Sensing. Sensors, 23.","DOI":"10.3390\/s23052851"},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.compag.2019.04.008","article-title":"Visible-near Infrared Spectroradiometry-Based Detection of Grapevine Leafroll-Associated Virus 3 in a Red-Fruited Wine Grape Cultivar","volume":"162","author":"Sinha","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_178","unstructured":"Ampatzidis, Y., Cruz, A., Pierro, R., Materazzi, A., Panattoni, A., De Bellis, L., and Luvisi, A. (2018, January 12\u201316). Vision-Based System for Detecting Grapevine Yellow Diseases Using Artificial Intelligence. Proceedings of the XXX International Horticultural Congress IHC2018: VII Conference on Landscape and Urban Horticulture, IV Conference on Turfgrass Management and Science for Sports Fields and II Symposium on Mechanization, Precision Horticulture, and Robotics, Istanbul, Turkey."},{"key":"ref_179","first-page":"103876","article-title":"Evaluating the Potential of High-Resolution Hyperspectral UAV Imagery for Grapevine Viral Disease Detection in Australian Vineyards","volume":"130","author":"Ostendorf","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"7376153","DOI":"10.1155\/2023\/7376153","article-title":"Evaluating the Potential of High-Resolution Visible Remote Sensing to Detect Shiraz Disease in Grapevines","volume":"2023","author":"Wang","year":"2023","journal-title":"Aust. J. Grape Wine Res."},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.compag.2016.10.003","article-title":"Remote Hyperspectral Imaging of Grapevine Leafroll-Associated Virus 3 in Cabernet Sauvignon Vineyards","volume":"130","author":"MacDonald","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"122877","DOI":"10.1016\/j.jclepro.2020.122877","article-title":"Internet of Things (IoT): Opportunities, Issues and Challenges towards a Smart and Sustainable Future","volume":"274","author":"Patrono","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1007\/s11119-014-9360-y","article-title":"Detection of Downy Mildew of Opium Poppy Using High-Resolution Multi-Spectral and Thermal Imagery Acquired with an Unmanned Aerial Vehicle","volume":"15","author":"Landa","year":"2014","journal-title":"Precis. Agric."},{"key":"ref_184","doi-asserted-by":"crossref","unstructured":"Fahey, T., Pham, H., Gardi, A., Sabatini, R., Stefanelli, D., Goodwin, I., and Lamb, D.W. (2021). Active and Passive Electro-Optical Sensors for Health Assessment in Food Crops. Sensors, 21.","DOI":"10.3390\/s21010171"},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"109849","DOI":"10.1016\/j.foodcont.2023.109849","article-title":"Recent Advances of Application of Optical Imaging Techniques for Disease Detection in Fruits and Vegetables: A Review","volume":"152","author":"Hashim","year":"2023","journal-title":"Food Control"},{"key":"ref_186","doi-asserted-by":"crossref","unstructured":"Kouadio, L., El Jarroudi, M., Belabess, Z., Laasli, S.-E., Roni, M.Z.K., Amine, I.D.I., Mokhtari, N., Mokrini, F., Junk, J., and Lahlali, R. (2023). A Review on UAV-Based Applications for Plant Disease Detection and Monitoring. Remote Sens., 15.","DOI":"10.3390\/rs15174273"},{"key":"ref_187","doi-asserted-by":"crossref","unstructured":"Morais, R., Mendes, J., Silva, R., Silva, N., Sousa, J.J., and Peres, E. (2021). A Versatile, Low-Power and Low-Cost IoT Device for Field Data Gathering in Precision Agriculture Practices. Agriculture, 11.","DOI":"10.3390\/agriculture11070619"},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"100464","DOI":"10.1016\/j.atech.2024.100464","article-title":"Identification of Unique Electromagnetic Signatures from GLRaV-3 Infected Grapevine Leaves in Different Stages of Virus Development","volume":"8","author":"Lee","year":"2024","journal-title":"Smart Agric. Technol."},{"key":"ref_189","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1016\/j.procs.2024.06.049","article-title":"AI-Powered Solution for Plant Disease Detection in Viticulture","volume":"238","author":"Madeira","year":"2024","journal-title":"Procedia Comput. Sci."},{"key":"ref_190","doi-asserted-by":"crossref","first-page":"107250","DOI":"10.1016\/j.dib.2021.107250","article-title":"An Annotated Image Dataset of Downy Mildew Symptoms on Merlot Grape Variety","volume":"37","author":"Abdelghafour","year":"2021","journal-title":"Data Brief"},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"108876","DOI":"10.1016\/j.dib.2022.108876","article-title":"Dataset on Unmanned Aerial Vehicle Multispectral Images Acquired over a Vineyard Affected by Botrytis Cinerea in Northern Spain","volume":"46","author":"Valente","year":"2023","journal-title":"Data Brief"},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"110497","DOI":"10.1016\/j.dib.2024.110497","article-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","volume":"54","author":"Valente","year":"2024","journal-title":"Data Brief"},{"key":"ref_193","unstructured":"Hughes, D.P., and Salathe, M. (2015). An Open Access Repository of Images on Plant Health to Enable the Development of Mobile Disease Diagnostics. arXiv."},{"key":"ref_194","doi-asserted-by":"crossref","unstructured":"Mendes, J., Peres, E., Neves dos Santos, F., Silva, N., Silva, R., Sousa, J.J., Cortez, I., and Morais, R. (2022). VineInspector: The Vineyard Assistant. Agriculture, 12.","DOI":"10.3390\/agriculture12050730"},{"key":"ref_195","doi-asserted-by":"crossref","unstructured":"Cubero, S., Marco-Noales, E., Aleixos, N., Barb\u00e9, S., and Blasco, J. (2020). RobHortic: A Field Robot to Detect Pests and Diseases in Horticultural Crops by Proximal Sensing. Agriculture, 10.","DOI":"10.3390\/agriculture10070276"},{"key":"ref_196","doi-asserted-by":"crossref","unstructured":"Azevedo, F., Shinde, P., Santos, L., Mendes, J., Santos, F.N., and Mendon\u00e7a, H. (2019, January 24\u201326). Parallelization of a Vine Trunk Detection Algorithm for a Real Time Robot Localization System. Proceedings of the 2019 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Porto, Portugal.","DOI":"10.1109\/ICARSC.2019.8733644"},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1007\/s10846-017-0770-8","article-title":"Localization Based on Natural Features Detector for Steep Slope Vineyards","volume":"93","author":"Mendes","year":"2019","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_198","doi-asserted-by":"crossref","first-page":"e34017","DOI":"10.1016\/j.heliyon.2024.e34017","article-title":"Bacterial-Fungicidal Vine Disease Detection with Proximal Aerial Images","volume":"10","author":"Dobra","year":"2024","journal-title":"Heliyon"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/24\/8172\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:57:34Z","timestamp":1760115454000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/24\/8172"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,21]]},"references-count":198,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["s24248172"],"URL":"https:\/\/doi.org\/10.3390\/s24248172","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,21]]}}}