2026-05-06T11:53:27Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/100552025-01-30T01:05:25Zcom_10259.4_2506com_10259.4_2505com_10259.4_106com_10259_2604col_10259_7491

Vélez Martín, Sergio 31deed37-f210-4d3c-8107-3297a0706165 600 Ariza Sentís, Mar be159a69-4311-4cb6-8826-775b79077656 600 Valente, João 724f56a4-7b2b-4049-9b3e-e3704b05b517 600 2025-01-29T08:24:26Z 2025-01-29T08:24:26Z 2023-01 1161-0301 http://hdl.handle.net/10259/10055 10.1016/j.eja.2022.126691 The fungus Botrytis cinerea causes severe diseases in many crops. In grapevines, it causes Botrytis bunch rot (BBR), one of the most reported diseases worldwide. It affects all herbaceous organs of the vine, especially the ripe berries, causing significant reductions in yield and wine quality. Botrytis detection models traditionally focus on temporal analysis at a specific spatial location, ignoring the study of the spatial variability of the crop. Unmanned aerial vehicles (UAVs) equipped with multispectral cameras can provide high-resolution images that can be valuable information to develop a tool for aerial pest detection. This paper proposes an algorithm to assess the risk of Botrytis development in a vineyard in Spain, using as input products generated by UAV imagery: DTM (Digital Terrain Model), NDVI (Normalised Difference Vegetation Index), CHM (Canopy Height Model) and LAI (Leaf Area Index). They represent the height and architecture of the canopy, the topography and the plant status. Healthy vines were significantly different from vines affected by Botrytis (p < 0.05) in each of these variables, supporting the consistency of using these inputs for the model. This methodology combines photogrammetric, spatial analysis techniques, and machine learning classification methods with deep vineyard-related agronomic knowledge to produce heatmaps with acceptable accuracy (R² > 0.7) that may support vineyard managers in understanding the spatial variability of the disease, allowing the spatial 2D visualisation of the risk of BBR disease development and, potentially, resulting in higher operational efficiency and reducing phytosanitary treatments, as well as economic costs. Furthermore, the present work takes advantage of imaging technologies that provide information about any location in the field, not only about specific points in the vineyard, suggesting that UAV imagery is appropriate to measure the likelihood of BBR development within the vineyard, highlighting the importance of efficient disease management based on spatial variability. This work has been carried out in the scope of the H2020 FlexiGroBots project, which has been funded by the European Commission in the scope of its H2020 programme (contract number 101017111, https://flexigrobots-h2020.eu/) application/pdf eng Elsevier European Journal of Agronomy. 2023, V. 142, p. 126691 https://doi.org/10.1016/j.eja.2022.126691 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Disease detection Multispectral NDVI Risk UAV Agricultura Viticultura Agriculture Viticulture Mapping the spatial variability of Botrytis bunch rot risk in vineyards using UAV multispectral imagery info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion European Journal of Agronomy 142 126691 THUMBNAIL Velez-ejoa_2023.pdf.jpg Velez-ejoa_2023.pdf.jpg IM Thumbnail image/jpeg 4989 https://riubu.ubu.es/bitstream/10259/10055/4/Velez-ejoa_2023.pdf.jpg 68d4e223c5ac8fe524b9a15fe12b7e02 MD5 4 LICENSE license.txt license.txt text/plain; charset=utf-8 1362 https://riubu.ubu.es/bitstream/10259/10055/3/license.txt 5d013bfa6e473ff0db22cd82a4d71a70 MD5 3 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 908 https://riubu.ubu.es/bitstream/10259/10055/2/license_rdf 0175ea4a2d4caec4bbcc37e300941108 MD5 2 ORIGINAL Velez-ejoa_2023.pdf Velez-ejoa_2023.pdf application/pdf 10408757 https://riubu.ubu.es/bitstream/10259/10055/1/Velez-ejoa_2023.pdf 9d0de5ed4f5297379dd56525fea8c850 MD5 1 10259/10055 oai:riubu.ubu.es:10259/10055 2025-01-30 02:05:25.762 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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