RT info:eu-repo/semantics/article
T1 Mapping the spatial variability of Botrytis bunch rot risk in vineyards using UAV multispectral imagery
A1 Vélez Martín, Sergio
A1 Ariza Sentís, Mar
A1 Valente, João
K1 Disease detection
K1 Multispectral
K1 NDVI
K1 Risk
K1 UAV
K1 Agricultura
K1 Agriculture
K1 Viticultura
K1 Viticulture
AB 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.
PB Elsevier
SN 1161-0301
YR 2023
FD 2023-01
LK http://hdl.handle.net/10259/10055
UL http://hdl.handle.net/10259/10055
LA eng
NO 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/)
DS Repositorio Institucional de la Universidad de Burgos
RD 06-feb-2025