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• W2766295554 endingPage "2488" @default.
• W2766295554 startingPage "2488" @default.
• W2766295554 abstract "Water stress, which affects yield and wine quality, is often evaluated using the midday stem water potential (Ψstem). However, this measurement is acquired on a per plant basis and does not account for the assessment of vine water status spatial variability. The use of multispectral cameras mounted on unmanned aerial vehicle (UAV) is capable to capture the variability of vine water stress in a whole field scenario. It has been reported that conventional multispectral indices (CMI) that use information between 500–800 nm, do not accurately predict plant water status since they are not sensitive to water content. The objective of this study was to develop artificial neural network (ANN) models derived from multispectral images to predict the Ψstem spatial variability of a drip-irrigated Carménère vineyard in Talca, Maule Region, Chile. The coefficient of determination (R2) obtained between ANN outputs and ground-truth measurements of Ψstem were between 0.56–0.87, with the best performance observed for the model that included the bands 550, 570, 670, 700 and 800 nm. Validation analysis indicated that the ANN model could estimate Ψstem with a mean absolute error (MAE) of 0.1 MPa, root mean square error (RMSE) of 0.12 MPa, and relative error (RE) of −9.1%. For the validation of the CMI, the MAE, RMSE and RE values were between 0.26–0.27 MPa, 0.32–0.34 MPa and −24.2–25.6%, respectively." @default.
• W2766295554 created "2017-11-10" @default.
• W2766295554 creator A5047539499 @default.
• W2766295554 creator A5057480937 @default.
• W2766295554 creator A5058036055 @default.
• W2766295554 creator A5073465170 @default.
• W2766295554 date "2017-10-30" @default.
• W2766295554 modified "2023-10-03" @default.
• W2766295554 title "Artificial Neural Network to Predict Vine Water Status Spatial Variability Using Multispectral Information Obtained from an Unmanned Aerial Vehicle (UAV)" @default.
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• W2766295554 doi "https://doi.org/10.3390/s17112488" @default.
• W2766295554 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5713508" @default.
• W2766295554 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29084169" @default.
• W2766295554 hasPublicationYear "2017" @default.
• W2766295554 type Work @default.

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