Article (Scientific journals)
Predicting the site-specific distribution of agrochemical spray deposition in vineyards at multiple phenological stages using 2D LiDAR-based primary canopy attributes
Cheraiet, Anice; Naud, Olivier; Carra, Mathilde et al.
2021In Computers and Electronics in Agriculture, 189 (106402)
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Keywords :
Canopy density; Variable-rate spraying; 3D vine; Leaf Wall Area; Log-linear models
Abstract :
[en] Predicting the dose to be applied on the basis of the structural characteristics of the plant canopy is a crucial step for the optimization of the spraying process. Mobile 2D LiDAR sensor data and local measurements of deposition rates from a face-to-face sprayer were made across eight fields in two Mediterranean vineyards at four dates in 2016 and 2017. Primary canopy attributes (height, width and density) were calculated from the LiDAR sensor data and the leaf wall area (LWA) determined. Multivariate models to predict the deposition distribution, as deciles, as a function of the primary canopy attributes were constructed and calibrated using the 2017 data and validated against the 2016 data. The prediction quality and uncertainty of these multivariate statistical models at various stages of growth was evaluated by comparison with a previously proposed univariate deposition models based on LWA at the same growth stages. The results showed that multivariate models can predict the distribution of deposits from a typical face-to-face sprayer more accurately (0.76 < R2 < 0.94), and robustly (10% < nRMSEp < 24%) than LWA-based univariate prediction models over the whole growing season. This improvement was especially clear for the lowest deciles (D1 to D5) of the deposition distribution. Results also demonstrated the importance of canopy density to provide relevant and complementary information to canopy dimensions when predicting deposition deciles with the multivariate models. The improved ability of multivariate models to predict underestimated deposition (􀀀 1.5% < bias < 􀀀 3.2%) when compared to univariate models makes it possible to consider a reduction in the plant protection products while guaranteeing a safety margin for winegrowers when spraying. These predictive multivariate models could enable variable-rate sprayers to modulate doses at an intra-plot scale, which would allow a potential reduction in the quantities of plant protection products to be applied.
Research center :
UMT EcotechViti
Disciplines :
Mechanical engineering
Agriculture & agronomy
Author, co-author :
Cheraiet, Anice;  National Research Institute for Agriculture, Food and Environment > AgroEcoSystems
Naud, Olivier;  National Research Institute for Agriculture, Food and Environment > MatNum > ITAP
Carra, Mathilde;  National Research Institute for Agriculture, Food and Environment > AgroEcoSystems > ITAP
Codis, Sébastien;  French Wine and Vine Institute
Lebeau, Frédéric  ;  Université de Liège - ULiège > Département GxABT > Biosystems Dynamics and Exchanges
Taylor, James;  National Research Institute for Agriculture, Food and Environment > MatNum > ITAP
Language :
English
Title :
Predicting the site-specific distribution of agrochemical spray deposition in vineyards at multiple phenological stages using 2D LiDAR-based primary canopy attributes
Publication date :
2021
Journal title :
Computers and Electronics in Agriculture
ISSN :
0168-1699
eISSN :
1872-7107
Publisher :
Elsevier, Netherlands
Volume :
189
Issue :
106402
Peer reviewed :
Peer Reviewed verified by ORBi
Funders :
DigitAg#
Available on ORBi :
since 13 September 2021

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