Effect of multi-level and multi-scale spectral data source on vineyard state assessment

Leaf water potential (LWP) is widely used to assess plant water status. Also, pigment concentration work as a proxy for the canopy's water status. Spectral data methods have been applied to monitor and evaluate crops' biophysical variables. This work developed a model to predict LWP using a UAS equipped with a VIS-NIR multispectral camera and trained artificial neural network (ANN) represents a good and relatively cheap solution to assess plant status spatial information to predict LWP and obtain canopy and foliar reflectance on three dates in 2020 and two ways of validation; a pressure chamber to measure and geophysics survey of the electronic conductivity (EC). Two modeling approaches, combining spectral data and spectral vegetation indices, were used to estimate LWP in a commercial vineyard in the Tufo Wine Region. The first approach predicts LWP through vine's canopy reflectance; reconstructed (from 5 bands to 21bands) dataset using a conventional neural network (CNN) the several vegetation indices (VIs) were computed and selected. The second modeling approach is based only on the (CNN) reconstructed dataset. Both approaches predicted LWP-based Vls and spectral data classified from high to low; the results were consistent in direct proportion to the laboratory results and performed the best results for both modeling approaches.