Vineyard water status estimation using multispectral imagery from an UAV platform and machine learning algorithms for irrigation scheduling management

Abstract

Remote sensing can provide a fast and reliable alternative for traditional in situ water status measurement in vineyards. Several vegetation indices (VIs) derived from aerial multispectral imagery were tested to estimate midday stem water potential (Ψstem) of grapevines. The experimental trial was carried out in a vineyard in the Shangri-La region, located in Yunnan province in China. Statistical methods and machine learning algorithms were used to evaluate the correlations between Ψstem and VIs. Results by simple regression between VIs individually and Ψstem showed no significant relationships, with coefficient of determination (R2) for linear fitting smaller than 0.3 for almost all the indices studied, except for the Optimal Soil Adjusted Vegetation Index (OSAVI); R2 = 0.42 with statistical significance (p ≤ 0.001). However, results from a model obtained by fitting using Artificial Neural Network (ANN), using all VIs calculated as inputs and real Ψstem from plants within the study site (n = 90) as targets (Model 1), showed high correlation between the estimated water potential through ANN (Ψstem ANN) and the actual measured Ψstem. Training, validation and testing data sets presented individual correlations of R = 0.8, 0.72 and 0.62 respectively. The models obtained from the study site were then applied to a wider area from the vineyard studied and compared to further Ψstem measured obtained from different sites (n = 23) showing high correlation values between Ψstem ANN and real Ψstem (R2 = 0.83; slope = 1; p ≤ 0.001). Finally, a pattern recognition ANN model (Model 2) was developed for irrigation scheduling purposes using the same Ψstem measured in the study site as inputs and with the following thresholds as outputs: Ψstem below −1.2 MPa considered as severe water stress (SS), Ψstem between −0.8 to −1.2 MPa as moderate stress (MS) and Ψstem over −0.8 MPa with no water stress (NS). This model can be applied to analyze on a plant by plant basis to identify sectors of stress within the vineyard for optimal irrigation management and to identify spatial variability within the vineyards.