In-Field Hyperspectral Proximal Sensing for Estimating Grapevine Water Status to Support Smart Precision Viticulture

Abstract

Predawn leaf water potential (Ψpd) is the main parameter to determine plant water status, and it has been broadly used to support irrigation management. However, the Scholander pressure chamber methodology is laborious, time-consuming and invasive. This study examined a low-cost hyperspectral proximal sensor to estimate the Ψpd in grapevine (Vitis vinifera L.). For this, both the Ψpd and spectral reflectance (340–850 nm) were accessed in grapevines in a commercial vineyard located in the Douro Wine Region, northeast Portugal. A machine-learning algorithm was tested and validated to assess grapevine’s water status. The experiment was performed in a randomized design with 12 grapevines (cv. Touriga Nacional) per irrigation treatment: non-irrigated, 30% crop evapotranspiration (Etc), and 60% Etc. The dataset was analyzed using Principal Component Analysis (PCA), and the machine-learning regression algorithm applied was Extreme Gradient Boosting (Xgboost). Results from the validation dataset (n = 108) for the Xgboost tested exhibited a root mean square error (RMSE) of 0.23 MPa, a mean absolute error (MAPE) of 16.57% and an R² value of 0.95. These results demonstrate that the hyperspectral sensor and Xgboost algorithm show potential for predicting the Ψpd in vineyards, regardless of a plant’s water status.

Keywords

• leaf water potential
• machine learning
• point-of-measurement
• precision viticulture