Assessing grapevine water status through fusion of hyperspectral imaging and 3D point clouds

Mild to moderate and timely water deficit is desirable in grape production to optimize fruit quality for winemaking. It is crucial to develop robust and rapid approaches to assess grapevine water stress for scheduling deficit irrigation. Hyperspectral imaging (HSI) has the potential to detect changes in leaf water status, but the robustness and accuracy are restricted in field applications. The varied leaf orientations can significantly affect how light interacts with the plant, ultimately influencing the reflectance properties. This study focused on developing an approach for detecting grapevine water status using HSI and 3D data. Leaf orientation parameters derived from 3D point clouds were integrated with spectral signatures to address the spectral variance caused by variations in leaf orientation. A water status assessment model was developed based on multiblock partial least squares (MBPLS) to estimate midday leaf water potential (Psi(L)) using spectral signatures and leaf orientation parameters. HSI and 3D point clouds of selected leaves were captured simultaneously in a vineyard during the 2021 growing season, and Psi(L) was measured as the ground truth to assess the model performance. Mean spectral reflectance was derived from the hyperspectral images, while leaf orientation parameters were extracted from 3D point cloud data. The dataset was split randomly into 70% training/calibration and 30% test datasets. The test result shows that the model estimated the Psi(L) with R-2 = 0.89, RMSE=0.12 MPa and MAE=0.09 MPa. The leaf orientation parameters derived from 3D point clouds had a contribution of 6.25% in estimating Psi(L). Moreover, it acted as an enhancing component that explained the spectral variance caused by variations in leaf orientation and improved the interpretation of the underlying relationship between spectral reflectance and vine water status.