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Estimation of the canopy water content (CWC) is extremely important for irrigation management decisions. Machine learning and hyperspectral imaging technology have provided a potentially useful tool for precise measurement of plant water content. The tools, however, are hampered by feature selection as well as an advanced model in itself. Therefore, this study aims to propose an efficient prediction model and compare three feature selection methods including vegetation indices (VI), model-based features (MF), and principal component analysis (PCA). The selected features were applied with a back-propagation neural network (BPNN), random forest (RF), and partial least square regression (PLSR) for training the samples with minimal loss on a cross-validation set. The hyperspectral images were collected from rice crops grown under different water stress levels. A total of 128 images were used to evaluate our proposed methods. The results indicated that the integration of PCA and MF methods can provide a more robust feature selection for the proposed prediction model. The three bands of 1467, 1456, and 1106 nm were the supreme variants of CWC forecasting. These features were combined with an optimized BPNN model and significantly improved the foretelling accuracy. The accuracy and correlation coefficient of the advanced BPNN-PCA-MF model are close to 1 with an RMSE of 0.252. Thus, this study positively contributes to plant water content prediction researchers and policymakers so that well in advance and effective steps can be taken for precision irrigation.

Fusion of Feature Selection Methods and Regression Algorithms for Predicting the Canopy Water Content of Rice Based on Hyperspectral Data