Accurate estimation of sorghum crop water content under different water stress levels using machine learning and hyperspectral data.

This study investigates the effects of different water stress levels on spectral information, leaf area index (LAI), and the performance of three machine learning (ML) algorithms in estimating crop water content (CWC) of sorghum. The results show that the spectral reflectance of sorghum varies with growth stage and irrigation treatment, but consistent patterns are observed for each treatment. The LAI of sorghum gradually increased throughout the growth stages, with the most significant variation observed during the flowering stage. In this study, three machine learning-based regression models, namely, extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM), were utilized to estimate sorghum CWC using hyperspectral measurements. Recursive feature elimination (RFE) method was used to select the optimal spectral reflectance wavelengths for the ML models, and principal component analysis (PCA) was used to reduce the dimensionality of the hyperspectral data. The results indicated that the RF model achieved the highest R (0.90) and lowest of RMSE (56.05) value using selected wavelengths, while the XGBoost model demonstrated superior accuracy and reliability in estimating CWC using dimensionality-reduced hyperspectral data (r = 0.96, RMSE = 45.77). Also, the study highlights the importance of vegetation index (VI) in CWC estimate. Some VIs, such as NDVI and MSAVI, performed poorly, while others, such as CL_Rededge and EVI, performed better. The study provides valuable insights into the effects of water stress levels on spectral information, LAI, and the performance of ML algorithms in estimating the CWC of sorghum. The findings have significant implications for precision agriculture, as accurate and reliable estimates of CWC can help farmers optimize irrigation and fertilizer applications, leading to improved crop yields and resource efficiency.