Estimation of Chlorophyll and Water Content in Maize Leaves Under Drought Stress Based on VIS/NIR Spectroscopy

Maize (Zea mays) is a key crop, with its growth impacted by drought stress. Accurate, non-destructive assessment of drought severity is crucial for precision agriculture. VIS/NIR reflectance spectroscopy is widely used for estimating plant parameters and detecting stress. However, the relationship between key parameters—such as chlorophyll and water content—and VIS/NIR spectra under drought conditions in maize remains unclear, lacking comprehensive models and validation. This study aims to develop a non-destructive and accurate method for predicting chlorophyll and water content in maize leaves under drought stress using VIS/NIR spectroscopy. Specifically, maize leaf reflectance spectra were collected under varying drought stress conditions, and the effects of different spectral preprocessing methods, dimensionality reduction techniques, and machine learning algorithms were evaluated. An optimal data processing pipeline was systematically established and deployed on an edge computing unit to enable rapid, non-destructive prediction of chlorophyll and water content in maize leaves. The experimental results demonstrated that the combination of stepwise regression (SR) for feature selection and a stacking regression model achieved the best performance for chlorophyll content prediction (Rp2 = 0.8740, RMSEp = 0.2768). For leaf water content prediction, random forest (RF) feature selection combined with a stacking model yielded the highest accuracy (Rp2 = 0.7626, RMSEp = 4.12%). This study confirms the effectiveness and potential of integrating VIS/NIR spectroscopy with machine learning algorithms for monitoring drought stress in maize, offering a valuable theoretical foundation and practical reference for non-destructive crop physiological monitoring in precision agriculture.