Research on Water and Fertilizer Diagnosis of Maize Using Visible–Near-Infrared Hyperspectral Technology

This study focuses on maize to explore spectral estimation methods for agricultural traits in maize leaves under water-saving and fertilizer-reduction strategies. A factorial experiment was conducted with different nitrogen application rates (N0–N4) and irrigation levels (W1–W4). Hyperspectral data were collected at V12, R1, and R3 stages, alongside measurements of agricultural traits ((relative chlorophyll content) SPAD values, leaf water content (LWC), and leaf nitrogen content (LNC)). Results indicated that reducing nitrogen by 10% (N3) had no significant effect on physiological indicators, whereas reducing irrigation by 10% (W3) led to significant differences. First- and second-derivative transformations of spectral data enhanced the correlation with agricultural traits. Support vector regression (SVR) and random forest (RF) models were developed for estimation. RF outperformed SVR in predicting agricultural traits (SPAD, LWC, and LNC), with estimation accuracy R2 values reaching 0.92, 0.94, and 0.95, respectively. The RF model demonstrated higher accuracy, providing technical support for growth monitoring and precise water and nutrient management in maize.