Estimating Rice Leaf Nitrogen Content and Field Distribution Using Machine Learning with Diverse Hyperspectral Features

Abstract

Leaf nitrogen content (LNC) is a vital agronomic parameter in rice, commonly used to evaluate photosynthetic capacity and diagnose crop nutrient levels. Nitrogen deficiency can significantly reduce yield, underscoring the importance of accurate LNC estimation for practical applications. This study utilizes hyperspectral UAV imagery to acquire rice canopy data, applying various machine learning regression algorithms (MLR) to develop an LNC estimation model and create a nitrogen concentration distribution map, offering valuable guidance for subsequent field nitrogen management. The analysis incorporates four types of spectral data extracted throughout the rice growth cycle: original reflectance bands (OR bands), vegetation indices (VIs), first-derivative spectral bands (FD bands), and hyperspectral variable parameters (HSPs) as model inputs, while measured nitrogen concentration serves as the output. Results demonstrate that the random forest regression (RFR) and gradient boosting decision tree (GBDT) algorithms performed effectively, with the GBDT achieving the highest average R2 of 0.76 across different nitrogen treatments. Among the nitrogen estimation models for various rice varieties, RFR exhibited superior accuracy, achieving an R2 of 0.95 for the SuXiangJing100 variety, while the GBDT reached 0.93. Meanwhile, the support vector machine regression (SVMR) showed slightly lower accuracy, and partial least-squares regression (PLSR) was the least effective. This study developed an LNC estimation method applicable to the whole growth stage of common rice varieties. The method is suitable for estimating rice LNC across different growth stages, varieties, and nitrogen treatments, and it also provides a reference for nitrogen estimation and fertilization planning at flight altitudes other than the 120 m used in this study.