Accurate and efficient screening of nitrogen-efficient rice varieties is crucial for implementing precision agriculture and achieving green and sustainable development. However, traditional screening methods rely on destructive sampling and chemical analysis, which are inefficient and costly, and thus cannot meet the requirements of large-scale breeding applications. Therefore, this study aims to develop a non-invasive, high-throughput screening method for nitrogen efficiency of rice based on unmanned aerial vehicle (UAV) hyperspectral data and machine learning algorithms. Sixty rice varieties were selected as the target, and principal component analysis (PCA) was used to reduce the dimension of seven key agronomic parameters (such as yield, nitrogen utilization rate, etc.). A comprehensive evaluation index for nitrogen utilization efficiency was constructed, and K-means clustering was used to classify the varieties into three categories: nitrogen-efficient, medium-efficient, and low-efficient varieties. On this basis, four machine learning algorithms (decision tree (DT), random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN)) were used to establish a variety nitrogen efficiency classification model based on spectral indices. The results showed that the indicators constructed based on PCA and clustering could effectively distinguish different nitrogen-efficient varieties; among the four models compared, the DT model achieved the highest overall performance, with an accuracy of 0.75, precision of 0.80, and F1-score of 0.74. This study confirmed the feasibility of combining UAV hyperspectral data with decision tree models, providing a reliable technical solution for the large-scale, rapid, and non-invasive screening of nitrogen-efficient rice varieties.
Han et al. (Sat,) studied this question.