Precision monitoring of rice nitrogen fertilizer levels based on machine learning and UAV multispectral imagery

Abstract

Rice is the primary food crop globally, and effective nitrogen fertilizer management is essential for optimizing yield while minimizing environmental impact. This study integrated unmanned aerial vehicle (UAV) imagery with multispectral imaging and machine learning (ML) methods to classify nitrogen levels (N levels) in rice fields. Experimental fields with various N levels (underfertilized, optimal fertilization, and overfertilized) were imaged in 2020 and 2021 by using UAVs. The captured images underwent geometric and spectral corrections, and rice pixel segmentation was performed using a decision tree classifier, which achieved a recall of 95.3 % and an overall accuracy of 88.8 %. N level classification was performed by extracting 16 spectral and structural features from the images, including color space transformations, vegetation indices, and canopy coverage. These features were input to support vector machine (SVM) and k nearest neighbors (KNN) models, and feature selection methods were applied to improve performance. The SVM model outperformed the KNN model, particularly in Period II, achieving an overall accuracy of 90.0 % when the chi-square feature selection method was applied. The Red Edge Ratio Vegetation Index and canopy coverage were the most informative features for classification. The integration of UAV-based multispectral imagery and ML in this study enhanced nitrogen classification accuracy and scalability. The method provides a data-driven approach for precision agriculture and sustainable fertilization management.