A Machine-Learning Model Based on the Fusion of Spectral and Textural Features from UAV Multi-Sensors to Analyse the Total Nitrogen Content in Winter Wheat

Abstract

Timely and accurate monitoring of the nitrogen levels in winter wheat can reveal its nutritional status and facilitate informed field management decisions. Machine learning methods can improve total nitrogen content (TNC) prediction accuracy by fusing spectral and texture features from UAV-based image data. This study used four machine learning models, namely Gaussian Process Regression (GPR), Random Forest Regression (RFR), Ridge Regression (RR), and Elastic Network Regression (ENR), to fuse data and the stacking ensemble learning method to predict TNC during the winter wheat heading period. Thirty wheat varieties were grown under three nitrogen treatments to evaluate the predictive ability of multi-sensor (RGB and multispectral) spectral and texture features. Results showed that adding texture features improved the accuracy of TNC prediction models constructed based on spectral features, with higher accuracy observed with more features input into the model. The GPR, RFR, RR, and ENR models yielded coefficient of determination (R2) values ranging from 0.382 to 0.697 for TNC prediction accuracy. Among these models, the ensemble learning approach produced the best TNC prediction performance (R2 = 0.726, RMSE = 3.203 mg·g−1, MSE = 10.259 mg·g−1, RPD = 1.867, RPIQ = 2.827). Our findings suggest that accurate TNC prediction based on UAV multi-sensor spectral and texture features can be achieved through data fusion and ensemble learning, offering a high-throughput phenotyping approach valuable for future precision agriculture research.