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

文献类型： 外文期刊

第一作者： Li, Zongpeng

作者： Li, Zongpeng;Zhou, Xinguo;Cheng, Qian;Chen, Zhen;Fei, Shuaipeng

作者机构：

关键词： RGB; multispectral; texture; ensemble learning; plant phenotyping

期刊名称：REMOTE SENSING （ 影响因子：5.0； 五年影响因子：5.6 ）

ISSN：

年卷期： 2023 年 15 卷 8 期

页码：

收录情况： SCI

摘要： 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 (R-2) 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 (R-2 = 0.726, RMSE = 3.203 mg center dot g(-1), MSE = 10.259 mg center dot 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.

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