Estimation of Nitrogen Content in Winter Wheat Based on Multi-Source Data Fusion and Machine Learning

被引:19
作者
Ding, Fan [1 ,2 ]
Li, Changchun [2 ]
Zhai, Weiguang [1 ,2 ]
Fei, Shuaipeng [1 ]
Cheng, Qian [1 ]
Chen, Zhen [1 ]
机构
[1] Chinese Acad Agr Sci, Inst Farmland Irrigat, Xinxiang 453002, Henan, Peoples R China
[2] Henan Polytech Univ, Sch Surveying & Land Informat Engn, Jiaozuo 454003, Henan, Peoples R China
来源
AGRICULTURE-BASEL | 2022年 / 12卷 / 11期
关键词
remote sensing; nitrogen content; multi-source data fusion; machine learning; VEGETATION INDEXES; GRAIN-YIELD; PREDICTION; IMAGES;
D O I
10.3390/agriculture12111752
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
Nitrogen (N) is an important factor limiting crop productivity, and accurate estimation of the N content in winter wheat can effectively monitor the crop growth status. The objective of this study was to evaluate the ability of the unmanned aerial vehicle (UAV) platform with multiple sensors to estimate the N content of winter wheat using machine learning algorithms; to collect multispectral (MS), red-green-blue (RGB), and thermal infrared (TIR) images to construct a multi-source data fusion dataset; to predict the N content in winter wheat using random forest regression (RFR), support vector machine regression (SVR), and partial least squares regression (PLSR). The results showed that the mean absolute error (MAE) and relative root-mean-square error (rRMSE) of all models showed an overall decreasing trend with an increasing number of input features from different data sources. The accuracy varied among the three algorithms used, with RFR achieving the highest prediction accuracy with an MAE of 1.616 mg/g and rRMSE of 12.333%. For models built with single sensor data, MS images achieved a higher accuracy than RGB and TIR images. This study showed that the multi-source data fusion technique can enhance the prediction of N content in winter wheat and provide assistance for decision-making in practical production.
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页数:16
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