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

Fan Ding, Changchun Li, Weiguang Zhai, Shuaipeng Fei, Qian Cheng and Zhen Chen ()
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Fan Ding: Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China
Changchun Li: School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China
Weiguang Zhai: Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China
Shuaipeng Fei: Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China
Qian Cheng: Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China
Zhen Chen: Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China

Agriculture, 2022, vol. 12, issue 11, 1-16

Abstract: 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.

Keywords: remote sensing; nitrogen content; multi-source data fusion; machine learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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