Effects of Variety and Growth Stage on UAV Multispectral Estimation of Plant Nitrogen Content of Winter Wheat

Meiyan Shu, Zhiyi Wang, Wei Guo, Hongbo Qiao, Yuanyuan Fu, Yan Guo, Laigang Wang (), Yuntao Ma and Xiaohe Gu
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Meiyan Shu: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Zhiyi Wang: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Wei Guo: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Hongbo Qiao: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Yuanyuan Fu: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China
Yan Guo: Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
Laigang Wang: Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
Yuntao Ma: College of Land Science and Technology, China Agricultural University, Beijing 100091, China
Xiaohe Gu: Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China

Agriculture, 2024, vol. 14, issue 10, 1-19

Abstract: The accurate estimation of nitrogen content in crop plants is the basis of precise nitrogen fertilizer management. Unmanned aerial vehicle (UAV) imaging technology has been widely used to rapidly estimate the nitrogen in crop plants, but the accuracy will still be affected by the variety, the growth stage, and other factors. We aimed to (1) analyze the correlation between the plant nitrogen content of winter wheat and spectral, texture, and structural information; (2) compare the accuracy of nitrogen estimation at single versus multiple growth stages; (3) assess the consistency of UAV multispectral images in estimating nitrogen content across different wheat varieties; (4) identify the best model for estimating plant nitrogen content (PNC) by comparing five machine learning algorithms. The results indicated that for the estimation of PNC across all varieties and growth stages, the random forest regression (RFR) model performed best among the five models, obtaining R 2 , RMSE, MAE, and MAPE values of 0.90, 0.10%, 0.08, and 0.06%, respectively. Additionally, the RFR estimation model achieved commendable accuracy in estimating PNC in three different varieties, with R 2 values of 0.91, 0.93, and 0.72. For the dataset of the single growth stage, Gaussian process regression (GPR) performed best among the five regression models, with R 2 values ranging from 0.66 to 0.81. Due to the varying nitrogen sensitivities, the accuracy of UAV multispectral nitrogen estimation was also different among the three varieties. Among the three varieties, the estimation accuracy of SL02-1 PNC was the worst. This study is helpful for the rapid diagnosis of crop nitrogen nutrition through UAV multispectral imaging technology.

Keywords: winter wheat; variety; unmanned aerial vehicle (UAV); multispectral image; plant nitrogen content (PNC); 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: 2024
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