Ensemble Learning-Driven and UAV Multispectral Analysis for Estimating the Leaf Nitrogen Content in Winter Wheat

The aim of this study is to develop a rapid method for monitoring leaf nitrogen content (LNC) in winter wheat, which is essential for precise field management and accurate crop growth assessment. This study used a natural winter wheat population at Shanxi Agricultural University’s experimental base...

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Published in:Agronomy (Basel) Vol. 15; no. 7; p. 1621
Main Authors: Han, Yu, Zhang, Jiaxue, Bai, Yan, Liang, Zihao, Guo, Xinhui, Zhao, Yu, Feng, Meichen, Xiao, Lujie, Song, Xiaoyan, Zhang, Meijun, Yang, Wude, Li, Guangxin, Yang, Sha, Qiao, Xingxing, Wang, Chao
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.07.2025
Subjects:
Accuracy
Agricultural production
Agriculture
Algorithms
Correlation analysis
Crop growth
Data mining
Design of experiments
Ensemble learning
Environmental conditions
Grouting
Image processing
Image segmentation
leaf nitrogen content
Learning
Learning strategies
Leaves
Low altitude
Machine learning
Monitoring
Monitoring methods
multispectral
Multispectral analysis
Nitrogen
Performance evaluation
Plot (Narrative)
Precision agriculture
Remote sensing
Root-mean-square errors
Sensors
Spectral reflectance
Support vector machines
Triticum aestivum
Unmanned aerial vehicles
Vegetation
Vegetation index
Wheat
Winter
Winter wheat
China
ISSN:2073-4395, 2073-4395
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Summary:The aim of this study is to develop a rapid method for monitoring leaf nitrogen content (LNC) in winter wheat, which is essential for precise field management and accurate crop growth assessment. This study used a natural winter wheat population at Shanxi Agricultural University’s experimental base as the subject. UAV-mounted multispectral sensors collected images at jointing, heading, pre-grouting, and late grouting stages. Canopy spectral reflectance was extracted using image segmentation, and vegetation indices were calculated. Correlation analysis identified highly relevant indices with LNC. Support Vector Regression (SVR), Random Forest (RF), Ridge Regression (RR), K-Nearest Neighbors (K-NN), and ensemble learning algorithms (Voting and Stacking) were employed to model the relationship between selected vegetation indices and LNC. Model performance was evaluated using the coefficient of determination (R2) and root mean square error (RMSE). Results showed that the Voting-based ensemble learning model outperformed other models. At the pre-grouting stage, this model achieved an R2 of 0.85 and an RMSE of 1.57 for the training set, and an R2 of 0.82 and an RMSE of 1.64 for the testing set. This study provides a theoretical basis and technical reference for monitoring LNC in winter wheat at key growth stages using low-altitude multispectral sensors, supporting precision agriculture and variety evaluation.
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ISSN:2073-4395
2073-4395
DOI:10.3390/agronomy15071621