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Agronomy
Volume 15
Issue 12
10.3390/agronomy15122915
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Estimation of Leaf Nitrogen Content in Rice Coupling Feature Fusion and Deep Learning with Multi-Sensor Images from UAV

by
Xinlei Xu
1,2,
Xingang Xu
1,2,*,
Sizhe Xu
1,
Yang Meng
1,
Guijun Yang
1,
Bo Xu
1,
Xiaodong Yang
1,
Xiaoyu Song
1,
Hanyu Xue
1,2,
Yuekun Song
1 and
Tuo Wang
1
1
Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
2
School of Intelligent Science and Engineering, Beijing University of Agriculture, Beijing 102206, China
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(12), 2915; https://doi.org/10.3390/agronomy15122915
Submission received: 17 November 2025 / Revised: 12 December 2025 / Accepted: 16 December 2025 / Published: 18 December 2025
(This article belongs to the Section Precision and Digital Agriculture)
Versions Notes

Abstract

Assessing Leaf Nitrogen Content (LNC) is critical for evaluating crop nutritional status and monitoring growth. While Unmanned Aerial Vehicle (UAV) remote sensing has become a pivotal tool for nitrogen monitoring at the field scale, current research predominantly relies on uni-modal feature variables. Consequently, the integration of multidimensional feature information for nitrogen assessment remains largely underutilized in existing literature. In this study, the four types of feature variables (two kinds of spectral indices, color space parameters and texture features from UAV images of RGB and multispectral sensors) were extracted from three dimensions, and crop nitrogen-sensitive feature variables were selected by GCA (Gray Correlation Analysis), followed by one fused deep neural network (DNN-F2) for remote sensing monitoring of rice nitrogen and a comparative analysis with five common machine learning algorithms (RF, GPR, PLSR, SVM and ANN). Experimental results indicate that the DNN-F2 model consistently outperformed conventional machine learning algorithms across all three growth stages. Notably, the model achieved an average R2 improvement of 40%, peaking at the rice jointing stage with an R2 of 0.72 and an RMSE of 0.08. The study shows that the fusion of multidimensional feature information from UAVs combined with deep learning algorithms has great potential for nitrogen nutrient monitoring in rice crops, and can also provide technical support to guide decisions on fertilizer application in rice fields.
Keywords: UAV remote sensing; nitrogen content monitoring; color space transformation; feature fusion; deep learning; rice UAV remote sensing; nitrogen content monitoring; color space transformation; feature fusion; deep learning; rice

Share and Cite

MDPI and ACS Style

Xu, X.; Xu, X.; Xu, S.; Meng, Y.; Yang, G.; Xu, B.; Yang, X.; Song, X.; Xue, H.; Song, Y.; et al. Estimation of Leaf Nitrogen Content in Rice Coupling Feature Fusion and Deep Learning with Multi-Sensor Images from UAV. Agronomy 2025, 15, 2915. https://doi.org/10.3390/agronomy15122915

AMA Style

Xu X, Xu X, Xu S, Meng Y, Yang G, Xu B, Yang X, Song X, Xue H, Song Y, et al. Estimation of Leaf Nitrogen Content in Rice Coupling Feature Fusion and Deep Learning with Multi-Sensor Images from UAV. Agronomy. 2025; 15(12):2915. https://doi.org/10.3390/agronomy15122915

Chicago/Turabian Style

Xu, Xinlei, Xingang Xu, Sizhe Xu, Yang Meng, Guijun Yang, Bo Xu, Xiaodong Yang, Xiaoyu Song, Hanyu Xue, Yuekun Song, and et al. 2025. "Estimation of Leaf Nitrogen Content in Rice Coupling Feature Fusion and Deep Learning with Multi-Sensor Images from UAV" Agronomy 15, no. 12: 2915. https://doi.org/10.3390/agronomy15122915

APA Style

Xu, X., Xu, X., Xu, S., Meng, Y., Yang, G., Xu, B., Yang, X., Song, X., Xue, H., Song, Y., & Wang, T. (2025). Estimation of Leaf Nitrogen Content in Rice Coupling Feature Fusion and Deep Learning with Multi-Sensor Images from UAV. Agronomy, 15(12), 2915. https://doi.org/10.3390/agronomy15122915

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