A Spatiotemporal Attention-Guided Graph Neural Network for Precise Hyperspectral Estimation of Corn Nitrogen Content

Lu, Feiyu; Zhang, Boming; Hou, Yifei; Xiong, Xiao; Dong, Chaoran; Lu, Wenbo; Li, Liangxue; Lv, Chunli

doi:10.3390/agronomy15051041

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Open AccessArticle

A Spatiotemporal Attention-Guided Graph Neural Network for Precise Hyperspectral Estimation of Corn Nitrogen Content

by

Feiyu Lu

,

Boming Zhang

,

Yifei Hou

,

Xiao Xiong

,

Chaoran Dong

,

Wenbo Lu

,

Liangxue Li

and

Chunli Lv

^*

China Agricultural University, Beijing 100083, China

^*

Author to whom correspondence should be addressed.

Agronomy 2025, 15(5), 1041; https://doi.org/10.3390/agronomy15051041 (registering DOI)

Submission received: 29 March 2025 / Revised: 20 April 2025 / Accepted: 24 April 2025 / Published: 26 April 2025

(This article belongs to the Special Issue Application of Deep and Machine Learning in Crop Monitoring and Management)

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Abstract

A hyperspectral maize nitrogen content prediction model is proposed, integrating a dynamic spectral–spatiotemporal attention mechanism with a graph neural network, with the aim of enhancing the accuracy and stability of nitrogen estimation. Across multiple experiments, the proposed method achieved outstanding performance on the test set, with

R^{2} = 0.93

, RMSE of 0.35, and MAE of 0.48, significantly outperforming comparative models including SVM, RF, ResNet, and ViT. In experiments conducted across different growth stages, the best performance was observed during the grain-filling stage, where

R^{2}

reached 0.96. In terms of accuracy, recall, and precision, the proposed model exhibited an average improvement exceeding 15%, demonstrating strong adaptability to temporal variation and generalization across spatial conditions. These results provide robust technical support for large-scale, nondestructive nitrogen monitoring in agricultural applications.

Keywords: hyperspectral imaging; corn nitrogen content estimation; spatiotemporal attention; graph neural networks

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MDPI and ACS Style

Lu, F.; Zhang, B.; Hou, Y.; Xiong, X.; Dong, C.; Lu, W.; Li, L.; Lv, C. A Spatiotemporal Attention-Guided Graph Neural Network for Precise Hyperspectral Estimation of Corn Nitrogen Content. Agronomy 2025, 15, 1041. https://doi.org/10.3390/agronomy15051041

AMA Style

Lu F, Zhang B, Hou Y, Xiong X, Dong C, Lu W, Li L, Lv C. A Spatiotemporal Attention-Guided Graph Neural Network for Precise Hyperspectral Estimation of Corn Nitrogen Content. Agronomy. 2025; 15(5):1041. https://doi.org/10.3390/agronomy15051041

Chicago/Turabian Style

Lu, Feiyu, Boming Zhang, Yifei Hou, Xiao Xiong, Chaoran Dong, Wenbo Lu, Liangxue Li, and Chunli Lv. 2025. "A Spatiotemporal Attention-Guided Graph Neural Network for Precise Hyperspectral Estimation of Corn Nitrogen Content" Agronomy 15, no. 5: 1041. https://doi.org/10.3390/agronomy15051041

APA Style

Lu, F., Zhang, B., Hou, Y., Xiong, X., Dong, C., Lu, W., Li, L., & Lv, C. (2025). A Spatiotemporal Attention-Guided Graph Neural Network for Precise Hyperspectral Estimation of Corn Nitrogen Content. Agronomy, 15(5), 1041. https://doi.org/10.3390/agronomy15051041

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A Spatiotemporal Attention-Guided Graph Neural Network for Precise Hyperspectral Estimation of Corn Nitrogen Content

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