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

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

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 o...

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Bibliographic Details
Main Authors: Feiyu Lu, Boming Zhang, Yifei Hou, Xiao Xiong, Chaoran Dong, Wenbo Lu, Liangxue Li, Chunli Lv
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Agronomy
Subjects:
hyperspectral imaging
corn nitrogen content estimation
spatiotemporal attention
graph neural networks
Online Access:https://www.mdpi.com/2073-4395/15/5/1041
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Summary: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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>=</mo><mn>0.93</mn></mrow></semantics></math></inline-formula>, 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> 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.
ISSN:2073-4395

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