HSDT-TabNet: A Dual-Path Deep Learning Model for Severity Grading of Soybean Frogeye Leaf Spot
Soybean frogeye leaf spot (FLS), a serious soybean disease, causes severe yield losses in the largest production regions of China. However, both conventional field monitoring and machine learning algorithms remain challenged in achieving rapid and accurate detection. In this study, an HSDT-TabNet mo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/7/1530 |
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| author | Xiaoming Li Yang Zhou Yongguang Li Shiqi Wang Wenxue Bian Hongmin Sun |
| author_facet | Xiaoming Li Yang Zhou Yongguang Li Shiqi Wang Wenxue Bian Hongmin Sun |
| author_sort | Xiaoming Li |
| collection | DOAJ |
| description | Soybean frogeye leaf spot (FLS), a serious soybean disease, causes severe yield losses in the largest production regions of China. However, both conventional field monitoring and machine learning algorithms remain challenged in achieving rapid and accurate detection. In this study, an HSDT-TabNet model was proposed for the grading of soybean FLS under field conditions by analyzing unmanned aerial vehicle (UAV)-based hyperspectral data. This model employs a dual-path parallel feature extraction strategy: the TabNet path performs sparse feature selection to capture fine-grained local discriminative information, while the hierarchical soft decision tree (HSDT) path models global nonlinear relationships across hyperspectral bands. The features from both paths are then dynamically fused via a multi-head attention mechanism to integrate complementary information. Furthermore, the overall generalization ability of the model is improved through hyperparameter optimization based on the tree-structured Parzen estimator (TPE). Experimental results show that HSDT-TabNet achieved a macro-accuracy of 96.37% under five-fold cross-validation. It outperformed the TabTransformer and SVM baselines by 2.08% and 2.23%, respectively. For high-severity cases (Level 4–5), the classification accuracy exceeded 97%. This study provides an effective method for precise field-scale crop disease monitoring. |
| format | Article |
| id | doaj-art-9b3a1b96bc5c432aa0c455cbef204391 |
| institution | DOAJ |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-9b3a1b96bc5c432aa0c455cbef2043912025-08-20T02:48:16ZengMDPI AGAgronomy2073-43952025-06-01157153010.3390/agronomy15071530HSDT-TabNet: A Dual-Path Deep Learning Model for Severity Grading of Soybean Frogeye Leaf SpotXiaoming Li0Yang Zhou1Yongguang Li2Shiqi Wang3Wenxue Bian4Hongmin Sun5College of Electrical Engineering and Information, Northeast Agricultural University, Harbin 150030, ChinaCollege of Electrical Engineering and Information, Northeast Agricultural University, Harbin 150030, ChinaCollege of Agriculture, Northeast Agricultural University, Harbin 150030, ChinaCollege of Electrical Engineering and Information, Northeast Agricultural University, Harbin 150030, ChinaCollege of Electrical Engineering and Information, Northeast Agricultural University, Harbin 150030, ChinaCollege of Electrical Engineering and Information, Northeast Agricultural University, Harbin 150030, ChinaSoybean frogeye leaf spot (FLS), a serious soybean disease, causes severe yield losses in the largest production regions of China. However, both conventional field monitoring and machine learning algorithms remain challenged in achieving rapid and accurate detection. In this study, an HSDT-TabNet model was proposed for the grading of soybean FLS under field conditions by analyzing unmanned aerial vehicle (UAV)-based hyperspectral data. This model employs a dual-path parallel feature extraction strategy: the TabNet path performs sparse feature selection to capture fine-grained local discriminative information, while the hierarchical soft decision tree (HSDT) path models global nonlinear relationships across hyperspectral bands. The features from both paths are then dynamically fused via a multi-head attention mechanism to integrate complementary information. Furthermore, the overall generalization ability of the model is improved through hyperparameter optimization based on the tree-structured Parzen estimator (TPE). Experimental results show that HSDT-TabNet achieved a macro-accuracy of 96.37% under five-fold cross-validation. It outperformed the TabTransformer and SVM baselines by 2.08% and 2.23%, respectively. For high-severity cases (Level 4–5), the classification accuracy exceeded 97%. This study provides an effective method for precise field-scale crop disease monitoring.https://www.mdpi.com/2073-4395/15/7/1530soybeanfrogeye leaf spotHSDT-TabNethyperspectral reflectanceUAV |
| spellingShingle | Xiaoming Li Yang Zhou Yongguang Li Shiqi Wang Wenxue Bian Hongmin Sun HSDT-TabNet: A Dual-Path Deep Learning Model for Severity Grading of Soybean Frogeye Leaf Spot Agronomy soybean frogeye leaf spot HSDT-TabNet hyperspectral reflectance UAV |
| title | HSDT-TabNet: A Dual-Path Deep Learning Model for Severity Grading of Soybean Frogeye Leaf Spot |
| title_full | HSDT-TabNet: A Dual-Path Deep Learning Model for Severity Grading of Soybean Frogeye Leaf Spot |
| title_fullStr | HSDT-TabNet: A Dual-Path Deep Learning Model for Severity Grading of Soybean Frogeye Leaf Spot |
| title_full_unstemmed | HSDT-TabNet: A Dual-Path Deep Learning Model for Severity Grading of Soybean Frogeye Leaf Spot |
| title_short | HSDT-TabNet: A Dual-Path Deep Learning Model for Severity Grading of Soybean Frogeye Leaf Spot |
| title_sort | hsdt tabnet a dual path deep learning model for severity grading of soybean frogeye leaf spot |
| topic | soybean frogeye leaf spot HSDT-TabNet hyperspectral reflectance UAV |
| url | https://www.mdpi.com/2073-4395/15/7/1530 |
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