UAV rice panicle blast detection based on enhanced feature representation and optimized attention mechanism
Abstract Background Rice blast is one of the most destructive diseases in rice cultivation, significantly threatening global food security. Timely and precise detection of rice panicle blast is crucial for effective disease management and prevention of crop losses. This study introduces ConvGAM, a n...
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| Format: | Article |
| Language: | English |
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BMC
2025-02-01
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| Series: | Plant Methods |
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| Online Access: | https://doi.org/10.1186/s13007-025-01333-4 |
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| author | Shaodan Lin Deyao Huang Libin Wu Zuxin Cheng Dapeng Ye Haiyong Weng |
| author_facet | Shaodan Lin Deyao Huang Libin Wu Zuxin Cheng Dapeng Ye Haiyong Weng |
| author_sort | Shaodan Lin |
| collection | DOAJ |
| description | Abstract Background Rice blast is one of the most destructive diseases in rice cultivation, significantly threatening global food security. Timely and precise detection of rice panicle blast is crucial for effective disease management and prevention of crop losses. This study introduces ConvGAM, a novel semantic segmentation model leveraging the ConvNeXt-Large backbone network and the Global Attention Mechanism (GAM). This design aims to enhance feature extraction and focus on critical image regions, addressing the challenges of detecting small and complex disease patterns in UAV-captured imagery. Furthermore, the model incorporates advanced loss functions to handle data imbalances effectively, supporting accurate classification across diverse disease severities. Results The ConvGAM model, leveraging the ConvNeXt-Large backbone network and the Global Attention Mechanism (GAM), achieves outstanding performance in feature extraction, crucial for detecting small and complex disease patterns. Quantitative evaluation demonstrates that the model achieves an overall accuracy of 91.4%, a mean IoU of 79%, and an F1 score of 82% on the test set. The incorporation of Focal Tversky Loss further enhances the model's ability to handle imbalanced datasets, improving detection accuracy for rare and severe disease categories. Correlation coefficient analysis across disease severity levels indicates high consistency between predictions and ground truth, with values ranging from 0.962 to 0.993. These results confirm the model’s reliability and robustness, highlighting its effectiveness in rice panicle blast detection under challenging conditions. Conclusion The ConvGAM model demonstrates strong qualitative advantages in detecting rice panicle blast disease. By integrating advanced feature extraction with the ConvNeXt-Large backbone and GAM, the model achieves precise detection and classification across varying disease severities. The use of Focal Tversky Loss ensures robustness against dataset imbalances, enabling accurate identification of rare disease categories. Despite these strengths, future efforts should focus on improving classification accuracy and adapting the model to diverse environmental conditions. Additionally, optimizing model parameters and exploring advanced data augmentation techniques could further enhance its detection capabilities and expand its applicability to broader agricultural scenarios. |
| format | Article |
| id | doaj-art-ef1dcf6538594609bf90165c810c0d39 |
| institution | Kabale University |
| issn | 1746-4811 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Plant Methods |
| spelling | doaj-art-ef1dcf6538594609bf90165c810c0d392025-02-09T12:38:42ZengBMCPlant Methods1746-48112025-02-0121112110.1186/s13007-025-01333-4UAV rice panicle blast detection based on enhanced feature representation and optimized attention mechanismShaodan Lin0Deyao Huang1Libin Wu2Zuxin Cheng3Dapeng Ye4Haiyong Weng5College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry UniversityCollege of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry UniversityCollege of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry UniversityCollege of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry UniversityCollege of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry UniversityCollege of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry UniversityAbstract Background Rice blast is one of the most destructive diseases in rice cultivation, significantly threatening global food security. Timely and precise detection of rice panicle blast is crucial for effective disease management and prevention of crop losses. This study introduces ConvGAM, a novel semantic segmentation model leveraging the ConvNeXt-Large backbone network and the Global Attention Mechanism (GAM). This design aims to enhance feature extraction and focus on critical image regions, addressing the challenges of detecting small and complex disease patterns in UAV-captured imagery. Furthermore, the model incorporates advanced loss functions to handle data imbalances effectively, supporting accurate classification across diverse disease severities. Results The ConvGAM model, leveraging the ConvNeXt-Large backbone network and the Global Attention Mechanism (GAM), achieves outstanding performance in feature extraction, crucial for detecting small and complex disease patterns. Quantitative evaluation demonstrates that the model achieves an overall accuracy of 91.4%, a mean IoU of 79%, and an F1 score of 82% on the test set. The incorporation of Focal Tversky Loss further enhances the model's ability to handle imbalanced datasets, improving detection accuracy for rare and severe disease categories. Correlation coefficient analysis across disease severity levels indicates high consistency between predictions and ground truth, with values ranging from 0.962 to 0.993. These results confirm the model’s reliability and robustness, highlighting its effectiveness in rice panicle blast detection under challenging conditions. Conclusion The ConvGAM model demonstrates strong qualitative advantages in detecting rice panicle blast disease. By integrating advanced feature extraction with the ConvNeXt-Large backbone and GAM, the model achieves precise detection and classification across varying disease severities. The use of Focal Tversky Loss ensures robustness against dataset imbalances, enabling accurate identification of rare disease categories. Despite these strengths, future efforts should focus on improving classification accuracy and adapting the model to diverse environmental conditions. Additionally, optimizing model parameters and exploring advanced data augmentation techniques could further enhance its detection capabilities and expand its applicability to broader agricultural scenarios.https://doi.org/10.1186/s13007-025-01333-4Rice blastSemantic segmentationConvNeXtGAMFocalTverskyLoss |
| spellingShingle | Shaodan Lin Deyao Huang Libin Wu Zuxin Cheng Dapeng Ye Haiyong Weng UAV rice panicle blast detection based on enhanced feature representation and optimized attention mechanism Plant Methods Rice blast Semantic segmentation ConvNeXt GAM FocalTverskyLoss |
| title | UAV rice panicle blast detection based on enhanced feature representation and optimized attention mechanism |
| title_full | UAV rice panicle blast detection based on enhanced feature representation and optimized attention mechanism |
| title_fullStr | UAV rice panicle blast detection based on enhanced feature representation and optimized attention mechanism |
| title_full_unstemmed | UAV rice panicle blast detection based on enhanced feature representation and optimized attention mechanism |
| title_short | UAV rice panicle blast detection based on enhanced feature representation and optimized attention mechanism |
| title_sort | uav rice panicle blast detection based on enhanced feature representation and optimized attention mechanism |
| topic | Rice blast Semantic segmentation ConvNeXt GAM FocalTverskyLoss |
| url | https://doi.org/10.1186/s13007-025-01333-4 |
| work_keys_str_mv | AT shaodanlin uavricepanicleblastdetectionbasedonenhancedfeaturerepresentationandoptimizedattentionmechanism AT deyaohuang uavricepanicleblastdetectionbasedonenhancedfeaturerepresentationandoptimizedattentionmechanism AT libinwu uavricepanicleblastdetectionbasedonenhancedfeaturerepresentationandoptimizedattentionmechanism AT zuxincheng uavricepanicleblastdetectionbasedonenhancedfeaturerepresentationandoptimizedattentionmechanism AT dapengye uavricepanicleblastdetectionbasedonenhancedfeaturerepresentationandoptimizedattentionmechanism AT haiyongweng uavricepanicleblastdetectionbasedonenhancedfeaturerepresentationandoptimizedattentionmechanism |