Rice disease detection method based on multi-scale dynamic feature fusion
In order to enhance the accuracy of rice leaf disease detection in complex farmland environments, and facilitate the deployment of the deep learning model onto mobile terminals for rapid real-time inference, this paper introduces a disease detection network titled YOLOv11 Multi-scale Dynamic Feature...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1543986/full |
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| author | Qian Fan Runhao Chen Bin Li |
| author_facet | Qian Fan Runhao Chen Bin Li |
| author_sort | Qian Fan |
| collection | DOAJ |
| description | In order to enhance the accuracy of rice leaf disease detection in complex farmland environments, and facilitate the deployment of the deep learning model onto mobile terminals for rapid real-time inference, this paper introduces a disease detection network titled YOLOv11 Multi-scale Dynamic Feature Fusion for Rice Disease Detection (YOLOv11-MSDFF-RiceD). The model adopts the concept of ParameterNet to design the FlexiC3k2Net module, which replaces the neck feature extraction network, thereby bolstering the model's feature learning capabilities without significantly increasing computational complexity. Additionally, an efficient multi-scale feature fusion module (EMFFM) is devised, improving both the computational efficiency and feature extraction capabilities of the model, while simultaneously reducing the number of parameters and memory footprint. The bounding box regression loss function, inner-WIoU, utilizes auxiliary bounding boxes and scale factors. Finally, the Dependency Graph (DepGraph) pruning model is employed to minimize the model's size, computational load, and parameter count, with only a moderate sacrifice in accuracy. Compared to the original YOLOv11n model, the optimized model achieves reductions in computational complexity, parameter scale, and memory usage by 50.7%, 49.6%, and 36.9%, respectively, with only a 1.7% improvement in mAP@0.5:0.9. These optimizations enable efficient deployment on resource-constrained mobile devices, making the model highly suitable for real-time disease detection in practical agricultural scenarios where hardware limitations are critical. Consequently, the improved model proposed in this paper effectively detects rice disease targets in complex environments, providing theoretical and technical support for the deployment and application of mobile terminal detection devices, such as rice disease detectors, in practical scenarios. |
| format | Article |
| id | doaj-art-eff87b4b0fc54d4d9a5cf1141cf9f0f0 |
| institution | DOAJ |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-eff87b4b0fc54d4d9a5cf1141cf9f0f02025-08-20T02:57:28ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-05-011610.3389/fpls.2025.15439861543986Rice disease detection method based on multi-scale dynamic feature fusionQian FanRunhao ChenBin LiIn order to enhance the accuracy of rice leaf disease detection in complex farmland environments, and facilitate the deployment of the deep learning model onto mobile terminals for rapid real-time inference, this paper introduces a disease detection network titled YOLOv11 Multi-scale Dynamic Feature Fusion for Rice Disease Detection (YOLOv11-MSDFF-RiceD). The model adopts the concept of ParameterNet to design the FlexiC3k2Net module, which replaces the neck feature extraction network, thereby bolstering the model's feature learning capabilities without significantly increasing computational complexity. Additionally, an efficient multi-scale feature fusion module (EMFFM) is devised, improving both the computational efficiency and feature extraction capabilities of the model, while simultaneously reducing the number of parameters and memory footprint. The bounding box regression loss function, inner-WIoU, utilizes auxiliary bounding boxes and scale factors. Finally, the Dependency Graph (DepGraph) pruning model is employed to minimize the model's size, computational load, and parameter count, with only a moderate sacrifice in accuracy. Compared to the original YOLOv11n model, the optimized model achieves reductions in computational complexity, parameter scale, and memory usage by 50.7%, 49.6%, and 36.9%, respectively, with only a 1.7% improvement in mAP@0.5:0.9. These optimizations enable efficient deployment on resource-constrained mobile devices, making the model highly suitable for real-time disease detection in practical agricultural scenarios where hardware limitations are critical. Consequently, the improved model proposed in this paper effectively detects rice disease targets in complex environments, providing theoretical and technical support for the deployment and application of mobile terminal detection devices, such as rice disease detectors, in practical scenarios.https://www.frontiersin.org/articles/10.3389/fpls.2025.1543986/fullinner-WIoUrice disease detectionmulti-scale feature fusionflexiC3k2Netdeep learning |
| spellingShingle | Qian Fan Runhao Chen Bin Li Rice disease detection method based on multi-scale dynamic feature fusion Frontiers in Plant Science inner-WIoU rice disease detection multi-scale feature fusion flexiC3k2Net deep learning |
| title | Rice disease detection method based on multi-scale dynamic feature fusion |
| title_full | Rice disease detection method based on multi-scale dynamic feature fusion |
| title_fullStr | Rice disease detection method based on multi-scale dynamic feature fusion |
| title_full_unstemmed | Rice disease detection method based on multi-scale dynamic feature fusion |
| title_short | Rice disease detection method based on multi-scale dynamic feature fusion |
| title_sort | rice disease detection method based on multi scale dynamic feature fusion |
| topic | inner-WIoU rice disease detection multi-scale feature fusion flexiC3k2Net deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1543986/full |
| work_keys_str_mv | AT qianfan ricediseasedetectionmethodbasedonmultiscaledynamicfeaturefusion AT runhaochen ricediseasedetectionmethodbasedonmultiscaledynamicfeaturefusion AT binli ricediseasedetectionmethodbasedonmultiscaledynamicfeaturefusion |