TomaFDNet: A multiscale focused diffusion-based model for tomato disease detection
IntroductionTomatoes are one of the most economically significant crops worldwide, with their yield and quality heavily impacted by foliar diseases. Effective detection of these diseases is essential for enhancing agricultural productivity and mitigating economic losses. Current tomato leaf disease...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1530070/full |
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| author | Rijun Wang Rijun Wang Yesheng Chen Fulong Liang Xiangwei Mou Xiangwei Mou Guanghao Zhang Hao Jin |
| author_facet | Rijun Wang Rijun Wang Yesheng Chen Fulong Liang Xiangwei Mou Xiangwei Mou Guanghao Zhang Hao Jin |
| author_sort | Rijun Wang |
| collection | DOAJ |
| description | IntroductionTomatoes are one of the most economically significant crops worldwide, with their yield and quality heavily impacted by foliar diseases. Effective detection of these diseases is essential for enhancing agricultural productivity and mitigating economic losses. Current tomato leaf disease detection methods, however, encounter challenges in extracting multi-scale features, identifying small targets, and mitigating complex background interference. MethodsThe multi-scale tomato leaf disease detection model Tomato Focus-Diffusion Network (TomaFDNet) was proposed to solve the above problems. The model utilizes a multi-scale focus-diffusion network (MSFDNet) alongside an efficient parallel multi-scale convolutional module (EPMSC) to significantly enhance the extraction of multi-scale features. This combination particularly strengthens the model's capability to detect small targets amidst complex backgrounds. Results and DiscussionExperimental results show that TomaFDNet reaches a mean average precision (mAP) of 83.1% in detecting Early_blight, Late_blight, and Leaf_Mold on tomato leaves, outperforming classical object detection algorithms, including Faster R-CNN (mAP = 68.2%) and You Only Look Once (YOLO) series (v5: mAP = 75.5%, v7: mAP = 78.3%, v8: mAP = 78.9%, v9: mAP = 79%, v10: mAP = 77.5%, v11: mAP = 79.2%). Compared to the baseline YOLOv8 model, TomaFDNet achieves a 4.2% improvement in mAP, which is statistically significant (P < 0.01). These findings indicate that TomaFDNet offers a valid solution to the precise detection of tomato leaf diseases. |
| format | Article |
| id | doaj-art-d9c93b2808aa4b85b7a332f5e978c3f6 |
| institution | OA Journals |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-d9c93b2808aa4b85b7a332f5e978c3f62025-08-20T02:12:38ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-04-011610.3389/fpls.2025.15300701530070TomaFDNet: A multiscale focused diffusion-based model for tomato disease detectionRijun Wang0Rijun Wang1Yesheng Chen2Fulong Liang3Xiangwei Mou4Xiangwei Mou5Guanghao Zhang6Hao Jin7School of Teachers College for Vocational Education, Guangxi Normal University, Guilin, ChinaGuangxi University Engineering Research Center of Agricultural and Forestry Intelligent Equipment Technology, Guangxi Normal University, Guilin, ChinaSchool of Teachers College for Vocational Education, Guangxi Normal University, Guilin, ChinaSchool of Teachers College for Vocational Education, Guangxi Normal University, Guilin, ChinaSchool of Teachers College for Vocational Education, Guangxi Normal University, Guilin, ChinaGuangxi University Engineering Research Center of Agricultural and Forestry Intelligent Equipment Technology, Guangxi Normal University, Guilin, ChinaSchool of Teachers College for Vocational Education, Guangxi Normal University, Guilin, ChinaGuangxi University Engineering Research Center of Agricultural and Forestry Intelligent Equipment Technology, Guangxi Normal University, Guilin, ChinaIntroductionTomatoes are one of the most economically significant crops worldwide, with their yield and quality heavily impacted by foliar diseases. Effective detection of these diseases is essential for enhancing agricultural productivity and mitigating economic losses. Current tomato leaf disease detection methods, however, encounter challenges in extracting multi-scale features, identifying small targets, and mitigating complex background interference. MethodsThe multi-scale tomato leaf disease detection model Tomato Focus-Diffusion Network (TomaFDNet) was proposed to solve the above problems. The model utilizes a multi-scale focus-diffusion network (MSFDNet) alongside an efficient parallel multi-scale convolutional module (EPMSC) to significantly enhance the extraction of multi-scale features. This combination particularly strengthens the model's capability to detect small targets amidst complex backgrounds. Results and DiscussionExperimental results show that TomaFDNet reaches a mean average precision (mAP) of 83.1% in detecting Early_blight, Late_blight, and Leaf_Mold on tomato leaves, outperforming classical object detection algorithms, including Faster R-CNN (mAP = 68.2%) and You Only Look Once (YOLO) series (v5: mAP = 75.5%, v7: mAP = 78.3%, v8: mAP = 78.9%, v9: mAP = 79%, v10: mAP = 77.5%, v11: mAP = 79.2%). Compared to the baseline YOLOv8 model, TomaFDNet achieves a 4.2% improvement in mAP, which is statistically significant (P < 0.01). These findings indicate that TomaFDNet offers a valid solution to the precise detection of tomato leaf diseases.https://www.frontiersin.org/articles/10.3389/fpls.2025.1530070/fulldeep learningMSFDNetobjection detectiontomato diseaseEPMSC |
| spellingShingle | Rijun Wang Rijun Wang Yesheng Chen Fulong Liang Xiangwei Mou Xiangwei Mou Guanghao Zhang Hao Jin TomaFDNet: A multiscale focused diffusion-based model for tomato disease detection Frontiers in Plant Science deep learning MSFDNet objection detection tomato disease EPMSC |
| title | TomaFDNet: A multiscale focused diffusion-based model for tomato disease detection |
| title_full | TomaFDNet: A multiscale focused diffusion-based model for tomato disease detection |
| title_fullStr | TomaFDNet: A multiscale focused diffusion-based model for tomato disease detection |
| title_full_unstemmed | TomaFDNet: A multiscale focused diffusion-based model for tomato disease detection |
| title_short | TomaFDNet: A multiscale focused diffusion-based model for tomato disease detection |
| title_sort | tomafdnet a multiscale focused diffusion based model for tomato disease detection |
| topic | deep learning MSFDNet objection detection tomato disease EPMSC |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1530070/full |
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