LITP-GAN: Leaf image translation pipeline using DETR and GLA-Net considering the lesion area of plant disease
Abstract Quick disease diagnosis is crucial in agriculture to reduce crop yield losses. Deep learning techniques have already been successfully applied to several tasks in agriculture, but it requires sufficient datasets. Obtaining sufficient plant disease images is challenging due to seasonality an...
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Springer
2025-06-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | https://doi.org/10.1007/s44443-025-00070-x |
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| author | Ri Zheng Jimin Lee Helin Yin Dong Jin Yeong Hyeon Gu |
| author_facet | Ri Zheng Jimin Lee Helin Yin Dong Jin Yeong Hyeon Gu |
| author_sort | Ri Zheng |
| collection | DOAJ |
| description | Abstract Quick disease diagnosis is crucial in agriculture to reduce crop yield losses. Deep learning techniques have already been successfully applied to several tasks in agriculture, but it requires sufficient datasets. Obtaining sufficient plant disease images is challenging due to seasonality and a shortage of experts. Generative Adversarial Networks (GANs) can address this issue by generating synthetic images, but existing methods struggle with complex diseases like fire blight, where symptoms are localized. To overcome this problem, we propose LITP-GAN, a novel image-to-image translation pipeline considering the lesion area. LITP-GAN includes three stages: (Stage 1) Lesion area detection using DEtection TRansformer (DETR) to detect lesion areas, (Stage 2) Image translation via Global and Local Alignment Networks (GLA-Net) to generate diseased leaf images, and (Stage 3) Post-Processing using Seamless Cloning method for generating realistic images by minimizing visual inconsistencies. We tested 400 healthy leaf images, and 236 generated images were identified as realistic diseased images. Applying these to a real field plant disease detection model showed significant improvements: 7.57% in precision, 4% in recall, and 3.5% in mAP compared to models trained on the original datasets. These findings suggest that LITP-GAN-augmented datasets can improve real field plant disease diagnosis, providing a promising tool for agricultural applications. |
| format | Article |
| id | doaj-art-1c0d13d709834a0f9497a5c9932f5b50 |
| institution | Kabale University |
| issn | 1319-1578 2213-1248 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-1c0d13d709834a0f9497a5c9932f5b502025-08-20T04:02:49ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782213-12482025-06-0137412110.1007/s44443-025-00070-xLITP-GAN: Leaf image translation pipeline using DETR and GLA-Net considering the lesion area of plant diseaseRi Zheng0Jimin Lee1Helin Yin2Dong Jin3Yeong Hyeon Gu4Department of Artificial Intelligence and Data Science, College of AI Convergence, Sejong UniversityDepartment of Artificial Intelligence and Data Science, College of AI Convergence, Sejong UniversityDepartment of Artificial Intelligence and Data Science, College of AI Convergence, Sejong UniversityDepartment of Artificial Intelligence and Data Science, College of AI Convergence, Sejong UniversityDepartment of Artificial Intelligence and Data Science, College of AI Convergence, Sejong UniversityAbstract Quick disease diagnosis is crucial in agriculture to reduce crop yield losses. Deep learning techniques have already been successfully applied to several tasks in agriculture, but it requires sufficient datasets. Obtaining sufficient plant disease images is challenging due to seasonality and a shortage of experts. Generative Adversarial Networks (GANs) can address this issue by generating synthetic images, but existing methods struggle with complex diseases like fire blight, where symptoms are localized. To overcome this problem, we propose LITP-GAN, a novel image-to-image translation pipeline considering the lesion area. LITP-GAN includes three stages: (Stage 1) Lesion area detection using DEtection TRansformer (DETR) to detect lesion areas, (Stage 2) Image translation via Global and Local Alignment Networks (GLA-Net) to generate diseased leaf images, and (Stage 3) Post-Processing using Seamless Cloning method for generating realistic images by minimizing visual inconsistencies. We tested 400 healthy leaf images, and 236 generated images were identified as realistic diseased images. Applying these to a real field plant disease detection model showed significant improvements: 7.57% in precision, 4% in recall, and 3.5% in mAP compared to models trained on the original datasets. These findings suggest that LITP-GAN-augmented datasets can improve real field plant disease diagnosis, providing a promising tool for agricultural applications.https://doi.org/10.1007/s44443-025-00070-xComputer visionData augmentationDeep learningImage-to-image translationPlant disease detection |
| spellingShingle | Ri Zheng Jimin Lee Helin Yin Dong Jin Yeong Hyeon Gu LITP-GAN: Leaf image translation pipeline using DETR and GLA-Net considering the lesion area of plant disease Journal of King Saud University: Computer and Information Sciences Computer vision Data augmentation Deep learning Image-to-image translation Plant disease detection |
| title | LITP-GAN: Leaf image translation pipeline using DETR and GLA-Net considering the lesion area of plant disease |
| title_full | LITP-GAN: Leaf image translation pipeline using DETR and GLA-Net considering the lesion area of plant disease |
| title_fullStr | LITP-GAN: Leaf image translation pipeline using DETR and GLA-Net considering the lesion area of plant disease |
| title_full_unstemmed | LITP-GAN: Leaf image translation pipeline using DETR and GLA-Net considering the lesion area of plant disease |
| title_short | LITP-GAN: Leaf image translation pipeline using DETR and GLA-Net considering the lesion area of plant disease |
| title_sort | litp gan leaf image translation pipeline using detr and gla net considering the lesion area of plant disease |
| topic | Computer vision Data augmentation Deep learning Image-to-image translation Plant disease detection |
| url | https://doi.org/10.1007/s44443-025-00070-x |
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