A Custom Backbone UNet Framework with DCGAN Augmentation for Efficient Segmentation of Leaf Spot Diseases in Jasmine Plant
Leaf blight spot disease, caused by bacteria and fungi, poses a considerable threat to commercial plants, manifesting as yellow to brown color spots on the leaves and potentially leading to plant mortality and reduced agricultural productivity. The susceptibility of jasmine plants to this disease em...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Journal of Computer Networks and Communications |
| Online Access: | http://dx.doi.org/10.1155/2024/5057538 |
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| author | Shwetha V. Arnav Bhagwat Vijaya Laxmi Sakshi Shrivastava |
| author_facet | Shwetha V. Arnav Bhagwat Vijaya Laxmi Sakshi Shrivastava |
| author_sort | Shwetha V. |
| collection | DOAJ |
| description | Leaf blight spot disease, caused by bacteria and fungi, poses a considerable threat to commercial plants, manifesting as yellow to brown color spots on the leaves and potentially leading to plant mortality and reduced agricultural productivity. The susceptibility of jasmine plants to this disease emphasizes the necessity for effective detection methods. In this study, we harness the power of a deep convolutional generative adversarial network (DCGAN) to generate a dataset of jasmine plant leaf disease images. Leveraging the capabilities of DCGAN, we curate a dataset comprising 10,000 images with two distinct classes specifically designed for segmentation applications. To evaluate the effectiveness of DCGAN-based generation, we propose and assess a novel loss function. For accurate segmentation of the leaf disease, we utilize a UNet architecture with a custom backbone based on the MobileNetV4 CNN. The proposed segmentation model yields an average pixel accuracy of 0.91 and an mIoU (mean intersection over union) of 0.95. Furthermore, we explore different UNet-based segmentation approaches and evaluate the performance of various backbones to assess their effectiveness. By leveraging deep learning techniques, including DCGAN for dataset generation and the UNet framework for precise segmentation, we significantly contribute to the development of effective methods for detecting and segmenting leaf diseases in jasmine plants. |
| format | Article |
| id | doaj-art-c6b1e25e0eeb4789b78183d7c95a9b05 |
| institution | Kabale University |
| issn | 2090-715X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Computer Networks and Communications |
| spelling | doaj-art-c6b1e25e0eeb4789b78183d7c95a9b052025-02-03T07:23:42ZengWileyJournal of Computer Networks and Communications2090-715X2024-01-01202410.1155/2024/5057538A Custom Backbone UNet Framework with DCGAN Augmentation for Efficient Segmentation of Leaf Spot Diseases in Jasmine PlantShwetha V.0Arnav Bhagwat1Vijaya Laxmi2Sakshi Shrivastava3Department of Electrical and Electronics EngineeringDepartment of Electrical and Electronics EngineeringDepartment of Electrical and Electronics EngineeringDepartment of Electrical and Electronics EngineeringLeaf blight spot disease, caused by bacteria and fungi, poses a considerable threat to commercial plants, manifesting as yellow to brown color spots on the leaves and potentially leading to plant mortality and reduced agricultural productivity. The susceptibility of jasmine plants to this disease emphasizes the necessity for effective detection methods. In this study, we harness the power of a deep convolutional generative adversarial network (DCGAN) to generate a dataset of jasmine plant leaf disease images. Leveraging the capabilities of DCGAN, we curate a dataset comprising 10,000 images with two distinct classes specifically designed for segmentation applications. To evaluate the effectiveness of DCGAN-based generation, we propose and assess a novel loss function. For accurate segmentation of the leaf disease, we utilize a UNet architecture with a custom backbone based on the MobileNetV4 CNN. The proposed segmentation model yields an average pixel accuracy of 0.91 and an mIoU (mean intersection over union) of 0.95. Furthermore, we explore different UNet-based segmentation approaches and evaluate the performance of various backbones to assess their effectiveness. By leveraging deep learning techniques, including DCGAN for dataset generation and the UNet framework for precise segmentation, we significantly contribute to the development of effective methods for detecting and segmenting leaf diseases in jasmine plants.http://dx.doi.org/10.1155/2024/5057538 |
| spellingShingle | Shwetha V. Arnav Bhagwat Vijaya Laxmi Sakshi Shrivastava A Custom Backbone UNet Framework with DCGAN Augmentation for Efficient Segmentation of Leaf Spot Diseases in Jasmine Plant Journal of Computer Networks and Communications |
| title | A Custom Backbone UNet Framework with DCGAN Augmentation for Efficient Segmentation of Leaf Spot Diseases in Jasmine Plant |
| title_full | A Custom Backbone UNet Framework with DCGAN Augmentation for Efficient Segmentation of Leaf Spot Diseases in Jasmine Plant |
| title_fullStr | A Custom Backbone UNet Framework with DCGAN Augmentation for Efficient Segmentation of Leaf Spot Diseases in Jasmine Plant |
| title_full_unstemmed | A Custom Backbone UNet Framework with DCGAN Augmentation for Efficient Segmentation of Leaf Spot Diseases in Jasmine Plant |
| title_short | A Custom Backbone UNet Framework with DCGAN Augmentation for Efficient Segmentation of Leaf Spot Diseases in Jasmine Plant |
| title_sort | custom backbone unet framework with dcgan augmentation for efficient segmentation of leaf spot diseases in jasmine plant |
| url | http://dx.doi.org/10.1155/2024/5057538 |
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