SUNet: Coffee Leaf Disease Detection Using Hybrid Deep Learning Model
Leaf mining, rust, bacterial blight, and berry pathology are major diseases in coffee plants. These diseases not only reduce yield but also affect quality. Early detection and targeted treatment are crucial to mitigate their effects. This paper introduces an efficient hybrid deep learning model, SUN...
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| Format: | Article |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10707607/ |
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| author | Deepak Thakur Tanya Gera Ambika Aggarwal Madhushi Verma Manjit Kaur Dilbag Singh Mohammed Amoon |
| author_facet | Deepak Thakur Tanya Gera Ambika Aggarwal Madhushi Verma Manjit Kaur Dilbag Singh Mohammed Amoon |
| author_sort | Deepak Thakur |
| collection | DOAJ |
| description | Leaf mining, rust, bacterial blight, and berry pathology are major diseases in coffee plants. These diseases not only reduce yield but also affect quality. Early detection and targeted treatment are crucial to mitigate their effects. This paper introduces an efficient hybrid deep learning model, SUNet, for prediction and classification of healthy and diseased coffee leaves. SUNet integrates U-Net with Segnet’s encoding system, using VGG16 for robust feature extraction. A decoder with skip connections is used to preserve spatial details. Mask R-CNN is also employed for instance segmentation, accurately localizing disease spots. A pyramid pooling module captures multi-scale contextual information. The model is tested using two benchmark datasets, JMuBEN and JMuBEN2. These datasets contain a wide range of coffee leaves affected by phoma, cercospora, or rust, along with healthy samples. SUNet achieved significant performance improvement over other models in terms of accuracy, Intersection over Union (IoU), F1-score, precision, and recall by 1.22%, 1.21%, 1.17%, 1.19%, and 1.24%, respectively. These improvements demonstrate that SUNet can be used for the early detection and classification of coffee leaf diseases. Therefore, with precise and timely interventions, SUNet can help farmers minimize crop losses, enhance coffee production quality, and reduce reliance on harmful chemical treatments. |
| format | Article |
| id | doaj-art-469bd73a551e409fb451dd5437b0362d |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-469bd73a551e409fb451dd5437b0362d2025-08-20T01:47:58ZengIEEEIEEE Access2169-35362024-01-011214917314919110.1109/ACCESS.2024.347621110707607SUNet: Coffee Leaf Disease Detection Using Hybrid Deep Learning ModelDeepak Thakur0Tanya Gera1Ambika Aggarwal2https://orcid.org/0000-0002-6005-2879Madhushi Verma3https://orcid.org/0000-0003-1996-2077Manjit Kaur4https://orcid.org/0000-0001-6259-2046Dilbag Singh5Mohammed Amoon6https://orcid.org/0000-0002-3212-8098Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, IndiaChitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, IndiaDepartment of Computer Science and Engineering, UPES, Dehradun, IndiaSchool of Computer Science Engineering and Technology, Bennett University, Greater Noida, IndiaSchool of Computer Science and Artificial Intelligence, SR University, Warangal, Telangana, IndiaResearch and Development Cell, Lovely Professional University, Phagwara, Punjab, IndiaDepartment of Computer Science, Community College, King Saud University, Riyadh, Saudi ArabiaLeaf mining, rust, bacterial blight, and berry pathology are major diseases in coffee plants. These diseases not only reduce yield but also affect quality. Early detection and targeted treatment are crucial to mitigate their effects. This paper introduces an efficient hybrid deep learning model, SUNet, for prediction and classification of healthy and diseased coffee leaves. SUNet integrates U-Net with Segnet’s encoding system, using VGG16 for robust feature extraction. A decoder with skip connections is used to preserve spatial details. Mask R-CNN is also employed for instance segmentation, accurately localizing disease spots. A pyramid pooling module captures multi-scale contextual information. The model is tested using two benchmark datasets, JMuBEN and JMuBEN2. These datasets contain a wide range of coffee leaves affected by phoma, cercospora, or rust, along with healthy samples. SUNet achieved significant performance improvement over other models in terms of accuracy, Intersection over Union (IoU), F1-score, precision, and recall by 1.22%, 1.21%, 1.17%, 1.19%, and 1.24%, respectively. These improvements demonstrate that SUNet can be used for the early detection and classification of coffee leaf diseases. Therefore, with precise and timely interventions, SUNet can help farmers minimize crop losses, enhance coffee production quality, and reduce reliance on harmful chemical treatments.https://ieeexplore.ieee.org/document/10707607/Agricultural applicationscoffee leaf diseasedeep learningsemantic segmentationSUNetSegNet |
| spellingShingle | Deepak Thakur Tanya Gera Ambika Aggarwal Madhushi Verma Manjit Kaur Dilbag Singh Mohammed Amoon SUNet: Coffee Leaf Disease Detection Using Hybrid Deep Learning Model IEEE Access Agricultural applications coffee leaf disease deep learning semantic segmentation SUNet SegNet |
| title | SUNet: Coffee Leaf Disease Detection Using Hybrid Deep Learning Model |
| title_full | SUNet: Coffee Leaf Disease Detection Using Hybrid Deep Learning Model |
| title_fullStr | SUNet: Coffee Leaf Disease Detection Using Hybrid Deep Learning Model |
| title_full_unstemmed | SUNet: Coffee Leaf Disease Detection Using Hybrid Deep Learning Model |
| title_short | SUNet: Coffee Leaf Disease Detection Using Hybrid Deep Learning Model |
| title_sort | sunet coffee leaf disease detection using hybrid deep learning model |
| topic | Agricultural applications coffee leaf disease deep learning semantic segmentation SUNet SegNet |
| url | https://ieeexplore.ieee.org/document/10707607/ |
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