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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2506.20323 |
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| _version_ | 1866911021667450880 |
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| author | Subramaniam, Saundarya Majumdar, Shalini Nadar, Shantanu Kulkarni, Kaustubh |
| author_facet | Subramaniam, Saundarya Majumdar, Shalini Nadar, Shantanu Kulkarni, Kaustubh |
| contents | This research presents the development of an Artificial Intelligence (AI) - driven crop disease detection system designed to assist farmers in rural areas with limited resources. We aim to compare different deep learning models for a comparative analysis, focusing on their efficacy in transfer learning. By leveraging deep learning models, including EfficientNet, ResNet101, MobileNetV2, and our custom CNN, which achieved a validation accuracy of 95.76%, the system effectively classifies plant diseases. This research demonstrates the potential of transfer learning in reshaping agricultural practices, improving crop health management, and supporting sustainable farming in rural environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20323 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comparative Analysis of Deep Learning Models for Crop Disease Detection: A Transfer Learning Approach Subramaniam, Saundarya Majumdar, Shalini Nadar, Shantanu Kulkarni, Kaustubh Machine Learning Artificial Intelligence This research presents the development of an Artificial Intelligence (AI) - driven crop disease detection system designed to assist farmers in rural areas with limited resources. We aim to compare different deep learning models for a comparative analysis, focusing on their efficacy in transfer learning. By leveraging deep learning models, including EfficientNet, ResNet101, MobileNetV2, and our custom CNN, which achieved a validation accuracy of 95.76%, the system effectively classifies plant diseases. This research demonstrates the potential of transfer learning in reshaping agricultural practices, improving crop health management, and supporting sustainable farming in rural environments. |
| title | Comparative Analysis of Deep Learning Models for Crop Disease Detection: A Transfer Learning Approach |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2506.20323 |