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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.12350 |
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| _version_ | 1866929504798113792 |
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| author | Rahman, Syada Tasfia Vasker, Nishat Ahammed, Amir Khabbab Hasan, Mahamudul |
| author_facet | Rahman, Syada Tasfia Vasker, Nishat Ahammed, Amir Khabbab Hasan, Mahamudul |
| contents | This study uses machine vision and drone technologies to propose a unique method for the diagnosis of cucumber disease in agriculture. The backbone of this research is a painstakingly curated dataset of hyperspectral photographs acquired under genuine field conditions. Unlike earlier datasets, this study included a wide variety of illness types, allowing for precise early-stage detection. The model achieves an excellent 87.5\% accuracy in distinguishing eight unique cucumber illnesses after considerable data augmentation. The incorporation of drone technology for high-resolution images improves disease evaluation. This development has enormous potential for improving crop management, lowering labor costs, and increasing agricultural productivity. This research, which automates disease detection, represents a significant step toward a more efficient and sustainable agricultural future. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_12350 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advancing Cucumber Disease Detection in Agriculture through Machine Vision and Drone Technology Rahman, Syada Tasfia Vasker, Nishat Ahammed, Amir Khabbab Hasan, Mahamudul Computer Vision and Pattern Recognition Artificial Intelligence This study uses machine vision and drone technologies to propose a unique method for the diagnosis of cucumber disease in agriculture. The backbone of this research is a painstakingly curated dataset of hyperspectral photographs acquired under genuine field conditions. Unlike earlier datasets, this study included a wide variety of illness types, allowing for precise early-stage detection. The model achieves an excellent 87.5\% accuracy in distinguishing eight unique cucumber illnesses after considerable data augmentation. The incorporation of drone technology for high-resolution images improves disease evaluation. This development has enormous potential for improving crop management, lowering labor costs, and increasing agricultural productivity. This research, which automates disease detection, represents a significant step toward a more efficient and sustainable agricultural future. |
| title | Advancing Cucumber Disease Detection in Agriculture through Machine Vision and Drone Technology |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2409.12350 |