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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.10940 |
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| _version_ | 1866929759707987968 |
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| author | Waseem, Muhammad Huang, Chung-Hsuan Sajjad, Muhammad Muzzammil Naqvi, Laraib Haider Majeed, Yaqoob Rehman, Tanzeel Ur Nadeem, Tayyaba |
| author_facet | Waseem, Muhammad Huang, Chung-Hsuan Sajjad, Muhammad Muzzammil Naqvi, Laraib Haider Majeed, Yaqoob Rehman, Tanzeel Ur Nadeem, Tayyaba |
| contents | Tomato maturity plays a pivotal role in optimizing harvest timing and ensuring product quality, but current methods struggle to achieve high accuracy along computational efficiency simultaneously. Existing deep learning approaches, while accurate, are often too computationally demanding for practical use in resource-constrained agricultural settings. In contrast, simpler techniques fail to capture the nuanced features needed for precise classification. This study aims to develop a computationally efficient tomato classification model using the ResNet-18 architecture optimized through transfer learning, pruning, and quantization techniques. Our objective is to address the dual challenge of maintaining high accuracy while enabling real-time performance on low-power edge devices. Then, these models were deployed on an edge device to investigate their performance for tomato maturity classification. The quantized model achieved an accuracy of 97.81%, with an average classification time of 0.000975 seconds per image. The pruned and auto-tuned model also demonstrated significant improvements in deployment metrics, further highlighting the benefits of optimization techniques. These results underscore the potential for a balanced solution that meets the accuracy and efficiency demands of modern agricultural production, paving the way for practical, real-world deployment in resource-limited environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_10940 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automated Tomato Maturity Estimation Using an Optimized Residual Model with Pruning and Quantization Techniques Waseem, Muhammad Huang, Chung-Hsuan Sajjad, Muhammad Muzzammil Naqvi, Laraib Haider Majeed, Yaqoob Rehman, Tanzeel Ur Nadeem, Tayyaba Computer Vision and Pattern Recognition Tomato maturity plays a pivotal role in optimizing harvest timing and ensuring product quality, but current methods struggle to achieve high accuracy along computational efficiency simultaneously. Existing deep learning approaches, while accurate, are often too computationally demanding for practical use in resource-constrained agricultural settings. In contrast, simpler techniques fail to capture the nuanced features needed for precise classification. This study aims to develop a computationally efficient tomato classification model using the ResNet-18 architecture optimized through transfer learning, pruning, and quantization techniques. Our objective is to address the dual challenge of maintaining high accuracy while enabling real-time performance on low-power edge devices. Then, these models were deployed on an edge device to investigate their performance for tomato maturity classification. The quantized model achieved an accuracy of 97.81%, with an average classification time of 0.000975 seconds per image. The pruned and auto-tuned model also demonstrated significant improvements in deployment metrics, further highlighting the benefits of optimization techniques. These results underscore the potential for a balanced solution that meets the accuracy and efficiency demands of modern agricultural production, paving the way for practical, real-world deployment in resource-limited environments. |
| title | Automated Tomato Maturity Estimation Using an Optimized Residual Model with Pruning and Quantization Techniques |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.10940 |