Automated Tomato Maturity Estimation Using an Optimized Residual Model with Pruning and Quantization Techniques
This provides a balanced solution for resource-constrained agricultural settings, though it is incremental as it applies existing optimization techniques to a specific domain.
The study tackled the problem of achieving high accuracy and computational efficiency in tomato maturity classification by optimizing a ResNet-18 model with pruning and quantization, resulting in a quantized model with 97.81% accuracy and 0.000975 seconds per image classification time.
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.