Enhancing Taiwan Guava Grading through Advanced Image Processing and Deep Learning Techniques

Grading unique fruits like Taiwan guavas has historically relied heavily on specialized human examination of visual and textural characteristics. This dependence on inconsistent manual inspection incurs massive inefficiencies across guava harvesting, sorting, sales and processing operations. The lack of rapid, precise and standardized grading practices results in improper quality evaluation, revenue losses for farmers and inadequate transparency for consumers regarding produce freshness and safety. This pioneering research aims to overcome these challenges by developing an advanced computer vision system tailored to automate the visual grading process for Taiwan guavas. A large and diverse dataset of guava images representing natural variations in shape, size, color, texture and defects is systematically compiled. This dataset is leveraged to optimize a state-of-the-art deep neural network model using techniques like transfer learning and data augmentation. Comprehensive benchmarking experiments demonstrate that the optimized model achieves 89% test accuracy in classifying guavas into industry-standard grades based on quality parameters learned from visual cues. The model operates with high precision and recall, with minimal confusion between adjacent grades. The proposed computer vision-based solution can potentially bring immense benefits in efficiency, consistency, transparency, sustainability and value across guava harvesting, sorting, logistics and sales. Automated grading ensures only premium quality guavas reach consumers, thereby improving farmer incomes and customer satisfaction.