Fruit and Vegetable Grading with Transfer Learning and Convolutional Neural Networks for Better Productivity

Grading provides quality assurance by confirming that agricultural products match predetermined standards, which can boost consumer trust in the goods. Additionally, it aids in ensuring that consumers receive products that are risk-free and error-free. In this study, a fruit and vegetable grading system based on deep learning methods such as transfer learning and convolutional neural networks (CNN) is proposed. The suggested approach correctly categorizes various fruit and vegetable varieties according to their outward appearance. Objective is to ascertain the effect of various grading parameters, such as size, shape, and color, on the grading system's correctness. This study utilized labeled image dataset for training models. Transfer learning is utilized to enhance the performance and accuracy of the grading system by leveraging pre-trained CNN models. The experimental findings show that the suggested system outperforms existing fruit grading systems and achieves excellent accuracy to comprehend how grading and standardization affect farming's profitability.