Deep Learning-Based Fruit and Vegetable Quality Grading Using Computer Vision: A Technical Review of Recent Advances, Challenges, and Future Directions

Fruit and vegetable quality grading is an important postharvest activity that affects consumer acceptance, market value, shelf life, food safety, and supply-chain efficiency. Conventional grading commonly depends on manual inspection, which can be labor-intensive, time-consuming, subjective, and difficult to scale. Recent developments in computer vision and deep learning enable automated analysis of visual characteristics such as color, size, shape, texture, ripeness, freshness, and surface defects. This paper presents a technical review of deep learning-based approaches for fruit and vegetable quality grading, with emphasis on convolutional neural networks, transfer learning, object detection, attention mechanisms, vision transformers, few-shot learning, and multimodal sensing. Recent literature is synthesized with respect to datasets, image acquisition, preprocessing, model architectures, evaluation metrics, and deployment considerations. Particular attention is given to the gap between benchmark performance and reliable real-world deployment under varying illumination, occlusion, cultivar diversity, domain shift, limited labeled data, and computational constraints. Based on this synthesis, an end-to-end implementation framework is proposed for image acquisition, preprocessing, detection or segmentation, feature learning, quality prediction, grade assignment, confidence estimation, and reporting. The manuscript is a technical review and does not claim original experimental results; experimental validation is identified as future implementation work.