Harvesting Insights: Automated Fruit Quality Classification Using EfficientNetB5

We explore the three categories—best quality, bad quality, and mixed quality—into which fruits fit depending on their quality using eighteen classes. Fruit quality identification and classification help to ensure product consistency, reduce waste, and increase supply chain efficiency, therefore promoting responsible usage and production. Faster, more precise quality evaluation made possible by automating this process immediately affects customer happiness, lowers food loss, and advances sustainable agriculture by helping sectors to Using the EfficientNetB5 architecture—known for great performance in picture classification tasks—our approach preserves computational efficiency while yet being strong. There are 5,553 photos in the dataset utilized for this work split into 3,600 for training, 977 for validation, and 976 for testing. Using several fine-tuning methods, we obtained on the test set an amazing 99% accuracy. These findings show how well the EfficientNetB5 model can discriminate between various fruit quality levels, therefore providing a workable solution for automated fruit quality inspection in practical settings that guarantees food security and supports zero hunger by means of which.