Automated Assessment of Fruit Quality Using Transfer Learning and MobileNetV2

Fruit quality and health strongly affect market value, customer satisfaction, and overall farm yield. Manual inspection methods remain exceedingly labour intensive. They are fairly subjective and vulnerable to inconsistencies. This paper presents an automatic system that uses many advanced deep learning methods, specifically transfer learning via MobileNetV2, to categorize fruits by freshness or spoilage. Classical machine learning (ML) methods like Support Vector Machines, Random Forest, and K-Nearest Neighbours are comparatively analysed with modern Convolutional Neural Network (CNN) designs (like VGG16, MobileNetV1, and MobileNetV2). Many experimental evaluations reveal that MobileNetV2 not only provides substantially outstanding training accuracy but maintains consistently strong validation metrics. These findings underscore the model’s potential for deployment on mobile platforms as well as in real-time agricultural applications, thereby leading to better productivity along with reduced post-harvest losses