Smart Agriculture: AI-based System for Classification of Fresh, Rotten, and Formalin Fruits

AI-based System for Classification of Fruits

Abstract

Ensuring the freshness and safety of fruits and vegetables is critical for protecting public health, minimizing food waste, and enhancing supply chain efficiency. Traditionally, the assessment of produce quality relies on manual visual inspection, which is time-consuming, labor-intensive, and prone to errors. Recent advances in artificial intelligence have opened opportunities for automating this process, offering faster, more accurate, and scalable solutions. In this work, we propose a lightweight Convolutional Neural Network (CNN) model designed to classify fruits into three categories based on freshness: fresh, rotten, and formalin-treated. The model architecture integrates four convolutional layers with regularization techniques, enabling efficient feature extraction while maintaining low computational cost. We conducted experiments on the peer-reviewed Fruit Vision dataset, consisting of 10,154 images across 15 fruit classes. The proposed CNN achieved a test accuracy of 92.19%, with training and validation accuracies of 94.10% and 92.84%, respectively, and an F1-score of 0.93, outperforming several pretrained baseline models such as VGG16, InceptionV3, and MobileNetV2. These results demonstrate the feasibility of applying deep learning for automated fruit quality inspection. Our approach offers a reliable and efficient framework that can contribute to enhanced food safety, reduced waste, and more intelligent supply chain management.