Fruit Quality and Disease Detection Using Deep Learning

Abstract

In recent years, the use of Artificial Intelligence (AI) has played an important role in improving agricultural quality assessment, especially in fruit quality evaluation and disease detection. This study presents an AI-driven fruit quality and disease detection system that uses TensorFlow for efficient image classification. The proposed system classifies fruits into quality categories such as Good, Bad, Ripened, and Rotten, and fruit diseases such as Black Rot and Apple Scab. A Deep Convolutional Neural Network (DCNN)  is trained on a diverse fruit image dataset to extract key visual features such as color, texture, and shape, which helps in achieving accurate and reliable predictions. The trained model is optimized and converted into TensorFlow Lite format to reduce computational complexity and inference latency while maintaining classification accuracy, enabling fast and efficient predictions even in resource-constrained environments. The system provides an easy-to-use interface where users can input fruit images and instantly receive quality classification, disease identification, and recommended treatment measures. Overall, the proposed solution offers a cost-effective and efficient AI-based approach to fruit quality assessment, helping to minimize post-harvest losses, support timely disease management, and enhance productivity across the agricultural supply chain.