FRUIT FRESHESS PREDICTION USING CNN

This Project proposes a methodology utilizing Convolutional Neural Networks (CNNs) for the holistic assessment of fruit quality, encompassing freshness, ripeness, and health status. By integrating CNNs with multi-feature analysis, including image data and spectral signatures, the model aims to provide accurate classification of fruits into categories of fresh/rotten, ripe/unripe, and healthy/diseased. The methodology outlines data collection, preprocessing, feature extraction, CNN model development, evaluation metrics, and potential applications. Ensuring fruit quality is crucial for consumer satisfaction and food safety. Manual inspection is time-consuming and subjective, necessitating automated systems. This methodology proposes a CNN-based approach for comprehensive fruit quality assessment, addressing freshness, ripeness, and health status simultaneously.