Image-Based Fruit and Vegetable Classification Using CNN Techniques

This paper introduces a system for identifying fruits and vegetables through a Convolutional Neural Network (CNN). The model is trained using a dataset that contains images of different fruits and vegetables, organized into sets for training, testing, and validation. It includes 36 categories, such as bananas, apples, tomatoes, and carrots. Once trained, the CNN model accurately determines whether an image represents a fruit or a vegetable. The system is deployed via Streamlit, allowing users to upload images and obtain classification results in real-time. This project highlights CNNs' potential in food identification, offering a dependable and user-friendly solution for recognizing ingredients across various applications.