Smart classification of fruits and vegetables using CNN and machine learning

In order to improve automation in food processing, agriculture, and quality control systems, this research uses machine learning (ML) approaches to classify fruits and vegetables. To improve clarity and lower noise, we collected a large dataset of photos and used preprocessing. Even with a small amount of training data, we were able to obtain good classification accuracy by utilizing convolutional neural networks (CNNs) and utilizing transfer learning with pre-trained models like ResNet, Inception, and EfficientNet. Extensive tests showed great precision and recall utilizing both real-world situations and benchmark datasets. The system exhibits promise for use in inventory management, automated sorting, and crop monitoring, providing increased effectiveness and lower expenses.