Automated Fruit and Vegetable Classification with Convolutional Neural Networks: A Comprehensive Study

Accurate classification of fruits and vegetables is absolutely essential for both agricultural efficiency and quality control. This work presents a novel method using Convolutional Neural Networks to improve fruit and vegetable classification from images. In the proposed CNN architecture, several convolutional, pooling, and thick layers enable the model to effectively record and investigate complex visual characteristics. The model is trained on a diversified dataset consisting of thirty categories of fruits and vegetables using 100 photographs per category for training and 10 images per category for testing and validation. By ensuring stability and hence reducing overfitting, batch normalisation and dropout layers together optimize the performance of the model. Despite its scalability and self-sufficient feature learning possibilities, this CNN-based method provides robustness and accuracy above more conventional techniques. Establishing a new bar for fruit and vegetable classification, the model reveals over all categories excellent accuracy, recall, and F1-scores. Expanding the dataset, including transfer learning approaches, and optimizing the model for real-time, mobile apps will be the main priorities of next improvements. With an overall accuracy of 97%, this study emphasizes the transforming power of deep learning technologies on agricultural classification, therefore highlighting the efficiency and pragmatic importance of the model.