Agricultural Practices: Deep Learning for Vegetable Classification

The agricultural industry is undergoing significant advancements, yet traditional methods for classifying vegetables by attributes such as size, color, texture, and freshness remain a challenge for farmers and vendors. This paper explores the application of deep learning models to automate the classification and recognition of vegetables, aiming to enhance efficiency and innovation in agricultural practices. Deep learning-powered automated systems can handle large volumes of produce, optimizing processes like sorting, grading, and quality inspection, thereby accelerating these tasks. Additionally, such systems can improve self-checkout mechanisms in retail by accurately identifying and pricing vegetables, enhancing customer convenience and minimizing errors. Utilizing a specialized dataset of vegetable images, this study employs various deep learning models, incorporating preprocessing techniques like data augmentation and normalization. The effectiveness of these models, particularly a Convolutional Neural Network (CNN) and VGG16, is rigorously tested. The results demonstrate that the CNN model achieves superior accuracy at 92.69 percentage, compared to 87.37 percentage for VGG16, highlighting the potential of deep learning models in revolutionizing vegetable recognition and classification.