A Hybrid Deep Learning Approach for Accurate and Efficient Classification for Vegetable Recognition

In this research work, we address the formidable challenge of automating the recognition and classification of vegetables by applying cutting-edge deep learning techniques. The manual identification of vegetables is a time-intensive process with the possibility of errors. To mitigate these challenges, we propose a hybrid deep learning approach that combines the strengths of VGG-16, VGG-19, and Inception V3 models. By leveraging the complementary features of these architectures, our hybrid model achieves enhanced accuracy in vegetable classification. Integrating multiple architectures strengthens the model's ability to discern intricate patterns and features, thereby improving its adaptability to a diverse range of vegetables, irrespective of colour, texture, and size variations. Moreover, the hybrid deep learning model exhibits remarkable efficiency, facilitating rapid processing and reducing the time-consuming nature of manual identification. Our research presents a notable advancement in vegetable recognition, offering a reliable and efficient solution for automated vegetable classification systems.