The accurate classification of vegetables based on image data is a critical task with significant implications for agricultural au- tomation, supply chain management, and consumer applications. However, this task is fraught with challenges due to the inherent variability in vegetable size, shape, color, and texture, which complicates the development of robust classification models. To ad- dress these challenges, this study proposes a Convolutional Neural Network (CNN) tailored for vegetable classification across 15 categories. The model leverages a dataset of 21,000 images, incorporating advanced techniques to enhance feature extraction and generalization. The proposed CNN is evaluated using metrics such as accuracy, precision, F1-score and recall. Experimental re- sults indicate that the model achieves high performance across all metrics, demonstrating its potential for integration into automated sorting systems and mobile applications for farmers. This work not only advances the state-of-the-art in vegetable classification but also highlights the societal benefits of improving accuracy in agricultural technologies.