The convenience of online shopping has revolutionized the way consumers procure goods, including groceries. However, in the domain of fresh produce, customers often face challenges in accurately visualizing and selecting items, as they rely heavily on visual cues such as color, size, and shape. Automated vegetable classification aims to address this issue by utilizing technology to assist customers in making informed choices. The primary challenge in this context is to develop a system that can accurately and swiftly classify vegetables based on visual attributes. This involves training a model to recognize and differentiate between various types of vegetables, taking into account factors like color, size, shape, and texture. Therefore, the online grocery shopping continues to grow in popularity, providing an efficient and user-friendly experience is crucial for e-commerce platforms. Automating the process of vegetable classification can enhance the accuracy and speed at which customers can select their produce, reducing the likelihood of mismatches between expectations and delivered items. This, in turn, boosts customer satisfaction and confidence in online grocery shopping. This work aims to revolutionize the online grocery shopping experience by employing advanced computer vision and machine learning techniques. By leveraging large datasets of annotated vegetable images, this research endeavors to train a model capable of accurately classifying vegetables in real-time. Through the integration of state-of-the-art algorithms, the system will provide customers with instant and precise visual cues, empowering them to confidently select their produce online. This advancement holds the potential to significantly enhance the efficiency and satisfaction of online grocery shopping, further establishing e-commerce platforms as a reliable source for fresh produce