AUTOMATED VEGETABLE CLASSIFICATION FOR E-COMMERCE: ENHANCING ONLINE GROCERY SHOPPING EXPERIENCES

Abstract

Online shopping's ease has revolutionized the way people buy products, especially groceries. However, because fresh food is mostly chosen online based on visual signals like color, size, and form, shoppers frequently find it difficult to choose. The goal of automated vegetable classification is to help consumers by improving their selection process using technology. Due of human subjectivity, manual image tagging and classification are time-consuming and inconsistent on traditional e-commerce platforms, and they don't scale well with rising vegetable demand. Creating a system that swiftly and reliably classifies vegetables according to their visual characteristics—that is, their color, size, shape, and texture—is the primary problem. Offering a quick and easy food shopping experience is essential as the trend of online grocery buying continues. Vegetable classification done automatically can increase customer confidence and satisfaction by decreasing mismatches between delivered items and expectations and by speeding up and improving consumer selection accuracy. With the use of cutting-edge computer vision and machine learning techniques, the project seeks to revolutionize the online grocery shopping experience. This work aims to provide accurate, real-time vegetable classification by training models on large datasets of annotated photos. Modern algorithms will be integrated into the system to deliver prompt, accurate visual signals, enabling customers to confidently choose produce online and increasing the effectiveness and happiness of online grocery shopping.