AI-Driven Produce Management and Self-Checkout System for Supermarkets

Abstract

With the increasing demand for fresh and healthy food options, more and more customers are turning to purchase fruits and vegetables in supermarkets. However, the current system for purchase can be time-consuming and cumbersome, involving long queues and delays, which can be frustrating for customers. In addition to this stocking fruits, and vegetables can be a challenging task as they are prone to spoilage, requiring constant manual monitoring to keep track of the remaining quantity. This paper proposes an innovative approach using deep learning to develop a self-checkout system and also streamline the stocking process by implementing an automated system that tracks the purchases and inventory in real-time. This not only improves the overall shopping experience but also benefits the supermarkets in several ways. This helps enhance the supermarkets’ efficiency, optimizes inventory management, minimizes waste, provides data-driven insights, and improves customer satisfaction. The proposed method involves training a deep learning model to recognize and classify fruits and vegetables and automate the billing process. The produce to be purchased is automatically scanned, weighed and billed thus significantly saving not only time but also manpower involved in the traditional manual process. By utilizing the current stock details as input, the system employs deep learning algorithms to provide real-time notifications, ensuring timely restocking and minimizing stock shortages in an automated and efficient manner. The quality and variety of the dataset used to train the deep learning model is a crucial step in ensuring its accuracy, precision, and recall. The model’s performance is evaluated on set metrics to determine its effectiveness and work on its improvement. Overall, the proposed use of deep learning to improve the purchase of fruits and vegetables in supermarkets has the potential to revolutionize the way customers shop for fresh produce. This innovative approach has the potential to transform the shopping experience by reducing checkout times and meeting customer needs, giving supermarkets a competitive edge. The potential limitations of the proposed method, such as the potential for errors in recognition and classification are to be factored in for the further development of this idea.