Fruit and Vegetable Identification Using Machine Learning for Retail Applications
This addresses the need for efficient and user-friendly retail checkout systems, though it is incremental as it applies existing methods to a specific domain.
The paper tackled the problem of automating fruit and vegetable identification in retail settings using image-based machine learning, resulting in a system that improved user-friendliness and reduced human-computer interactions compared to manual methods.
This paper describes an approach of creating a system identifying fruit and vegetables in the retail market using images captured with a video camera attached to the system. The system helps the customers to label desired fruits and vegetables with a price according to its weight. The purpose of the system is to minimize the number of human computer interactions, speed up the identification process and improve the usability of the graphical user interface compared to existing manual systems. The hardware of the system is constituted by a Raspberry Pi, camera, display, load cell and a case. To classify an object, different convolutional neural networks have been tested and retrained. To test the usability, a heuristic evaluation has been performed with several users, concluding that the implemented system is more user friendly compared to existing systems.