Fruit and Vegetable Identification Using Machine Learning for Retail Applications

TL;DR

This paper presents a machine learning-based system using CNNs on Raspberry Pi to identify fruits and vegetables in retail, aiming to enhance speed, usability, and reduce manual effort.

Contribution

It introduces a practical retail identification system with CNN-based classification and usability improvements over existing manual methods.

Findings

System achieves faster identification process.

User usability is significantly improved.

CNN models effectively classify fruits and vegetables.

Abstract

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.