Support Vector Machine and YOLO for a Mobile Food Grading System

TL;DR

This paper presents a mobile food grading system using SVM and YOLO v3 to classify and locate defects in bananas with high accuracy, integrating edge and cloud computing for real-time assessment.

Contribution

It introduces a novel multi-layer machine learning approach combining SVM and YOLO v3 for real-time, mobile food grading with high accuracy.

Findings

SVM achieved 98.5% accuracy in classifying banana ripeness.

YOLO v3 located peel defects with 85.7% accuracy.

Overall system accuracy reached 96.4%.

Abstract

Food quality and safety are of great concern to society since it is an essential guarantee not only for human health but also for social development, and stability. Ensuring food quality and safety is a complex process. All food processing stages should be considered, from cultivating, harvesting and storage to preparation and consumption. Grading is one of the essential processes to control food quality. This paper proposed a mobile visual-based system to evaluate food grading. Specifically, the proposed system acquires images of bananas when they are on moving conveyors. A two-layer image processing system based on machine learning is used to grade bananas, and these two layers are allocated on edge devices and cloud servers, respectively. Support Vector Machine (SVM) is the first layer to classify bananas based on an extracted feature vector composed of color and texture features. Then, the a You Only Look Once (YOLO) v3 model further locating the peel's defected area and determining if the inputs belong to the mid-ripened or well-ripened class. According to experimental results, the first layer's performance achieved an accuracy of 98.5% while the accuracy of the second layer is 85.7%, and the overall accuracy is 96.4%.