  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%.
