An Intelligent Food Inventory Monitoring System using Machine Learning and Computer Vision


Tianyu Li1 and Yu Sun2, 1St. George’s School, Canada, 2California State Polytechnic University, USA


Due to technological advancements, humans are able to produce more food than ever before. In fact, the food production level is so high that all population could be supported if the food resource is distributed correctly. Yet, it is more than common to see items left expiring on the supermarket shelves, wasting the food resource that could otherwise be useful. Neither are the adverse impacts on the climate due to food disposal in anyone’s favor or interest. This paper proposes an application to identify the stock status of supermarket items, specifically food items, so that supermarket managers can react to the selling status and prevent oversupply. The key tool implemented in the application is computer vision, specifically YOLOv5, which uses convolutional neural networks [1]. The model automatically recognizes and counts the items in a taken picture. We applied our computer vision model to numerous supermarket shelf photos and conducted an evaluation of the model’s precision and speed. The results show that the application is a useful tool for users to log supermarket stock information since the computer vision model, despite lacking slightly in object detection precision, can return a reliable count for well-taken photos. As a platform where such information is shared, the application is therefore a viable tool for store managers to import amounts of food accordingly and for the public to be informed and make smart buying choices.


Flutter, YOLOv5, Computer Vision, Inventory Management.

Full Text  Volume 12, Number 15