Preprint Communication Version 1 Preserved in Portico This version is not peer-reviewed

Mold Detection on Food Surfaces Using YOLOv5

Version 1 : Received: 25 May 2021 / Approved: 27 May 2021 / Online: 27 May 2021 (14:14:20 CEST)

How to cite: Jubayer, M.F.; Soeb, M.J.A.; Paul, M.K.; Barua, P.; Kayshar, M.S.; Rahman, M.M.; Islam, M.A. Mold Detection on Food Surfaces Using YOLOv5. Preprints 2021, 2021050679 (doi: 10.20944/preprints202105.0679.v1). Jubayer, M.F.; Soeb, M.J.A.; Paul, M.K.; Barua, P.; Kayshar, M.S.; Rahman, M.M.; Islam, M.A. Mold Detection on Food Surfaces Using YOLOv5. Preprints 2021, 2021050679 (doi: 10.20944/preprints202105.0679.v1).

Abstract

The study aimed to identify different molds that grow on various food surfaces. As a result, we conducted a case study for the detection of mold on food surfaces based on the “you only look once (YOLO) v5” principle. In this context, a dataset of 2050 food images with mold growing on their surfaces was created. The dataset was trained using the pre-trained YOLOv5 algorithm. In comparison to YOLOv3 and YOLOv4, this current YOLOv5 model had better precision, recall, and average precision (AP), which were 98.10%, 100%, and 99.60%, respectively. The YOLOv5 algorithm was used for the first time in this study to detect mold on food surfaces. In conclusion, the proposed model successfully recognizes any kind of mold present on the food surface. Using YOLOv5, we are currently conducting research to identify the specific species of the detected mold.

Subject Areas

YOLOv5; object detection; mold; food spoilage; deep learning.

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.