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. Preprints2021, 2021050679. https://doi.org/10.20944/preprints202105.0679.v1
APA Style
Jubayer, M.F., Soeb, M.J.A., Paul, M.K., Barua, P., Kayshar, M.S., Rahman, M.M., & Islam, M.A. (2021). Mold Detection on Food Surfaces Using YOLOv5. Preprints. https://doi.org/10.20944/preprints202105.0679.v1
Chicago/Turabian Style
Jubayer, M.F., Md. M. Rahman and Md. A. Islam. 2021 "Mold Detection on Food Surfaces Using YOLOv5" Preprints. https://doi.org/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.
Keywords
YOLOv5; object detection; mold; food spoilage; deep learning.
Subject
Biology and Life Sciences, Immunology and Microbiology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
