Detection of mold on the food surface using YOLOv5

• Published in: Current research in food science Vol. 4; pp. 724 - 728
• Main Authors: Jubayer, Fahad, Soeb, Janibul Alam, Mojumder, Abu Naser, Paul, Mitun Kanti, Barua, Pranta, Kayshar, Shahidullah, Akter, Syeda Sabrina, Rahman, Mizanur, Islam, Amirul
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2021; Elsevier
• Subjects: algorithms; case studies; data collection; Deep learning; Food spoilage; Internet; laboratory experimentation; Mold; Object detection; Short Communication; YOLOv5; Deep learning; Food spoilage; YOLOv5; Mold; Object detection
• ISSN: 2665-9271; 2665-9271
• DOI: 10.1016/j.crfs.2021.10.003

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. Images were obtained from our own laboratory (850 images) as well as from the internet (1200 images). The dataset was trained using the pre-trained YOLOv5 algorithm. A laboratory test was also performed to confirm that the grown organisms were mold. 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. [Display omitted] •First approach ever to detect the mold on food surfaces using YOLOv5.•A dataset of 2050 images was used in this purpose.•The YOLOv5 model has a higher and better performance in detecting mold on food surfaces.