Статья

Detection of mold on the food surface using YOLOv5

Md. Fahad JubayerDepartment of Food Engineering and Technology, Sylhet Agricultural University, Sylhet, 3100, BangladeshMd. Janibul Alam SoebDepartment of Farm Power and Machinery, Sylhet Agricultural University, Sylhet, 3100, BangladeshAbu Naser MojumderDepartment of Computer Science and Engineering, Sylhet Engineering College, Sylhet, 3100, BangladeshMitun Kanti PaulDepartment of Electrical and Electronic Engineering, Sylhet Engineering College, Sylhet, 3100, BangladeshPranta BaruaDepartment of Electrical and Electronic Engineering, Sylhet Engineering College, Sylhet, 3100, BangladeshShahidullah KaysharDepartment of Food Engineering and Technology, Sylhet Agricultural University, Sylhet, 3100, BangladeshSyeda Sabrina AkterDepartment of Food Engineering and Technology, Sylhet Agricultural University, Sylhet, 3100, BangladeshMizanur RahmanDepartment of Farm Power and Machinery, Sylhet Agricultural University, Sylhet, 3100, BangladeshAmirul IslamDepartment of Farm Power and Machinery, Sylhet Agricultural University, Sylhet, 3100, Bangladesh

2021en

ABI

Аннотация

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.

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Идентификаторы

DOI: 10.1016/j.crfs.2021.10.003

Цитирования и источники

Цитирований: 2Использованных источников: 0

Показатели — AkademScholar