Jubayer Fahad, Soeb Janibul Alam, Mojumder Abu Naser, Paul Mitun Kanti, Barua Pranta, Kayshar Shahidullah, Akter Syeda Sabrina, Rahman Mizanur, Islam Amirul
Department of Food Engineering and Technology, Sylhet Agricultural University, Sylhet, 3100, Bangladesh.
Department of Farm Power and Machinery, Sylhet Agricultural University, Sylhet, 3100, Bangladesh.
Curr Res Food Sci. 2021 Oct 16;4:724-728. doi: 10.1016/j.crfs.2021.10.003. eCollection 2021.
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.
该研究旨在识别生长在各种食物表面的不同霉菌。因此，我们基于“你只看一次（YOLO）v5”原则开展了一项关于食物表面霉菌检测的案例研究。在此背景下，创建了一个包含2050张表面生长有霉菌的食物图像的数据集。图像来自我们自己的实验室（850张图像）以及互联网（1200张图像）。该数据集使用预训练的YOLOv5算法进行训练。还进行了实验室测试以确认生长的生物体是霉菌。与YOLOv3和YOLOv4相比，当前的YOLOv5模型具有更好的精度、召回率和平均精度（AP），分别为98.10%、100%和99.60%。本研究首次使用YOLOv5算法检测食物表面的霉菌。总之，所提出的模型成功识别出食物表面存在的任何种类的霉菌。使用YOLOv5，我们目前正在进行研究以识别所检测到的霉菌的具体种类。
