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.
The coronavirus disease 2019 (COVID-19) poses significant risks to health in the workplace for employees in the manufacturing sector of Bangladesh. A variety of preventive steps are being taken by many food industries to sustain their production during this period by ensuring food safety. In response to the current outbreak, early identification, preparedness for the growing threat, and employee well-being are of utmost importance. Food health is also a concern in this regard, as workers in the food industry remain close to food and packages. The risk of spreading the virus within the industry can be held to a minimum with timely action and concerted efforts. A study was carried out in one baking industry of Bangladesh to investigate their regular activities during the pandemic period. Until the writing of this report, there were no cases of COVID-19 among employees. Thus this case study shows how one baking industry in Bangladesh prepares and responds to the COVID 19 outbreak.
This paper investigates the obstacles to expanding and developing Bangladesh's agricultural machinery manufacturing and production industry. Due to its fertile soil and hospitable climate, Bangladesh is primarily an agricultural country where many crops are grown in large quantities. For food production to be secure and sustainable in the foreseeable future to meet future demand, mechanization is an indispensable factor. However, the agro machinery industry faces obstacles such as research facilities, financial support, qualified workers, technical support, double taxation, and storage facilities. Government policy can play a crucial role in fostering the growth and expansion of the agro-machinery industry and related communities. The article clearly describes how far away the agro machinery industry in Bangladesh is, discusses recent challenges, and encourages support for promoting mechanization throughout the country and strategies for overcoming future obstacles.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.