A reliable and accurate diagnosis and identification system is required to prevent and manage tea leaf diseases. Tea leaf diseases are detected manually, increasing time and affecting yield quality and productivity. This study aims to present an artificial intelligence-based solution to the problem of tea leaf disease detection by training the fastest single-stage object detection model, YOLOv7, on the diseased tea leaf dataset collected from four prominent tea gardens in Bangladesh. 4000 digital images of five types of leaf diseases are collected from these tea gardens, generating a manually annotated, data-augmented leaf disease image dataset. This study incorporates data augmentation approaches to solve the issue of insufficient sample sizes. The detection and identification results for the YOLOv7 approach are validated by prominent statistical metrics like detection accuracy, precision, recall, mAP value, and F1-score, which resulted in 97.3%, 96.7%, 96.4%, 98.2%, and 0.965, respectively. Experimental results demonstrate that YOLOv7 for tea leaf diseases in natural scene images is superior to existing target detection and identification networks, including CNN, Deep CNN, DNN, AX-Retina Net, improved DCNN, YOLOv5, and Multi-objective image segmentation. Hence, this study is expected to minimize the workload of entomologists and aid in the rapid identification and detection of tea leaf diseases, thus minimizing economic losses.

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.

In Bangladesh, with the mounting esteem of bakery products, food safety issues in bakery industries are a paramount concern nowadays. In this regard, this current study was performed to evaluate food safety knowledge, attitude, and self-reported practices of two groups (160 trained and 55 new untrained) of workers from two popular baking industries in Dhaka, Bangladesh. A self-administrated questionnaire was used to acquire the data during the study. On food safety knowledge, attitude, and self-reported practices, trained workers' scores (33.01 ± 0.09, 14.86 ± 0.03, 10.66 ± 0.25, respectively) were significantly higher than the scores (9.82 ± 0.23, 10.44 ± 0.26, 5.91 ± 0.33, respectively) of newly appointed untrained workers. The quality assurance department displayed better knowledge, attitude, and self-reported practices scores than the rest of the departments of the industries. However, compared to knowledge and attitude, the self-reported practice was not up to a satisfactory level. According to the study, training can be proved effective for improving knowledge and attitude but does not always translate those into self-reported practice and behaviors. The results also reinforce the importance of conducting training for untrained workers and suggest further behavior-based food safety training for all employees.

The COVID-19, also known as a coronavirus, is currently wreaking havoc on livelihood, food security, and nutrition security around the world. In developing countries like Bangladesh the situation is far worse. The purpose of this perspective is to highlight the current state and changes of food security in Bangladesh in the context of COVID-19. During the COVID-19 period, the income of a certain set of people fell, which may have contributed to the growth in the poverty rate. It also had an impact on the agro-food systems, supply-value chain, and market levels as a result of the lockdown, movement and social gathering restrictions. The COVID-19 pandemic has an impact on the total food consumption status of the entire country, affecting all segments of the population. To obtain a greater understanding, our analysis identifies current gaps and the pandemic's potential impact from previously published works and reports.