外文摘要：At present, detection methods for rice microbial indicators are usually based on microbial culture or sensory detection methods, which are time-consuming or require expertise and thus cannot meet the needs of on-site rice testing when the rice is taken out of storage or traded. In order to develop a fast and non-destructive method for detecting rice mildew, in this paper, micro-computer vision technology is used to collect images of mildewed rice samples from 9 image locations. Then, a YOLO-V5 convolutional neural network model is used to detect moldy areas of rice, and the mold coverage area is estimated. The relationship between the moldy areas and the total number of bacterial colonies in the image is obtained. The results show that the precision and the recall of the established YOLO-v5 model in identifying the mildewed areas of rice in the validation set were 82.1% and 86.5%, respectively. Based on the mean mildewed area identified by the YOLO-v5 model, the precision and recall for light mold detection were 100% and 95.3%, respectively. The proposed method based on micro-computer vision and the YOLO convolutional neural network can be applied to the rapid detection of mildew in rice taken out of storage or traded.
外文关键词：computer vision;microscopic images;moldy rice;quick detection;YOLO convolutional neural network
作者：Sun, Ke;Tang, Mengdi;Li, Shu;Tong, Siyuan
作者单位：Anhui Normal Univ
期刊名称：FOOD SCIENCE & NUTRITION
期刊影响因子：0.0
出版年份：2024
出版刊次：12(2)
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台