Sun Ke, Zhang Yu-Jie, Tong Si-Yuan, Tang Meng-Di, Wang Chang-Bao
College of Life Sciences, Anhui Normal University, Wuhu 241000, China.
College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China.
Foods. 2022 Dec 14;11(24):4031. doi: 10.3390/foods11244031.
This study aims to develop a high-speed and nondestructive mildewed rice grain detection method. First, a set of microscopic images of rice grains contaminated by , , and are acquired to serve as samples, and the mildewed regions are marked. Then, three YOLO-v5 models for identifying regions of rice grain with contamination of , , and in microscopic images are established. Finally, the relationship between the proportion of mildewed regions and the total number of colonies is analyzed. The results show that the proposed YOLO-v5 models achieve accuracy levels of 89.26%, 91.15%, and 90.19% when detecting mildewed regions with contamination of , , and in the microscopic images of the verification set. The proportion of the mildewed region area of rice grain with contamination of // is logarithmically correlated with the logarithm of the total number of colonies (). The corresponding determination coefficients are 0.7466, 0.7587, and 0.8148, respectively. This study provides a reference for future research on high-speed mildewed rice grain detection methods based on MCV technology.
本研究旨在开发一种高速且无损的霉变稻谷检测方法。首先，获取一组被[具体霉菌1]、[具体霉菌2]和[具体霉菌3]污染的稻谷微观图像作为样本，并标记出霉变区域。然后，建立三个用于识别微观图像中被[具体霉菌1]、[具体霉菌2]和[具体霉菌3]污染的稻谷区域的YOLO - v5模型。最后，分析霉变区域比例与菌落总数之间的关系。结果表明，所提出的YOLO - v5模型在检测验证集微观图像中被[具体霉菌1]、[具体霉菌2]和[具体霉菌3]污染的霉变区域时，准确率分别达到89.26%、91.15%和90.19%。被[具体霉菌1]//[具体霉菌2]//[具体霉菌3]污染的稻谷霉变区域面积比例与菌落总数（[具体数值]）的对数呈对数相关。相应的决定系数分别为0.7466、0.7587和0.8148。本研究为未来基于MCV技术的高速霉变稻谷检测方法研究提供了参考。
