Study on Rice Grain Mildewed Region Recognition Based on Microscopic Computer Vision and YOLO-v5 Model

Abstract

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 Aspergillus niger, Penicillium citrinum, and Aspergillus cinerea 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 Aspergillus niger, Penicillium citrinum, and Aspergillus cinerea 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 Aspergillus niger, Penicillium citrinum, and Aspergillus cinerea in the microscopic images of the verification set. The proportion of the mildewed region area of rice grain with contamination of Aspergillus niger/Penicillium citrinum/Aspergillus cinerea is logarithmically correlated with the logarithm of the total number of colonies (TVC). 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.

Mildew detection in rice grains based on computer vision and the YOLO convolutional neural network

“…Compared with the study by Sun et al (2022), the detection target for the experiment in this study was 10 g of rice rather than single grains of rice. By identifying and analyzing the mildewed areas of grouped samples, the obtained results can play a more effective role in practical applications of mold detection in large batches of rice.…”

“…( 2016 ) compared the effect of traditional machine learning and the LeNet5 CNN on image recognition of mildewed rice, and the results showed that the early LeNet5 deep learning algorithm had a great advantage in recognition speed and accuracy. However, conventional computer vision methods are unable to detect rice samples with mild mildew contamination, and it is difficult to detect mildly mildewed grains with a total viable bacterial count (TVC) of 10 5 –10 6 CFU/g using methods at conventional observational scales (Sun et al., 2022 ; Zhou et al., 2008 ). Sun et al.…”

“…( 2022 ) used micro‐computer vision (MCV) technology to improve the sensitivity to mildew features and used an advanced YOLO CNN to recognize the location of A. niger , Penicillium orange , and Aspergillus griseus on single rice grains with an accuracy of 89.26%, 91.15%, and 90.19%, respectively. Conventional computer vision methods can directly detect obvious bacterial colonies in mildewed rice images; however, bacterial colonies of mildly mildewed rice are small, and MCV techniques are required for feature extraction (Barsanti et al., 2022 ; Sun et al., 2022 ). At present, detection methods for rice mold based on MCV only realize the detection of a single kind of mold on isolated rice grains.…”

“…In their work using spectroscopy and hyperspectral imaging, Cong et al (2018) achieved non-destructive detection of the total number of mold colonies in rice using a model combining gray wolf optimization and support vector regression. Jiang et al (2018) However, conventional computer vision methods are unable to detect rice samples with mild mildew contamination, and it is difficult to detect mildly mildewed grains with a total viable bacterial count (TVC) of 10 5 -10 6 CFU/g using methods at conventional observational scales (Sun et al, 2022;Zhou et al, 2008). Sun et al (2022) used micro-computer vision (MCV) technology to improve the sensitivity to mildew features and used an advanced YOLO CNN to recognize the location of A. niger, Penicillium orange, and Aspergillus griseus on single rice grains with an accuracy of 89.26%, 91.15%, and 90.19%, respectively.…”

“…Jiang et al (2018) However, conventional computer vision methods are unable to detect rice samples with mild mildew contamination, and it is difficult to detect mildly mildewed grains with a total viable bacterial count (TVC) of 10 5 -10 6 CFU/g using methods at conventional observational scales (Sun et al, 2022;Zhou et al, 2008). Sun et al (2022) used micro-computer vision (MCV) technology to improve the sensitivity to mildew features and used an advanced YOLO CNN to recognize the location of A. niger, Penicillium orange, and Aspergillus griseus on single rice grains with an accuracy of 89.26%, 91.15%, and 90.19%, respectively. Conventional computer vision methods can directly detect obvious bacterial colonies in mildewed rice images; however, bacterial colonies of mildly mildewed rice are small, and MCV techniques are required for feature extraction (Barsanti et al, 2022;Sun et al, 2022).…”