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

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.

Advances in the application of recombinase-aided amplification combined with CRISPR-Cas technology in quick detection of pathogenic microbes

The rapid diagnosis of pathogenic infections plays a vital role in disease prevention, control, and public health safety. Recombinase-aided amplification (RAA) is an innovative isothermal nucleic acid amplification technology capable of fast DNA or RNA amplification at low temperatures. RAA offers advantages such as simplicity, speed, precision, energy efficiency, and convenient operation. This technology relies on four essential components: recombinase, single-stranded DNA-binding protein (SSB), DNA polymerase, and deoxyribonucleoside triphosphates, which collectively replace the laborious thermal cycling process of traditional polymerase chain reaction (PCR). In recent years, the CRISPR-Cas (clustered regularly interspaced short palindromic repeats-associated proteins) system, a groundbreaking genome engineering tool, has garnered widespread attention across biotechnology, agriculture, and medicine. Increasingly, researchers have integrated the recombinase polymerase amplification system (or RAA system) with CRISPR technology, enabling more convenient and intuitive determination of detection results. This integration has significantly expanded the application of RAA in pathogen detection. The step-by-step operation of these two systems has been successfully employed for molecular diagnosis of pathogenic microbes, while the single-tube one-step method holds promise for efficient pathogen detection. This paper provides a comprehensive review of RAA combined with CRISPR-Cas and its applications in pathogen detection, aiming to serve as a valuable reference for further research in related fields.