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.

A charged nanocomposite membrane via co-deposition of gallic acid and polyethyleneimine-silver for improving separation and antibacterial properties.

Pharmaceuticals have been continuously detected from surface water and groundwater. In order to improve the rejection performance of pharmaceuticals by a nanofiltration membrane (NF), a positively charged membrane was prepared by co-deposition of natural gallic acid and polyethyleneimine on the polyacrylonitrile hydrolysis membrane. Effects of gallic acid concentration, polyethylene imine concentration, reaction time, and the molecular weight of polyethylene imine were documented. The physical and chemical properties of the membrane were also investigated by surface morphology, hydrophilicity, surface charge, and molecular weight cut-off. The optimized membrane had a molecular weight cut-off of about 958 Da and possessed a pure water permeability of 74.21 L·m-2·h-1·MPa-1. The results exhibited salt rejection in the following order: MgCl2 > CaCl2 > MgSO4 > Na2CO3 > NaCl > Na2SO4, while the rejection ability of pharmaceuticals is as follows: amlodipine > atenolol > carbamazepine > ibuprofen, suggesting that the positively charged membrane has enhanced retention to both divalent cations and charged pharmaceuticals. In addition, the antibacterial membrane was obtained by loading silver nanoparticles onto the positively charged membrane, which greatly improved the antibacterial ability of the membrane.

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

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.

Enhancing the activation of persulfate using nitrogen-doped carbon materials in the electric field for the effective removal of p-nitrophenol.

Degradation of nonbiodegradable organic compounds into harmless substances is one of the main challenges in environmental protection. Electrically-activated persulfate process has served as an efficient advanced oxidation process (AOP) to degrade organic compounds. In this study, we synthesized three nitrogen-doped carbon materials, namely, nitrogen-doped activated carbon plus graphene (NC), and nitrogen-doped activated carbon (NAC), nitrogen-doped graphene (NGE), and three nitrogen-doped carbon material-graphite felt (GF) cathodes. The three nitrogen-doped carbon materials (NC, NGE, NAC) were characterized using X-ray diffraction, Raman spectroscopy, X-ray electron spectroscopy, and nitrogen desorption-adsorption. The electron spin resonance technique was used to identify the presence of hydroxyl radicals (˙OH), sulfate radicals (SO4˙-) and singlet oxygen (1O2) species. The results showed that NC was more conducive for the production of free radicals. In addition, we applied NC-GF to an electro-activated persulfate system with the degradation of p-nitrophenol and investigated its performance for contaminant degradation under different conditions. In general, the nitrogen-doped carbon electrode electro-activated persulfate process is a promising way to treat organic pollutants in wastewater.