Color image indices used to characterize surface roughness are more sensitive than grayscale images or spectral indices owing to the rich information contained in color images. In this paper, a method for measuring grinding surface roughness grinding based on the singular value entropy of the color image quaternion matrix is proposed. Color images captured at different levels of surface roughness are analyzed using a quaternion matrix, which is subjected to singular value decomposition from which a quaternion singular value entropy is derived as an index for evaluating roughness. Our experimental results show that this method for measuring grinding surface roughness based on quaternion singular value entropy is a more feasible roughness detection method than color difference indices because the singular value entropy is more strongly correlated with the actual roughness, with the monotonicity of the entropy decreasing more significantly as the roughness increases. Roughness prediction results obtained using a support vector machine also support the feasibility of measuring surface roughness based on the singular value entropy of the color image quaternion matrix, which can provide a reliable engineering application for the automatic measurement of surface roughness. Finally, the high degree of correspondence between the pure quaternion matrix and the image color matrix in the mathematical structure provides a broad mathematical space for the design and optimization of the color index.

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The interaction between the microbial communities in aquatic animals and those in the ambient environment is important for both healthy aquatic animals and the ecological balance of aquatic environment. Crayfish (Procambarus clarkii), with their high commercial value, have become the highest-yield freshwater shrimp in China. The traditional cultivation in ponds (i.e., monoculture, MC) and emerging cultivation in rice co-culture fields (i.e., rice–crayfish co-culture, RC) are the two main breeding modes for crayfish, and the integrated RC is considered to be a successful rice-livestock integration practice in eco-agricultural systems. This study explored the ecological interactions between the microbial communities in crayfish intestine and the ambient environment, which have not been fully described to date. The bacterial communities in crayfish intestine, the surrounding water, and sediment in the two main crayfish breeding modes were analyzed with MiSeq sequencing and genetic networks. In total, 53 phyla and 1,206 genera were identified, among which Proteobacteria, Actinobacteria, Tenericutes, Firmicutes, Cyanobacteria, Chloroflexi, Bacteroidetes, Acidobacteria, RsaHF231, and Nitrospirae were the dominant phyla. The microbiota composition significantly differed between the water, sediment, and crayfish intestine, while it did not between the two breeding modes. We also generated a co-occurrence correlation network based on the high-confidence interactions with Spearman correlation ρ ≥ 0.75. In the genera co-correlation network, 95 nodes and 1,158 edges were identified, indicating significant genera interactions between crayfish intestine and the environment. Furthermore, the genera clustered into three modules, based on the different environments. Additionally, Candidatus_Bacilloplasma, g_norank_f_Steroidobacteraceae, Dinghuibacter, Hydrogenophaga, Methyloparacoccus, and Defluviicoccus had the highest betweenness centrality and might be important in the interaction between crayfish and the ambient environment. Overall, this study enhances our understanding of the characteristics of the microbiota in crayfish and their surrounding environment. Moreover, our findings provide insights into the microecological balance in crayfish eco-agricultural systems and theoretical reference for the development of such systems.

At present, the application of machine vision methods for roughness measurement in production sites is limited by its adaptability to illumination variations during the measurement. In this study, a machine vision method for roughness measurement with robustness to illumination is proposed so as to explore the functions of its color image indices in improving the mathematical expression of the vector of three primary colors. Besides, virtual images of different-roughness surfaces were analyzed, the effects of the samples’ surface texture orientations on measurement indices were discussed, and the singular value ratio was derived as an index for evaluating roughness. The experimental results showed that the samples’ index values remained unchanged when the illumination was increased for both vertical and horizontal surface textures, indicating that the proposed method has strong robustness to illumination. In addition, the experimental results were verified by a support vector machine (SVM)-based method using 10 different-roughness test samples, with the verification range of 0.127–2.245 µm. It was found that the measurement accuracy reached 90%, suggesting that the proposed method is reasonable and feasible, and shows certain potential to be applied in engineering.