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Tuna resources are an important part of China's pelagic fishery production. However, for China's tuna fishery, tuna species caught at sea are still manually classified, which is a time-consuming and inefficient process; so China's tuna fishery needs to develop toward automation. This study uses gray-level co-occurrence matrix (GLCM) and VGG16 to visualize phenotypic texture through local images of three Thunnus species. At the same time, texture feature index data (TFD), deep feature data (DFD), and their combined feature data (CFD) are obtained from texture images. Support vector machine (SVM) with different kernel functions is used to classify phenotypic texture of tuna automatically. The study shows that visualized texture images of different tuna using GLCM and VGG16 have biological characteristics. In the classification results without cross-validation, the average classification accuracy of TFD in polynomial was 83%, the average classification accuracy of DFD in RBF (Radial basis function) was 93%, and the average classification accuracy of CFD in RBF was 95%. It is concluded that tuna phenotype texture can be efficiently classified by using SVM with different kernel functions. Key words: support vector machine, Thunnus, phenotype textures, gray-level co-occurrence matrix, texture feature, deep feature, kernel function, automatic classification, Introduction Tuna is one of the important targets for marine fisheries (Restrepo et al. 2016). It has a large value both economically and nutritionally, and plays a significant role in [...]

Tuna are economically important fish species. The automated identification of tuna species is of importance in fishery production and resource assessment in that it would facilitate the informed monitoring of tuna fishing vessels and the establishment of electronic observer systems. As morphological characteristics are important for tuna identification, this study aims to verify the performance of the automated identification of three Thunnus species through morphological characteristics based on different machine learning algorithms. Firstly, morphological outlines were visually analyzed using EFT (elliptic Fourier transform) and CNN (convolutional neural network). Then, the EFT feature data and deep feature data of the tuna outline images were extracted, and principal component analysis of the two different morphological characteristics was performed. Finally, different machine learning algorithms were used to analyze the identification performance of tuna of the same genus and different species. The experimental results showed that EFT features had the highest identification accuracy in KNN (K-nearest neighbor), with 90% for T. obesus, 90% for T. albacores, and 85% for T. alalunga. Deep features had the best identification performance in SVM (support vector machine), with 80% for T. obesus, 90% for T. albacores, and 100% for T. alalunga. Deep features were better than EFT features in identification performance. The biodiversity and intergeneric differences among tuna species can be well analyzed using these two different morphological characteristics. Machine learning algorithms open up the way for rapid near-real-time electronic observer systems in these important international fisheries. [ABSTRACT FROM AUTHOR]

In order to improve the corrosion resistance and dispersiveness of aluminium pigments in aqueous media, SiO 2 -coated aluminium was prepared and polyacrylic acid brushes were grafted on its surface. A decrease in hydrogen generation indicated that the corrosion resistance was improved by the SiO 2 coating, while the polyacrylic acid brushes improved dispersiveness. The d (0.5) (particle size of 50% volume fraction) values of the SiO 2 -coated aluminium sample and that of the sample with the polyacrylic acid brushes grafted on it were 41 μm and 29 μm, respectively. We believe that thus modified aluminium will find use in water-based paints and printing inks. [ABSTRACT FROM AUTHOR]