Tan Hui Yuan, Goh Zhi Yun, Loh Kar-Hoe, Then Amy Yee-Hui, Omar Hasmahzaiti, Chang Siow-Wee
Bioinformatics Programme, Institute of Biological Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia.
Institute of Ocean & Earth Sciences, Universiti Malaya, Kuala Lumpur, Malaysia.
PeerJ. 2021 Aug 9;9:e11825. doi: 10.7717/peerj.11825. eCollection 2021.
Despite the high commercial fisheries value and ecological importance as prey item for higher marine predators, very limited taxonomic work has been done on cephalopods in Malaysia. Due to the soft-bodied nature of cephalopods, the identification of cephalopod species based on the beak hard parts can be more reliable and useful than conventional body morphology. Since the traditional method for species classification was time-consuming, this study aimed to develop an automated identification model that can identify cephalopod species based on beak images.
A total of 174 samples of seven cephalopod species were collected from the west coast of Peninsular Malaysia. Both upper and lower beaks were extracted from the samples and the left lateral views of upper and lower beak images were acquired. Three types of traditional morphometric features were extracted namely grey histogram of oriented gradient (HOG), colour HOG, and morphological shape descriptor (MSD). In addition, deep features were extracted by using three pre-trained convolutional neural networks (CNN) models which are VGG19, InceptionV3, and Resnet50. Eight machine learning approaches were used in the classification step and compared for model performance.
The results showed that the Artificial Neural Network (ANN) model achieved the best testing accuracy of 91.14%, using the deep features extracted from the VGG19 model from lower beak images. The results indicated that the deep features were more accurate than the traditional features in highlighting morphometric differences from the beak images of cephalopod species. In addition, the use of lower beaks of cephalopod species provided better results compared to the upper beaks, suggesting that the lower beaks possess more significant morphological differences between the studied cephalopod species. Future works should include more cephalopod species and sample size to enhance the identification accuracy and comprehensiveness of the developed model.
尽管头足类动物具有很高的商业渔业价值，并且作为高级海洋捕食者的猎物具有重要的生态意义，但马来西亚对头足类动物的分类学研究非常有限。由于头足类动物身体柔软，基于喙的坚硬部分对头足类物种进行鉴定可能比传统的身体形态学方法更可靠、更有用。由于传统的物种分类方法耗时，本研究旨在开发一种能够基于喙图像识别头足类物种的自动识别模型。
从马来西亚半岛西海岸共采集了174个七种头足类物种的样本。从样本中提取上下喙，并获取上下喙图像的左侧视图。提取了三种传统的形态特征，即定向梯度灰度直方图（HOG）、彩色HOG和形态形状描述符（MSD）。此外，还使用了三种预训练的卷积神经网络（CNN）模型，即VGG19、InceptionV3和Resnet50来提取深度特征。在分类步骤中使用了八种机器学习方法，并对模型性能进行了比较。
结果表明，人工神经网络（ANN）模型使用从VGG19模型提取的来自下喙图像的深度特征，实现了91.14%的最佳测试准确率。结果表明，在突出头足类物种喙图像的形态测量差异方面，深度特征比传统特征更准确。此外，与上喙相比，使用头足类物种的下喙提供了更好的结果，这表明在所研究的头足类物种之间，下喙具有更显著的形态差异。未来的工作应包括更多的头足类物种和样本量，以提高所开发模型的识别准确性和全面性。
