Department of Computer Science, College of Computing, Khon Kaen University, Khon Kaen, 40002, Thailand.
Department of Environmental Science, Faculty of Science, Khon Kaen University, Khon Kaen, 40002, Thailand.
Sci Rep. 2024 Oct 23;14(1):24993. doi: 10.1038/s41598-024-76431-9.
In Thailand, two snail-eating turtle species in the genus Malayemes (M. subtrijuga and M. macrocephala) are protected animals in which smuggling and trading are illegal. Recently, a new species M. khoratensis has been reported and it has not yet been considered as protected animal species. To enforce the law, species identification of Malayemes is crucial. However, it is quite challenging and requires expertise. Therefore, a simple tool, such as image analysis, to differentiate these three snail-eating species would be highly useful. This study proposes a novel ensemble multiview image processing approach for the automated classification of three turtle species in the genus Malayemys. The original YOLOv8 architecture was improved by utilizing a convolutional neural network (CNN) to overcome the limitations of traditional identification methods. This model captures unique morphological features by analyzing Malayemys species images from various angles, addressing challenges such as occlusion and appearance variations. The ensemble multiview strategy significantly increases the YOLOv8 classification accuracy using a comprehensive dataset, achieving an average mean average precision (mAP) of 98% for the genus Malayemys compared with the nonensemble multiview and single-view strategies. The species identification accuracy of the proposed models was validated by comparing genetic methods using mitochondrial DNA with morphological characteristics. Even though the morphological characteristics of these three species are ambiguous, the mitochondrial DNA sequences are quite distinct. Therefore, this alternative tool should be used to increase confidence in field identification. In summary, the contribution of this study not only marks a significant advancement in computational biology but also supports wildlife and turtle conservation efforts by enabling rapid, accurate species identification.
在泰国，两种食蜗牛龟鳖目马来鳖属（M. subtrijuga 和 M. macrocephala）物种均被列为受保护动物，走私和交易均属违法行为。最近，报道了一个新物种 M. khoratensis，尚未被视为受保护的物种。为了执法，马来鳖属物种的鉴定至关重要。然而，这极具挑战性，需要专业知识。因此，一种简单的工具，如图像分析，来区分这三种食蜗牛物种将非常有用。本研究提出了一种新颖的马来鳖属三种龟鳖物种的集成多视图图像处理方法。通过利用卷积神经网络（CNN）改进原始的 YOLOv8 架构，克服了传统识别方法的局限性。该模型通过分析来自不同角度的马来鳖属物种图像来捕捉独特的形态特征，解决了遮挡和外观变化等挑战。基于集成多视图策略的 YOLOv8 分类精度显著提高，在综合数据集上的平均精度均值（mAP）为 98%，相比非集成多视图和单视图策略有明显提升。通过比较线粒体 DNA 与形态特征的遗传方法验证了所提出模型的物种识别准确性。尽管这三个物种的形态特征模糊不清，但线粒体 DNA 序列却有明显的区别。因此，应该使用这种替代工具来提高现场识别的可信度。综上所述，本研究的贡献不仅标志着计算生物学领域的重大进展，而且通过支持快速、准确的物种鉴定，为野生动物和龟鳖类保护工作做出了贡献。
