Machine learning analysis of wing venation patterns accurately identifies Sarcophagidae, Calliphoridae and Muscidae fly species,Medical and Veterinary Entomology

当前位置： X-MOL 学术 › Med. Vet. Entomol. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Machine learning analysis of wing venation patterns accurately identifies Sarcophagidae, Calliphoridae and Muscidae fly species
Medical and Veterinary Entomology ( IF 1.6 ) Pub Date : 2023-07-21 , DOI: 10.1111/mve.12682
Min Hao Ling ₁ , Tania Ivorra _{2,

3} , Chong Chin Heo ₂ , April Hari Wardhana _{4,

5} , Martin Jonathan Richard Hall ₆ , Siew Hwa Tan _{7,

8} , Zulqarnain Mohamed ₈ , Tsung Fei Khang _{1,

9}

Affiliation

In medical, veterinary and forensic entomology, the ease and affordability of image data acquisition have resulted in whole-image analysis becoming an invaluable approach for species identification. Krawtchouk moment invariants are a classical mathematical transformation that can extract local features from an image, thus allowing subtle species-specific biological variations to be accentuated for subsequent analyses. We extracted Krawtchouk moment invariant features from binarised wing images of 759 male fly specimens from the Calliphoridae, Sarcophagidae and Muscidae families (13 species and a species variant). Subsequently, we trained the Generalized, Unbiased, Interaction Detection and Estimation random forests classifier using linear discriminants derived from these features and inferred the species identity of specimens from the test samples. Fivefold cross-validation results show a 98.56 ± 0.38% (standard error) mean identification accuracy at the family level and a 91.04 ± 1.33% mean identification accuracy at the species level. The mean F1-score of 0.89 ± 0.02 reflects good balance of precision and recall properties of the model. The present study consolidates findings from previous small pilot studies of the usefulness of wing venation patterns for inferring species identities. Thus, the stage is set for the development of a mature data analytic ecosystem for routine computer image-based identification of fly species that are of medical, veterinary and forensic importance.

中文翻译：

对翅膀脉络模式的机器学习分析可准确识别石蝇科、丽蝇科和蝇科蝇种

在医学、兽医和法医昆虫学中，图像数据采集的简便性和经济性使得全图像分析成为物种识别的宝贵方法。Krawtchouk 矩不变量是一种经典的数学变换，可以从图像中提取局部特征，从而可以在后续分析中强调微妙的物种特异性生物变化。我们从来自丽蝇科、石蝇科和蝇科（13 个物种和一个物种变种）的 759 个雄蝇标本的二值化翅膀图像中提取了 Krawtchouk 矩不变特征。随后，我们使用从这些特征导出的线性判别式训练广义、无偏、交互检测和估计随机森林分类器，并从测试样本中推断出样本的物种身份。五重交叉验证结果显示，科水平的平均识别准确度为 98.56 ± 0.38%（标准误差），物种水平的平均识别准确度为 91.04 ± 1.33%。平均F 1 分数为 0.89 ± 0.02，反映了模型的精确度和召回率特性之间的良好平衡。本研究整合了之前关于翅膀脉络模式对于推断物种身份的有用性的小型试点研究的结果。因此，为开发成熟的数据分析生态系统奠定了基础，用于对具有医学、兽医和法医学重要性的苍蝇物种进行基于计算机图像的常规识别。

更新日期：2023-07-21

点击分享查看原文

点击收藏

公开下载

阅读更多本刊新发论文本刊介绍/投稿指南