Khantao gen. nov., a new genus of Selenocosmiinae Simon, 1889 from western Yunnan of China (Araneae: Mygalomorphae: Theraphosidae)

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  Khantao gen. nov., a new genus of Selenocosmiinae Simon, 1889 from western Yunnan of China (Araneae: Mygalomorphae: Theraphosidae) Abstract The new genus Khantao gen. nov. from western Yunnan, China, is established for the subfamily Selenocosmiinae Simon, 1889, including three new species and one new combination: Khantao talpoides sp. nov. (type species); Khantao xuanae sp. nov.; Khantao ya sp. nov.; and Khantao xinhuaensis (Zhu & Zhang, 2008) comb. nov. (transferred from Selenocosmia Ausserer, 1871). DNA data (Cytochrome c oxidase subunit 1, COI sequences) are provided for all species. Yu, K., Wirth, V.V., West, R.C., Zhang, S. & Zhang, F. (2026) Khantao gen. nov., a new genus of Selenocosmiinae Simon, 1889 from western Yunnan of China (Araneae: Mygalomorphae: Theraphosidae). Zootaxa, 5853 (2), 151–188. https://doi.org/10.11646/zootaxa.5853.2.1

Look Out for Dangerous Spiders: Araneae Classification Using Deep Learning Methods

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Look Out for Dangerous Spiders: Araneae Classification Using Deep Learning Methods

Abstract

Of the 50,000 known spider species that taxonomists have identified, a subset of them are considered to be particularly significant due to the harmful physiological effects their venom has on humans, which often demands prompt and precise identification of the spider in emergency scenarios. Traditional spider identification relies on expert knowledge of morphological characteristics for identification, but in critical scenarios this may be inadequate due to time or knowledge constraints. Thanks to the rise of machine learning, we have developed an effective solution to this problem through the testing of powerful deep learning models. In this paper, we utilize various proven image classification models as a backbone, then fine-tune them on a curated dataset of spider images from the citizen science platform iNaturalist with an emphasis on spiders that are particularly harmful to humans. Experimental results are favorable and indicate that modern image classification models perform well on the task of spider species identification. Our highest performing model is a ConvNeXtV2 backbone model which achieves 91.2% accuracy on our testing set. Compared to previous related works, our fine-tuned model is able to achieve higher classification accuracy while handling a much larger number of spider species.

Z. K. Deng and J. J. Rodriguez, "Look Out for Dangerous Spiders: Araneae Classification Using Deep Learning Methods," 2024 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), Santa Fe, NM, USA, 2024, pp. 134-137, doi: 10.1109/SSIAI59505.2024.10508676.