Spider Species Identification: Bridging Traditions with Molecular and Deep Learning Approaches

Aakriti Shrivastava *

Department of Zoology, Govt. Holkar Science College, Indore, India.

V. K. Sharma

Department of Zoology, Govt. Holkar Science College, Indore, India.

*Author to whom correspondence should be addressed.


Abstract

This article provides an in-depth review of various methods employed in the identification and sequencing of spiders, highlighting the advancements and challenges in the field. With the increasing importance of spiders in ecological studies, medical research, and biodiversity conservation, accurate identification and genetic analysis have become crucial. This review discusses traditional and modern techniques, shedding light on their applications, limitations, and future prospects.

The exploration begins with an analysis of taxonomists' etymological choices, examining patterns in naming conventions across continents and centuries. Traditional morphological identification, anchored in backbone taxonomy, dichotomous keys, and statistical analyses, highlights the advantages and challenges of relying on observable features. The study transitions to molecular techniques, elucidating the applications and challenges of DNA barcoding, Next-Generation Sequencing (NGS), and metabarcoding in spider identification. The integration of deep learning models, exemplified by the YOLOv7-based Spider Identification APP, represents a landmark in computer vision for efficient and user-friendly spider species recognition. The study's multifaceted approach provides a nuanced understanding of spider taxonomy, bridging historical practices with state-of-the-art technologies, and lays the groundwork for future advancements in the field.

Keywords: Spider identification, morphological taxonomy, DNA barcoding, next-generation sequencing, metabarcoding, deep learning models, biodiversity analysis


How to Cite

Shrivastava, Aakriti, and V. K. Sharma. 2024. “Spider Species Identification: Bridging Traditions With Molecular and Deep Learning Approaches”. Annual Research & Review in Biology 39 (7):40-45. https://doi.org/10.9734/arrb/2024/v39i72097.

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