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VOL. 10, ISSUE 10 (2025)
Artificial Intelligence in Entomology: Revolutionizing Insect Classification and Systematics
Authors
Manish Sharma and Heena Sachdeva
Abstract
Accurate and rapid insect identification is a cornerstone of
entomology, biodiversity monitoring, and integrated pest management.
Traditional taxonomic approaches, reliant on expert morphological evaluation,
are often time-consuming, labor-intensive, and prone to human error, especially
when handling large-scale datasets. Recent advances in artificial intelligence
(AI), particularly deep learning (DL) and machine learning (ML), have
revolutionized insect classification and systematics by enabling automated,
high-throughput, and scalable identification pipelines. Convolutional neural
networks (CNNs), object detection frameworks, and hybrid approaches that
integrate morphological, spectral, and acoustic data have demonstrated
performance approaching human-level accuracy for several insect taxa. Despite
these advancements, challenges such as limited labeled datasets, class
imbalance, small-object detection, domain adaptation, and integration with
classical taxonomy remain. This review synthesizes current AI approaches in
insect classification, evaluates available datasets and benchmarking strategies,
examines applications in biodiversity monitoring, pest management, and
life-stage analysis, and outlines limitations and solutions to enhance
AI-assisted entomological research.
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Pages:81-83
How to cite this article:
Manish Sharma and Heena Sachdeva "Artificial Intelligence in Entomology: Revolutionizing Insect Classification and Systematics". International Journal of Entomology Research, Vol 10, Issue 10, 2025, Pages 81-83
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