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VOL. 10, ISSUE 7 (2025)
Artificial intelligence and machine learning in insect identification and monitoring
Authors
Dr. Deepika Goswami
Abstract
Artificial Intelligence (AI) and Machine Learning (ML) are
revolutionizing the field of entomology by enhancing the accuracy, speed, and
scalability of insect identification and monitoring. Traditional methods, which
rely heavily on manual identification, are time-consuming and require
specialized expertise. In contrast, AI and ML approaches leverage image
processing, pattern recognition, and deep learning algorithms to automate the
identification of insect species from images or sensor data. These technologies
also enable real-time monitoring of insect populations in various environments,
including agricultural fields, forests, and urban areas. Integrating AI with
remote sensing and IoT devices allows for the continuous tracking of insect
activity, which is vital for pest control, biodiversity conservation, and
ecological research. Despite challenges such as dataset quality, species
variability, and environmental noise, recent advances in neural networks,
transfer learning, and edge computing have significantly improved system
performance. This paper explores current developments, applications, and future
directions in using AI and ML for insect identification and monitoring.
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Pages:26-33
How to cite this article:
Dr. Deepika Goswami "Artificial intelligence and machine learning in insect identification and monitoring". International Journal of Entomology Research, Vol 10, Issue 7, 2025, Pages 26-33
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