Envisioning Artificial Intelligence Possibilities for Tracking Eidolon Helvum Bats: A Review

Halleluyah O. Aworinde, Segun Adebayo, Damilare I. Ayokunle, Oluwatosin E. Atobatele, Abidemi Emmanuel Adeniyi

Abstract

Artificial intelligence (AI) is rapidly assuming significant responsibilities because it can be used to curate data on wildlife observations, migration patterns, ecological population monitoring, and behavior understanding. In this study, current trends in how artificial intelligence is used to understand activities of Eidolon helvum fruit bats are discussed. This study reviewed 122 publications related to bats and their environment. Of these publications, only 20 (16%) used machine learning to assess bat activities ranging from specie classification to sound signal processing. In addition, we listed the most widely used machine learning methods and justified their application to ecological research. Furthermore, the study identified other AI-based technology like edge computing and the internet of things (IoT) as new opportunities in ecological research. In conclusion, this study anticipates the joint use of cutting-edge technologies such as TinyML for ecological research and conservation and the need for innovative methods and AI-driven solutions to track wildlife demographics.

 

Keywords: artificial intelligence, ecology, biodiversity, pattern recognition, wildlife.

 

https://doi.org/10.55463/issn.1674-2974.51.5.9


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References


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