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Integration of Unmanned Aerial Vehicle Systems With Machine Learning Algorithms for Wildlife Monitoring and Conservation

Integration of Unmanned Aerial Vehicle Systems With Machine Learning Algorithms for Wildlife Monitoring and Conservation

R. Raffik, M. Mahima Swetha, Rithish Ramamoorthy Sathya, V. Vaishali, B. Madhana Adithya, S. Balavedhaa
Copyright: © 2024 |Pages: 39
ISBN13: 9798369305782|ISBN13 Softcover: 9798369305799|EISBN13: 9798369305805
DOI: 10.4018/979-8-3693-0578-2.ch006
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MLA

Raffik, R., et al. "Integration of Unmanned Aerial Vehicle Systems With Machine Learning Algorithms for Wildlife Monitoring and Conservation." Applications of Machine Learning in UAV Networks, edited by Jahan Hassan and Saeed Alsamhi, IGI Global, 2024, pp. 121-159. https://doi.org/10.4018/979-8-3693-0578-2.ch006

APA

Raffik, R., Mahima Swetha, M., Sathya, R. R., Vaishali, V., Madhana Adithya, B., & Balavedhaa, S. (2024). Integration of Unmanned Aerial Vehicle Systems With Machine Learning Algorithms for Wildlife Monitoring and Conservation. In J. Hassan & S. Alsamhi (Eds.), Applications of Machine Learning in UAV Networks (pp. 121-159). IGI Global. https://doi.org/10.4018/979-8-3693-0578-2.ch006

Chicago

Raffik, R., et al. "Integration of Unmanned Aerial Vehicle Systems With Machine Learning Algorithms for Wildlife Monitoring and Conservation." In Applications of Machine Learning in UAV Networks, edited by Jahan Hassan and Saeed Alsamhi, 121-159. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-0578-2.ch006

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Abstract

Two cutting-edge technologies, unmanned aerial vehicle (UAV) systems and deep learning algorithms, have the potential to completely change how wildlife is monitored and conserved. Data collection across wide areas, in challenging locations, and in real time are all possible with UAVs. Data collection via UAVs is possible in locations that are difficult or impossible to reach using conventional human approaches. Along with spotting strange behavior by wild creatures, the UAV can also spot it in human activity. Deep learning algorithms can be used to recognize certain animals, follow their motions, and categorize their behavior. The ecology of wildlife populations may be better understood using this knowledge, which can also be utilized to create more successful conservation plans. A novel technique that has promise for wildlife monitoring and conservation is the fusion of UAV systems and deep learning algorithms. The anticipation is even more creative and successful methods to use UAVs and deep learning to protect animals as technology progresses.

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