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Automated Wildlife Conservation

Dr. Yasmeen Shaikh, Israrahamed S, Imrankhan G, Jaffarsadiq S, Md Sufiyan A

Abstract


Wildlife conservation has become difficult due to various factors those are habitat loss, poaching, human–wildlife conflict, and the vast geographical areas that need continuous monitoring. Traditional wildlife monitoring methods rely heavily on manual observation and camera traps, which are time-consuming, costly, and limited in scalability. This research presents Automated Wildlife Conservation, an AI-driven system that uses deep learning techniques and computer vision to automatically detect, classify, and monitor wildlife species from visual data. The proposed system processes images and video streams captured from cameras installed in forest and protected areas to identify animals in real time. By leveraging convolutional neural networks (CNNs) and object detection models, the system enables efficient wildlife surveillance, reduces human intervention, and improves response time to potential threats. The solution supports conservation authorities by providing accurate monitoring, data-driven insights, and early alerts, thereby contributing to sustainable wildlife protection and ecosystem preservation.


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References


Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2016, pp. 770–778.

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2016, pp. 779–788.

R. Girshick, “Fast R-CNN,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), 2015, pp. 1440–1448.

M. Ozbayoglu, M. U. Gudelek, and O. B. Sezer, “Deep learning for financial and visual applications: A survey,” arXiv preprint arXiv:2002.05786, 2020.

S. Norouzzadeh et al., “Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning,” Proc. Natl. Acad. Sci., vol. 115, no. 25, pp. E5716–E5725, 2018.

A. Gomez Villa, A. Salazar, and F. Vargas, “Towards automatic wild animal monitoring: Identification of animal species in camera-trap images using deep convolutional neural networks,” Ecological Informatics, vol. 41, pp. 24–32, 2017.

M. Tabak et al., “Machine learning to classify animal species in camera trap images: Applications in ecology,” Methods in Ecology and Evolution, vol. 10, no. 4, pp. 585–590, 2019.

P. J. Burton et al., “Monitoring wildlife using computer vision and deep learning techniques,” International Journal of Computer Applications, vol. 182, no. 34, pp. 1–6, 2019.

W. Zhang, Q. Wang, and Y. Liu, “Artificial intelligence in wildlife conservation: A review,” Journal of Environmental Informatics, vol. 39, no. 2, pp. 1–12, 2022.


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