Work

Automated Wildlife Conservation

Year: 2025

Type: article

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.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.18092259

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

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Life on land

Open Access status: green

OpenAlex ID: W7117755526

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