

Harmful Animal Identification and Detection In Forests Using AI & ML
Abstract
The increasing human-wildlife conflict in forested areas necessitates the development of advanced systems for early identification and detection of harmful animals. This project, titled "Harmful Animal Identification and Detection in Forests Using AI/ML," aims to leverage Artificial Intelligence (AI) and Machine Learning (ML) technologies to create an automated system capable of detecting dangerous animals in real-time from image and video data captured by drones or camera traps. Using deep learning models such as Convolutional Neural Networks (CNNs), the system will accurately classify animal species and identify those that pose potential risks to human life, livestock, or the ecosystem.
References
Sharma, P., Kumar, A., & Singh, V., AI- Based Animal Detection and Classification in Forest Areas, 2020, International Journal of Computer Vision and Image Processing.
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Zhu, C., Wang, F., & Li, T., Multimodal Animal Detection in Forests Using Sensor Fusion, 2020, IEEE Sensors Journal.
Patil, M., & Joshi, R., Deep Learning for Wildlife Surveillance and Threat Detection, 2021, Journal of Environmental Monitoring and Management.
Kaur, N., & Singh, P., An Acoustic- Based Approach for Detecting Predatory Animals in Forests, 2019, International Journal of Signal Processing Systems.
Wang, Y., Luo, J., & Zhang, H., Using Drones and Machine Learning to Monitor Wildlife in Forests, 2021, Remote Sensing Technology and Applications.
Bansal, R., Mishra, A., & Jain, P., AI- Based Animal Threat Detection for Human- Wildlife Conflict Reduction, 2022, Journal of Computational Ecology.
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