Harmful Animal Identification and Detection in Forests Using AI & ML
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| Format: | Recurso digital |
| Sprache: | Englisch |
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Zenodo
2025
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| author | Ramesh Patil Abhishek Jagadish Karan Pitre Mayur Baswaraj |
| author_facet | Ramesh Patil Abhishek Jagadish Karan Pitre Mayur Baswaraj |
| contents | <p><em><span lang="EN-US">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<span> </span>Artificial Intelligence (AI) and Machine Learning (ML) technologies to create an<span> </span>automated<span> </span>system<span> </span>capable of<span> </span>detecting<span> </span>dangerous<span> </span>animals<span> </span>in real-time<span> </span>from<span> </span>image<span> </span>and<span> </span>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.</span></em></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15372086 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Harmful Animal Identification and Detection in Forests Using AI & ML Ramesh Patil Abhishek Jagadish Karan Pitre Mayur Baswaraj Human-Wildlife Conflict, Animal Detection, Deep Learning, Convolutional Neural Networks (CNNs), Raspberry Pi, Edge AI Processing, Drones and Camera Trap, Real- Time Wildlife Monitoring <p><em><span lang="EN-US">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<span> </span>Artificial Intelligence (AI) and Machine Learning (ML) technologies to create an<span> </span>automated<span> </span>system<span> </span>capable of<span> </span>detecting<span> </span>dangerous<span> </span>animals<span> </span>in real-time<span> </span>from<span> </span>image<span> </span>and<span> </span>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.</span></em></p> |
| title | Harmful Animal Identification and Detection in Forests Using AI & ML |
| topic | Human-Wildlife Conflict, Animal Detection, Deep Learning, Convolutional Neural Networks (CNNs), Raspberry Pi, Edge AI Processing, Drones and Camera Trap, Real- Time Wildlife Monitoring |
| url | https://doi.org/10.5281/zenodo.15372086 |