International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | YOLOv10-Based Smart Farming for Wildlife Threat Detection |
|---|---|
| ABSTRACT | The transition toward Agriculture 5.0 has introduced advanced monitoring capabilities, yet the conflict between wildlife conservation and crop security remains a critical economic challenge. Farmers in forest-bordering regions frequently suffer significant losses due to intrusions by animals such as elephants, wild boars, and deer. Traditional mitigation strategies—ranging from manual patrolling to passive infrared (PIR) sensors—often lack the necessary speed, classification accuracy, and non-lethal deterrence capabilities [1]. This paper proposes a robust, automated solution utilizing Edge Artificial Intelligence (Edge AI) and the YOLOv10 object detection model. The proposed system processes video feeds in real-time to detect and classify wildlife species, dynamically assessing threat levels (High, Medium, Safe) based on user-defined sensitivity parameters. Upon positive verification, the system triggers immediate alerts via a web-based dashboard and activates physical deterrents (buzzers). Experimental results validate the system’s efficacy, demonstrating a detection accuracy of 98.5% and an alert latency of 450 ms. This framework offers a scalable, cost-effective, and non-lethal mechanism for mitigating human-wildlife conflict in modern smart farming ecosystems [2]. |
| AUTHOR | ABHISHEK B M, ANOOP B K, DEVARAJ, BHOOMIKA K G Assistant Professor, Dept. of CSE, Acharya institute of Technology, Bengaluru, Karnataka, India UG Student, Department of Computer Science and Engineering, Acharya institute of Technology, Bengaluru, Karnataka, India |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405047 |
| pdf/47_YOLOv10-Based Smart Farming for Wildlife Threat Detection.pdf | |
| KEYWORDS | |
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