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 | AI-Based Smart Electric Fence System with IoT Monitoring and Adaptive Voltage Control |
|---|---|
| ABSTRACT | Human–wildlife conflict in agricultural regions adjacent to forest reserves has led to severe crop losses, economic instability for farmers, and increased risks to both human and animal lives. Existing solutions such as electric fencing, camera-based surveillance, and manual monitoring are either harmful, expensive, or inefficient.This project proposes the design and development of a cost-effective, eco-friendly, AI-driven ultrasound-based system capable of detecting, classifying, and deterring wild animals from entering agricultural fields. The system utilizes edge-based artificial intelligence, ultrasonic sensing, and IoT-enabled alert mechanisms to provide real-time warnings to nearby communities. Additionally, it collects movement data to analyze migration patterns from forest reserves to human habitation areas.The proposed solution aims to minimize crop damage, reduce human–wildlife conflict, and promote sustainable coexistence through intelligent automation. |
| AUTHOR | DR.P.N.PALANISAMY, GUNNATHI.K, KARTHICKRAJA.S, JOSHVAEBINESAR.J, HARIHARAN.R Associate Professor, Department of Electronics and Communication Engineering, Mahendra College of Engineering, Salem, Tamil Nadu, India UG Students, Department of ECE, Mahendra College of Engineering, Salem, Tamil Nadu, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404133 |
| pdf/133_AI-Based Smart Electric Fence System with IoT Monitoring and Adaptive Voltage Control.pdf | |
| KEYWORDS | |
| References | 1. A. Kumar and R. Singh, “Smart Agriculture Using IoT Technologies,” International Journal of Engineering Research, vol. 10, no. 3, pp. 45–50, 2021. 2. S. Patel et al., “AI-Based Animal Detection System for Crop Protection,” IEEE Transactions on Smart Agriculture, vol. 5, no. 2, pp. 120–128, 2022. 3. Espressif Systems, “ESP32-CAM Datasheet,” Available: https://www.espressif.com 4. P.N.Palanisamy, Secure Routing In Mobile Adhoc Networks (MANET)* International Journal of InnovativeResearch in Computer and Communication Engineering (IJIRCCE) 2(7) 2320-9801 (2014). 5. J. Redmon et al., “YOLO: Real-Time Object Detection,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 6. Howard et al., “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” Google Research, 2017. 7. Blynk Inc., “Blynk IoT Platform Documentation,” Available: https://blynk.io 8. MathWorks, “IoT-Based Smart Farming Systems,” Technical Documentation, 2020. 9. R. Sharma and P. Verma, “Design of Solar Powered Electric Fence System,” International Journal of Electronics and Communication, vol. 8, no. 4, pp. 89–95, 2019. 10. P.N.Palanisamy, Performance Analysis of Wireless Sensor Networks in Different Terrain Areas using DSR Routing Protocol in NS2 Simulation * International Journal of Innovative Research in Computerand Communication Engineering (IJIRCCE) 2320-9801 (2016). |
