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@article{189752,
        author = {Kapil G. Dhappadhule},
        title = {CNN-Powered Real-Time Object Detection Framework for Intelligent Farm Monitoring},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {8},
        pages = {10-20},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189752},
        abstract = {Agricultural security is a critical concern as farms are frequently exposed to intrusions by humans, stray animals, and birds, all of which contribute to significant crop damage, reduced yield, and financial loss. Conventional monitoring techniques, such as manual supervision and CCTV- based surveillance, are limited to passive recording and require constant human attention. These systems fail to provide immediate notifications or proactive measures, often allowing incidents to escalate before corrective action can be taken.
This paper proposes a real-time, intelligent surveillance system that combines Convolutional Neural Networks (CNNs) with advanced object detection frameworks such as YOLOv8 to address these challenges. The system continuously monitors farm environments through live video streams, accurately classifies detected entities as humans, animals, or birds, and automatically generates snapshots of intrusion events. To ensure timely intervention, the framework is integrated with the Telegram Bot API, enabling instant alerts to be delivered directly to administrators’ devices.
The system not only improves response time compared to traditional surveillance but also provides a scalable and cost-effective solution that can be deployed across small farms as well as large agricultural landscapes.},
        keywords = {Object Detection, Smart Farming, YOLOv8, CNN, Agricultural Surveillance, Telegram Alerts.},
        month = {December},
        }