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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM66350
Volume:
10 |
7 |
Jul 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of studies in Computer Science (M.Sc.)
Davangere University
Department of studies in Computer Science (M.Sc.)
Davangere University
Post-harvest grain losses caused by birds are the major challenge in agricultural storage facilities, resulting in reduced grain quality, contamination, and significant economic losses. Conventional bird deterrence techniques such as scarecrows, reflective tapes, and continuous sounds emitters gradually become ineffective because birds adapt to these static methods over time. To address these limitations, this research proposes an AI-Powered Bird Detection and Deterrence System that integrates deep learning with the Internet of Things (IoT) to provide intelligent and automated grain storage protection. The proposed system employs the YOLO object detection algorithm to identify birds in real time using live video captured through a camera. Once a bird is detected, the system immediately activates a deterrence mechanism consisting of a buzzer connected to an ESP32 microcontroller while simultaneously sending notifications to the user through the Blynk IoT platform. This Automation minimizes the need for continuous human monitoring and enables timely intervention. The proposed system is designed to achieve high detection accuracy, rapid response time, and low operational cost while promoting environmentally friendly crop protection practices. Experimental implementation demonstrates that integrating artificial intelligence with IoT enhances the efficiency of bird detection, reduces grain losses, and provides a scalable solution suitable for warehouses, grain storage facilities, and agriculture technologies for sustainable post-harvest grain management.
KEYWORDS
The proposed AI-Powered Bird Detection and Deterrence System offers an intelligent and automated solution for protecting post-harvest grain storage from bird intrusion. By integrating the YOLO object detection model, ResNet100/CNN classification, ESP32 microcontroller, and the Blynk IoT platform, the system detects bird species with approximately 95% accuracy and provides real-time monitoring. Upon detection, the system activates a non-lethal buzzer and sends instant notifications to the user, reducing the need for continuous human supervision. The developed system effectively minimizes grain losses, improves storage security, and provides a cost-effective, reliable, and environmentally friendly approach to bird deterrence. Its integration of AI and IoT demonstrates the potential of smart technologies in modern agriculture and offers a scalable solution for enhancing post-harvest grain protection and sustainable farming practices.
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Publication Date
7/25/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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