Detecting Intended Target Birds and Using Frightened Techniques in Crops to Preserve Yield

Anup Ritti; J. Chandrashekhara

Abstract

An invention that is more appealing and practical for the general public is required as the world moves toward new trends and technology. The study uses AI and IoT technology to reduce crop damage caused by birds, a significant threat to crops. The automated system identifies and discourages specific bird species, reducing the cost of traditional deterrents. The system uses YOLO, a high-performance object detection model, to identify birds in real-time using a webcam feed. It then uses a ResNet100-based CNN for selective bird classification, minimizing disruption to wildlife. The system identifies a bird and triggers automated responses, providing real-time notifications via the Blynk platform. A buzzer on an ESP32 board scares birds, protecting crops. The ESP32 board manages the buzzer and communication with the Blynk server. The Paper utilizes AI and IoT to automate bird detection and deterrence, reducing human intervention and providing a cost-effective solution for farmers.

Keywords

AI, IoT, YOLO, Blynk Server, CNN

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Cites this article as

A. Ritti, . J. Chandrashekhara, "Detecting Intended Target Birds and Using Frightened Techniques in Crops to Preserve Yield", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-12, no.5, pp.24-27, 2024. Available from: https://doi.org/10.55524/ijircst.2024.12.5.4

Corresponding Author

Anup Ritti

Assistant Professor, Department of Studies in Computer Science, Davangere University, Davangere, India

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