AI-Enabled Predictive Beacon-Based Non-Invasive Bird Distraction System for Industrial Environments

Abstract

Bird intrusions pose consistent operational costs, hygienic expenses, and maintenance expenses on industrial plants, warehouses, agricultural plants, airports, and solar power plants. At least 70% of big industries experience bird intrusions on regular basis, while almost 50% of bird deterrence devices fail due to bird habituation. Current strategies for bird deterring include physical protection, visual scares, and continuous acoustical emissions and are reactive, energy-consuming or ineffective once the birds get used to repeated stimuli. This paper proposes an AI-Enabled Predictive Beacon-Based Non-Invasive Bird Distractive Device, which is a combination of Facebook Prophet time series model of forecast and modular beacon, which features adaptive acoustical signals, high-power LED light and magnetic assist module. Temporal data about the occurrence of bird activities (date, month, season, time) will be used to predict future activity rate and feed three-stage decision algorithm (Idle-Warning-Active), executed by the Flask-based prediction engine, React-based monitoring interface and Arduino Uno-based beacon, which executes the physical bird deterring actions. A working prototype based on HC-SR04 ultrasonic sensor, servo-controlled scanning, LED and buzzer demonstrates the synergy between hardware and software. Comparative study shows significantly lower energy consumption and less habituation chances for such system. The architecture is modular, low-cost (approximately ₹1,500–₹2,000 per unit), and extensible toward cloud connectivity, multi-zone deployment, and edge-AI species recognition.