Machine Learning Approaches for Bird Identification to Protect Agriculture Crops

Abstract

Agriculture faces substantial crop losses annually due to bird predation, particularly in cereal, oilseed, and fruit cultivation. Conventional deterrence techniques, such as scarecrows, reflective tapes, and netting, often prove ineffective over time as birds habituate to static stimuli. This paper introduces an intelligent, automated, and farmer-centric bird deterrence system built on deep learning-based real-time bird detection. The proposed system continuously monitors the field and accurately identifies the presence of birds while reliably ignoring false triggers caused by wind, moving foliage, insects, or shadows. Upon confident detection of avian activity, it instantly activates high-intensity, biologically relevant sonic repellents—including variable predator calls and distress signals—for short, unpredictable durations. This approach prevents habituation by introducing randomness and using sounds that naturally provoke strong escape responses in birds. A smart cooldown mechanism further avoids overuse and preserves effectiveness over extended periods. Designed to be low-cost, energy-efficient, and fully autonomous, this paper provides small and marginal farmers with a practical, scalable, and sustainable solution that significantly reduces crop damage without requiring constant human intervention or expensive infrastructure.