Abstract
In the realm of precision agriculture, safeguarding crops against intrusions by animals, pests, or other environmental threats is paramount to ensuring yield quality and productivity. This study presents a multimodal approach to crop protection through the integration of acoustic event detection and visual scene analysis. The proposed system employs parallel pipelines for audio and image classification to comprehensively monitor farmland environments. Environmental audio signals are processed using Mel-spectrogram features and classified via a convolutional neural network (CNN) architecture to detect anomalous acoustic events such as animal vocalizations, machinery noises, or weather disturbances. Simultaneously, visual data captured through surveillance cameras is analysed using a deep learning-based image classifier to identify potential threats, including the presence of animals or human intruders.
By fusing predictions from both modalities, the system enhances detection robustness and reduces false alarms. Real-time alerts are generated to notify farmers of potential crop hazards, thereby enabling timely intervention. The multimodal design significantly outperforms unimodal systems in terms of detection accuracy and responsiveness. This research demonstrates the efficacy of combining audio and visual modalities in a unified framework to achieve intelligent, automated, and scalable crop protection.


Introduction
Crop protection from birds remains a critical challenge in agriculture, leading to significant economic losses for farmers. Traditional deterrents like scarecrows, nets, and chemical repellents are ineffective in the long term and may harm the environment.
Our proposed solution leverages multimodal detection technology by integrating audio and visual data for real-time bird detection and deterrence.
This system will:
• Use cameras and microphones to monitor the field.
• Employ deep learning models to analyze bird movements and sounds.
• Activate automated deterrents, such as alarm sounds or pre-recorded speech, to scare birds away.
• Provide real-time monitoring via a mobile application, reducing labor costs and environmental harm.
This AI-driven approach ensures efficient, automated, and sustainable crop protection, minimizing yield loss and financial damage.