Advanced Smart Systems for Detecting and Mitigating Aggression in Poultry Farms Using CNN and SVM algorithm

Aggressive behavior in chickens can harm both animal welfare and farm output. Traditional techniques of detecting such behavior are mainly reliant on manual observation , which is inefficient and subject to human mistake. This paper describes a smart system that uses Convolutional Neural Networks (C...

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Bibliographic Details
Published in:2025 3rd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA) pp. 1 - 6
Main Authors: Rajesh, M., Raman, D. Raghu, G, Jaisuriya
Format: Conference Proceeding
Language:English
Published: IEEE 04.04.2025
Subjects:
Accuracy
Aggressive behavior detection
Animal welfare assessment
Animals
Automated monitoring
Behavior recognition
Convolutional neural networks
Convolutional Neural Networks (CNN)
Monitoring
Poultry welfare
Real-time systems
Smart agriculture
Smart farming
Smart systems
Support Vector Machine (SVM)
Support vector machines
Telecommunication computing
Video surveillance
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Summary:Aggressive behavior in chickens can harm both animal welfare and farm output. Traditional techniques of detecting such behavior are mainly reliant on manual observation , which is inefficient and subject to human mistake. This paper describes a smart system that uses Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) algorithms to automatically detect and minimize aggressive behavior in chickens. Sensors and video surveillance were utilized to collect behavioral data, which was then extracted using the CNN model and classified using the SVM. This hybrid technique improves the system's accuracy in spotting aggression, provides real-time insights, and allows for quick response. The proposed real-time insights, and allows for quick response. The proposed system aims to optimize animal welfare and improve overall farm management, providing an automated, accurate, and scalable solution for poultry welfare monitoring.
DOI:10.1109/ICAECA63854.2025.11012392