Convolutional Neural Network-based Model for Intrusion Detection in Poultry Farms
Keywords:
Convolutional Neural Network (CNN), Intrusion Detection, Machine Learning, Smart Poultry Farm, Deep Learning, Image ClassificationAbstract
This research develops a convolutional neural network-based model for intrusion detection in poultry farms. Poultry farm is a vital sector agriculture that aims at meeting the growing demand for high-protein food. However, managing large poultry populations poses challenges, particularly in monitoring birds’ health and detecting intrusions. This study leverages CNN to automate farm surveillance, enabling continuous monitoring, early anomaly detection, and rapid response to threats such as animal invasions. The proposed intrusion detection system significantly enhances poultry welfare, strengthens farm security, and advances machine learning applications in agriculture. Experimental results demonstrate the model's effectiveness, achieving a high Mean Average Precision (mAP) of 0.995. The confusion matrix indicates accurate classification, correctly identifying 25 out of 27 chickens, all 16 owners, and 11 out of 12 other subjects, while background images were properly detected.
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