Broiler-Net: A Deep Convolutional Framework for Broiler Behavior Analysis in Poultry Houses
This work addresses the problem of maintaining chicken health and productivity for poultry farmers, but it is incremental as it applies existing deep learning and tracking methods to a specific agricultural domain.
The paper tackled the problem of detecting anomalies in cage-free poultry houses by analyzing chicken behavior, specifically focusing on inactive broiler and huddling behaviors, and resulted in a framework that provides precise and efficient real-time anomaly detection to facilitate timely interventions.
Detecting anomalies in poultry houses is crucial for maintaining optimal chicken health conditions, minimizing economic losses and bolstering profitability. This paper presents a novel real-time framework for analyzing chicken behavior in cage-free poultry houses to detect abnormal behaviors. Specifically, two significant abnormalities, namely inactive broiler and huddling behavior, are investigated in this study. The proposed framework comprises three key steps: (1) chicken detection utilizing a state-of-the-art deep learning model, (2) tracking individual chickens across consecutive frames with a fast tracker module, and (3) detecting abnormal behaviors within the video stream. Experimental studies are conducted to evaluate the efficacy of the proposed algorithm in accurately assessing chicken behavior. The results illustrate that our framework provides a precise and efficient solution for real-time anomaly detection, facilitating timely interventions to maintain chicken health and enhance overall productivity on poultry farms. Github: https://github.com/TaherehZarratEhsan/Chicken-Behavior-Analysis