An AI-Based System for Monitoring Laying Hen Behavior Using Computer Vision for Small-Scale Poultry Farms

Jill Italiya, Ahmed Abdelmoamen Ahmed (), Ahmed A. A. Abdel-Wareth and Jayant Lohakare
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Jill Italiya: Department of Computer Science, Prairie View A&M University, Prairie View, TX 77446, USA
Ahmed Abdelmoamen Ahmed: Department of Computer Science, Prairie View A&M University, Prairie View, TX 77446, USA
Ahmed A. A. Abdel-Wareth: Poultry Center, Cooperative Agricultural Research Center, Prairie View A&M University, Prairie View, TX 77446, USA
Jayant Lohakare: Poultry Center, Cooperative Agricultural Research Center, Prairie View A&M University, Prairie View, TX 77446, USA

Agriculture, 2025, vol. 15, issue 18, 1-20

Abstract: Small-scale poultry farms often lack access to advanced monitoring tools and rely heavily on manual observation, which is time-consuming, inconsistent, and insufficient for precise flock management. Feeding and drinking behaviors are critical, as they serve as early indicators of health and environmental issues. With global poultry production expanding, raising over 70 billion hens annually, there is an urgent need for intelligent, low-cost systems that can continuously and accurately monitor bird behavior in resource-limited farm settings. This paper presents the development of a computer vision-based chicken behavior monitoring system, specifically designed for small barn environments where at most 10–15 chickens are housed at any time. The developed system consists of an object detection model, created on top of the YOLOv8 model, trained with an imagery dataset of laying hen, feeder, and waterer objects. Although chickens are visually indistinguishable, the system processes each detection per frame using bounding boxes and movement-based approximation identification rather than continuous identity tracking. The approach simplifies the tracking process without losing valuable behavior insights. Over 700 frames were annotated manually for high-quality labeled data, with different lighting, hen positions, and interaction angles with dispensers. The images were annotated in YOLO format and used for training the detection model for 100 epochs, resulting in a model having an average mean average precision (mAP@0.5) metric value of 91.5% and a detection accuracy of over 92%. The proposed system offers an efficient, low-cost solution for monitoring chicken feeding and drinking behaviors in small-scale farms, supporting improved management and early health detection.

Keywords: machine learning (ML); computer vision (CV); poultry farms; laying hen; behavioral monitoring (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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