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Computer Vision Applied to the Analysis of Pig Behavior Patterns in an Air-Conditioned Environment.

Maria de Fatima Araújo Alves¹, Héliton Pandorfi², Rodrigo Gabriel Ferreira Soares²

¹Department of Agricultural Engineering, Federal Rural University of Pernambuco, Dom Manoel de Medeiros Avenue, SN, Dois Irmãos, Recife 52171-900, Pernambuco, Brazil.

Animals : an Open Access Journal From MDPI

|May 13, 2026

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Computer vision accurately monitors pig behavior, including feeding and drinking, reducing manual labor. This technology enhances animal well-being and farm productivity by enabling efficient pig behavior analysis.

Area of Science:

Animal Science
Computer Vision
Agricultural Technology

Background:

Monitoring pig behavior is crucial for animal welfare and farm productivity.
Traditional methods of observation are labor-intensive and can disrupt animal behavior.
Automated systems are needed to efficiently track pig activities.

Purpose of the Study:

To develop and evaluate a computer vision system for automated pig behavior monitoring.
To identify feeding, drinking, standing, and lying behaviors using artificial intelligence.
To assess the system's accuracy in an air-conditioned agricultural environment.

Main Methods:

Utilized microcameras to record pig behavior over 92 days.
Employed the YOLOv5 algorithm for animal detection and behavior recognition.

Keywords:

animal welfare automatic recognition smart pig farming technology in swine production

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Defined specific criteria for identifying feeding and water intake based on pig position and feeder/drinker zone occupancy.

Main Results:

The YOLOv5 model achieved high accuracy (97.3%) and recall (96.1%) in detecting pigs.
The system demonstrated excellent performance in recognizing feeding behavior (97.5% accuracy) and water consumption (97.0% recall).
The computer vision approach significantly reduces the need for manual observation.

Conclusions:

Computer vision offers an efficient and accurate method for monitoring pig behavior.
This technology can improve animal welfare and optimize pig farming operations.
The developed system provides a valuable tool for real-time behavioral analysis in agricultural settings.