AI-Based Monitoring for Enhanced Poultry Flock Management

Edmanuel Cruz, Miguel Hidalgo-Rodriguez, Adiz Mariel Acosta-Reyes, José Carlos Rangel () and Keyla Boniche
Additional contact information
Edmanuel Cruz: Centro Regional Veraguas, Universidad Tecnológica de Panamá, Atalaya 0901, Panama
Miguel Hidalgo-Rodriguez: Centro Regional Veraguas, Universidad Tecnológica de Panamá, Atalaya 0901, Panama
Adiz Mariel Acosta-Reyes: Centro Regional Veraguas, Universidad Tecnológica de Panamá, Atalaya 0901, Panama
José Carlos Rangel: Sistema Nacional de Investigación (SNI), SENACYT, Panama City 0816-02852, Panama
Keyla Boniche: Facultad de Ingeniería Mecánica, Universidad Tecnológica de Panamá, Panama City 0819-07289, Panama

Agriculture, 2024, vol. 14, issue 12, 1-26

Abstract: The exponential growth of global poultry production highlights the critical need for efficient flock management, particularly in accurately counting chickens to optimize operations and minimize economic losses. This study advances the application of artificial intelligence (AI) in agriculture by developing and validating an AI-driven automated poultry flock management system using the YOLOv8 object detection model. The scientific objective was to address challenges such as occlusions, lighting variability, and high-density flock conditions, thereby contributing to the broader understanding of computer vision applications in agricultural environments. The practical objective was to create a scalable and reliable system for automated monitoring and decision-making, optimizing resource utilization and improving poultry management efficiency. The prototype achieved high precision (93.1%) and recall (93.0%), demonstrating its reliability across diverse conditions. Comparative analysis with prior models, including YOLOv5, highlights YOLOv8’s superior accuracy and robustness, underscoring its potential for real-world applications. This research successfully achieves its objectives by delivering a system that enhances poultry management practices and lays a strong foundation for future innovations in agricultural automation.

Keywords: poultry monitoring; computer vision; artificial intelligence; flock management; agriculture automation (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: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/12/2187/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/12/2187/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:14:y:2024:i:12:p:2187-:d:1533709

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().