AI-Based Monitoring for Enhanced Poultry Flock Management
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 dev...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/14/12/2187 |
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| _version_ | 1846106405570871296 |
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| author | Edmanuel Cruz Miguel Hidalgo-Rodriguez Adiz Mariel Acosta-Reyes José Carlos Rangel Keyla Boniche |
| author_facet | Edmanuel Cruz Miguel Hidalgo-Rodriguez Adiz Mariel Acosta-Reyes José Carlos Rangel Keyla Boniche |
| author_sort | Edmanuel Cruz |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-489a194304db46e79f77345d32b5bdcb |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-489a194304db46e79f77345d32b5bdcb2024-12-27T14:02:57ZengMDPI AGAgriculture2077-04722024-11-011412218710.3390/agriculture14122187AI-Based Monitoring for Enhanced Poultry Flock ManagementEdmanuel Cruz0Miguel Hidalgo-Rodriguez1Adiz Mariel Acosta-Reyes2José Carlos Rangel3Keyla Boniche4Centro Regional Veraguas, Universidad Tecnológica de Panamá, Atalaya 0901, PanamaCentro Regional Veraguas, Universidad Tecnológica de Panamá, Atalaya 0901, PanamaCentro Regional Veraguas, Universidad Tecnológica de Panamá, Atalaya 0901, PanamaSistema Nacional de Investigación (SNI), SENACYT, Panama City 0816-02852, PanamaFacultad de Ingeniería Mecánica, Universidad Tecnológica de Panamá, Panama City 0819-07289, PanamaThe 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.https://www.mdpi.com/2077-0472/14/12/2187poultry monitoringcomputer visionartificial intelligenceflock managementagriculture automation |
| spellingShingle | Edmanuel Cruz Miguel Hidalgo-Rodriguez Adiz Mariel Acosta-Reyes José Carlos Rangel Keyla Boniche AI-Based Monitoring for Enhanced Poultry Flock Management Agriculture poultry monitoring computer vision artificial intelligence flock management agriculture automation |
| title | AI-Based Monitoring for Enhanced Poultry Flock Management |
| title_full | AI-Based Monitoring for Enhanced Poultry Flock Management |
| title_fullStr | AI-Based Monitoring for Enhanced Poultry Flock Management |
| title_full_unstemmed | AI-Based Monitoring for Enhanced Poultry Flock Management |
| title_short | AI-Based Monitoring for Enhanced Poultry Flock Management |
| title_sort | ai based monitoring for enhanced poultry flock management |
| topic | poultry monitoring computer vision artificial intelligence flock management agriculture automation |
| url | https://www.mdpi.com/2077-0472/14/12/2187 |
| work_keys_str_mv | AT edmanuelcruz aibasedmonitoringforenhancedpoultryflockmanagement AT miguelhidalgorodriguez aibasedmonitoringforenhancedpoultryflockmanagement AT adizmarielacostareyes aibasedmonitoringforenhancedpoultryflockmanagement AT josecarlosrangel aibasedmonitoringforenhancedpoultryflockmanagement AT keylaboniche aibasedmonitoringforenhancedpoultryflockmanagement |