Research on machine vision online monitoring system for egg production and quality in cage environment
In the domain of egg production, the application of automation technologies is essential for boosting productivity and quality. This study introduces an online monitoring system designed for egg quality assessment within caged environments, incorporating a robotic patrol system for egg localization...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Poultry Science |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0032579124011301 |
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| author | Zhenlong Wu Hengyuan Zhang Cheng Fang |
| author_facet | Zhenlong Wu Hengyuan Zhang Cheng Fang |
| author_sort | Zhenlong Wu |
| collection | DOAJ |
| description | In the domain of egg production, the application of automation technologies is essential for boosting productivity and quality. This study introduces an online monitoring system designed for egg quality assessment within caged environments, incorporating a robotic patrol system for egg localization and a fixed video stream for quality analysis. The project involved upgrading traditional henhouses with enhanced wireless connectivity and developing data transmission techniques for video streams and image data. The core of the system, an enhanced You Only Look Once Version 8-small (YOLOv8s) model, was augmented by substituting the Residual Network-18 backbone and integrating the Shuffle Attention mechanism, significantly improving egg detection precision. This refined model was implemented on Jetson AGX Orin industrial computer to facilitate real-world applications. To diverse operational needs, two distinct post-processing algorithms were developed: one for counting eggs and detecting abnormalities during robotic patrols, and another for assessing egg quality through fixed video streams, which measured crucial parameters such as egg dimensions and shape indexes. Experimental results revealed that the henhouse average network latencies of 35 ms, with signal strengths between -30 and -71 dBm, ensuring data transmission to the poultry management system. The enhanced YOLOv8s model, deployed on the Jetson AGX Orin, demonstrated well improvements: a Precision of 94.0 % (+2.4 %), Recall rate of 92.8 % (+4.6 %), Average Precision50:95 of 91.5 % (+3 %) and F1 score of 93.4 % (+3.9 %), with a minor decrease in detection speed to 91.7 Frame Per Second (-18.2). Field experiment in 60 chicken cages during robotic patrols achieved an egg recognition rate of 98.9 %, validating the system's effectiveness. In fixed settings, an 83-minute experiment managed to analyze egg numbers and abnormalities, attaining a 100 % recognition rate with all scoring data promptly relayed back to the management system. Overall, this research introduces a comprehensive system for monitoring egg production and quality in cage environments, addressing manual recording and quality assessment challenges in caged poultry farming. This study is crucial for optimizing modern livestock management, enhancing production efficiency, and ensuring animal welfare. |
| format | Article |
| id | doaj-art-aa060ec266dd4cebb53f500af8ba13b4 |
| institution | Kabale University |
| issn | 0032-5791 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Poultry Science |
| spelling | doaj-art-aa060ec266dd4cebb53f500af8ba13b42025-01-22T05:40:27ZengElsevierPoultry Science0032-57912025-01-011041104552Research on machine vision online monitoring system for egg production and quality in cage environmentZhenlong Wu0Hengyuan Zhang1Cheng Fang2College of Engineering, South China Agricultural University, Guangzhou, China; State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, ChinaCollege of Engineering, South China Agricultural University, Guangzhou, China; State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, ChinaCollege of Engineering, South China Agricultural University, Guangzhou, China; State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, China; Corresponding author: College of Engineering, South China Agricultural University, Guangzhou, 510642, China.In the domain of egg production, the application of automation technologies is essential for boosting productivity and quality. This study introduces an online monitoring system designed for egg quality assessment within caged environments, incorporating a robotic patrol system for egg localization and a fixed video stream for quality analysis. The project involved upgrading traditional henhouses with enhanced wireless connectivity and developing data transmission techniques for video streams and image data. The core of the system, an enhanced You Only Look Once Version 8-small (YOLOv8s) model, was augmented by substituting the Residual Network-18 backbone and integrating the Shuffle Attention mechanism, significantly improving egg detection precision. This refined model was implemented on Jetson AGX Orin industrial computer to facilitate real-world applications. To diverse operational needs, two distinct post-processing algorithms were developed: one for counting eggs and detecting abnormalities during robotic patrols, and another for assessing egg quality through fixed video streams, which measured crucial parameters such as egg dimensions and shape indexes. Experimental results revealed that the henhouse average network latencies of 35 ms, with signal strengths between -30 and -71 dBm, ensuring data transmission to the poultry management system. The enhanced YOLOv8s model, deployed on the Jetson AGX Orin, demonstrated well improvements: a Precision of 94.0 % (+2.4 %), Recall rate of 92.8 % (+4.6 %), Average Precision50:95 of 91.5 % (+3 %) and F1 score of 93.4 % (+3.9 %), with a minor decrease in detection speed to 91.7 Frame Per Second (-18.2). Field experiment in 60 chicken cages during robotic patrols achieved an egg recognition rate of 98.9 %, validating the system's effectiveness. In fixed settings, an 83-minute experiment managed to analyze egg numbers and abnormalities, attaining a 100 % recognition rate with all scoring data promptly relayed back to the management system. Overall, this research introduces a comprehensive system for monitoring egg production and quality in cage environments, addressing manual recording and quality assessment challenges in caged poultry farming. This study is crucial for optimizing modern livestock management, enhancing production efficiency, and ensuring animal welfare.http://www.sciencedirect.com/science/article/pii/S0032579124011301EggLayered cage farmingObject detectionPoultryPrecision livestock farming |
| spellingShingle | Zhenlong Wu Hengyuan Zhang Cheng Fang Research on machine vision online monitoring system for egg production and quality in cage environment Poultry Science Egg Layered cage farming Object detection Poultry Precision livestock farming |
| title | Research on machine vision online monitoring system for egg production and quality in cage environment |
| title_full | Research on machine vision online monitoring system for egg production and quality in cage environment |
| title_fullStr | Research on machine vision online monitoring system for egg production and quality in cage environment |
| title_full_unstemmed | Research on machine vision online monitoring system for egg production and quality in cage environment |
| title_short | Research on machine vision online monitoring system for egg production and quality in cage environment |
| title_sort | research on machine vision online monitoring system for egg production and quality in cage environment |
| topic | Egg Layered cage farming Object detection Poultry Precision livestock farming |
| url | http://www.sciencedirect.com/science/article/pii/S0032579124011301 |
| work_keys_str_mv | AT zhenlongwu researchonmachinevisiononlinemonitoringsystemforeggproductionandqualityincageenvironment AT hengyuanzhang researchonmachinevisiononlinemonitoringsystemforeggproductionandqualityincageenvironment AT chengfang researchonmachinevisiononlinemonitoringsystemforeggproductionandqualityincageenvironment |