A Machine Vision-Based Method for Monitoring Broiler Chicken Floor Distribution
The proper spatial distribution of chickens is an indication of a healthy flock. Routine inspections of broiler chicken floor distribution are done manually in commercial grow-out houses every day, which is labor intensive and time consuming. This task requires an efficient and automatic system that...
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| Format: | Article |
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MDPI AG
2020-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/11/3179 |
| _version_ | 1797566268796043264 |
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| author | Yangyang Guo Lilong Chai Samuel E. Aggrey Adelumola Oladeinde Jasmine Johnson Gregory Zock |
| author_facet | Yangyang Guo Lilong Chai Samuel E. Aggrey Adelumola Oladeinde Jasmine Johnson Gregory Zock |
| author_sort | Yangyang Guo |
| collection | DOAJ |
| description | The proper spatial distribution of chickens is an indication of a healthy flock. Routine inspections of broiler chicken floor distribution are done manually in commercial grow-out houses every day, which is labor intensive and time consuming. This task requires an efficient and automatic system that can monitor the chicken’s floor distributions. In the current study, a machine vision-based method was developed and tested in an experimental broiler house. For the new method to recognize bird distribution in the images, the pen floor was virtually defined/divided into drinking, feeding, and rest/exercise zones. As broiler chickens grew, the images collected each day were analyzed separately to avoid biases caused by changes of body weight/size over time. About 7000 chicken areas/profiles were extracted from images collected from 18 to 35 days of age to build a BP neural network model for floor distribution analysis, and another 200 images were used to validate the model. The results showed that the identification accuracies of bird distribution in the drinking and feeding zones were 0.9419 and 0.9544, respectively. The correlation coefficient (R), mean square error (MSE), and mean absolute error (MAE) of the BP model were 0.996, 0.038, and 0.178, respectively, in our analysis of broiler distribution. Missed detections were mainly caused by interference with the equipment (e.g., the feeder hanging chain and water line); studies are ongoing to address these issues. This study provides the basis for devising a real-time evaluation tool to detect broiler chicken floor distribution and behavior in commercial facilities. |
| first_indexed | 2024-03-10T19:24:19Z |
| format | Article |
| id | doaj.art-f22c750bd4854d01b3889873ff64d9b5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T19:24:19Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f22c750bd4854d01b3889873ff64d9b52023-11-20T02:46:02ZengMDPI AGSensors1424-82202020-06-012011317910.3390/s20113179A Machine Vision-Based Method for Monitoring Broiler Chicken Floor DistributionYangyang Guo0Lilong Chai1Samuel E. Aggrey2Adelumola Oladeinde3Jasmine Johnson4Gregory Zock5Department of Poultry Science, College of Agricultural & Environmental Sciences, University of Georgia, Athens, GA 30602, USADepartment of Poultry Science, College of Agricultural & Environmental Sciences, University of Georgia, Athens, GA 30602, USADepartment of Poultry Science, College of Agricultural & Environmental Sciences, University of Georgia, Athens, GA 30602, USADepartment of Poultry Science, College of Agricultural & Environmental Sciences, University of Georgia, Athens, GA 30602, USADepartment of Poultry Science, College of Agricultural & Environmental Sciences, University of Georgia, Athens, GA 30602, USADepartment of Poultry Science, College of Agricultural & Environmental Sciences, University of Georgia, Athens, GA 30602, USAThe proper spatial distribution of chickens is an indication of a healthy flock. Routine inspections of broiler chicken floor distribution are done manually in commercial grow-out houses every day, which is labor intensive and time consuming. This task requires an efficient and automatic system that can monitor the chicken’s floor distributions. In the current study, a machine vision-based method was developed and tested in an experimental broiler house. For the new method to recognize bird distribution in the images, the pen floor was virtually defined/divided into drinking, feeding, and rest/exercise zones. As broiler chickens grew, the images collected each day were analyzed separately to avoid biases caused by changes of body weight/size over time. About 7000 chicken areas/profiles were extracted from images collected from 18 to 35 days of age to build a BP neural network model for floor distribution analysis, and another 200 images were used to validate the model. The results showed that the identification accuracies of bird distribution in the drinking and feeding zones were 0.9419 and 0.9544, respectively. The correlation coefficient (R), mean square error (MSE), and mean absolute error (MAE) of the BP model were 0.996, 0.038, and 0.178, respectively, in our analysis of broiler distribution. Missed detections were mainly caused by interference with the equipment (e.g., the feeder hanging chain and water line); studies are ongoing to address these issues. This study provides the basis for devising a real-time evaluation tool to detect broiler chicken floor distribution and behavior in commercial facilities.https://www.mdpi.com/1424-8220/20/11/3179broiler chickenhealth and welfareanimal behaviorsprecision farming |
| spellingShingle | Yangyang Guo Lilong Chai Samuel E. Aggrey Adelumola Oladeinde Jasmine Johnson Gregory Zock A Machine Vision-Based Method for Monitoring Broiler Chicken Floor Distribution Sensors broiler chicken health and welfare animal behaviors precision farming |
| title | A Machine Vision-Based Method for Monitoring Broiler Chicken Floor Distribution |
| title_full | A Machine Vision-Based Method for Monitoring Broiler Chicken Floor Distribution |
| title_fullStr | A Machine Vision-Based Method for Monitoring Broiler Chicken Floor Distribution |
| title_full_unstemmed | A Machine Vision-Based Method for Monitoring Broiler Chicken Floor Distribution |
| title_short | A Machine Vision-Based Method for Monitoring Broiler Chicken Floor Distribution |
| title_sort | machine vision based method for monitoring broiler chicken floor distribution |
| topic | broiler chicken health and welfare animal behaviors precision farming |
| url | https://www.mdpi.com/1424-8220/20/11/3179 |
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