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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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Autores principales: Guo, Yangyang, Chai, Lilong, Aggrey, Samuel E., Oladeinde, Adelumola, Johnson, Jasmine, Zock, Gregory
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7309057/
https://www.ncbi.nlm.nih.gov/pubmed/32503296
http://dx.doi.org/10.3390/s20113179
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author Guo, Yangyang
Chai, Lilong
Aggrey, Samuel E.
Oladeinde, Adelumola
Johnson, Jasmine
Zock, Gregory
author_facet Guo, Yangyang
Chai, Lilong
Aggrey, Samuel E.
Oladeinde, Adelumola
Johnson, Jasmine
Zock, Gregory
author_sort Guo, Yangyang
collection PubMed
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.
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spelling pubmed-73090572020-06-25 A Machine Vision-Based Method for Monitoring Broiler Chicken Floor Distribution Guo, Yangyang Chai, Lilong Aggrey, Samuel E. Oladeinde, Adelumola Johnson, Jasmine Zock, Gregory Sensors (Basel) Article 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. MDPI 2020-06-03 /pmc/articles/PMC7309057/ /pubmed/32503296 http://dx.doi.org/10.3390/s20113179 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Guo, Yangyang
Chai, Lilong
Aggrey, Samuel E.
Oladeinde, Adelumola
Johnson, Jasmine
Zock, Gregory
A Machine Vision-Based Method for Monitoring Broiler Chicken Floor Distribution
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 Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7309057/
https://www.ncbi.nlm.nih.gov/pubmed/32503296
http://dx.doi.org/10.3390/s20113179
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