Detection system of dead and sick chickens in large scale farms based on artificial intelligence

With the continuous enrichment of scientific and technological means, the production of most chicken farms has been able to achieve automation, but for the dead and sick chickens in the farm, there is no automatic monitoring step, only through continuous manual inspection and discovery. In the face...

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Autores principales:	Yiqin Bao, Hongbing Lu, Qiang Zhao, Zhongxue Yang, Wenbin Xu
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	artificial intelligence three dimensional total variance machine learning classification algorithm sensor networks Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/a71d6f90e155458f9ebb74f458aa4506
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spelling	oai:doaj.org-article:a71d6f90e155458f9ebb74f458aa45062021-11-11T01:07:53ZDetection system of dead and sick chickens in large scale farms based on artificial intelligence10.3934/mbe.20213061551-0018https://doaj.org/article/a71d6f90e155458f9ebb74f458aa45062021-07-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021306?viewType=HTMLhttps://doaj.org/toc/1551-0018With the continuous enrichment of scientific and technological means, the production of most chicken farms has been able to achieve automation, but for the dead and sick chickens in the farm, there is no automatic monitoring step, only through continuous manual inspection and discovery. In the face of this problem, there are many solutions to identify dead and sick chickens through sound and image, but they can not achieve the ideal effect. In this paper, a sensor detection method based on artificial intelligence is proposed. This method 1) The maximum displacement of chicken activity is measured by fastening a foot ring on each chicken, and the three-dimensional total variance is designed and calculated to represent the chicken activity intensity. 2) The detection terminal collects the sensing data of foot ring through ZigBee network. 3) The state of chicken (dead chicken and sick chicken) can be identified by machine learning algorithm. This method of artificial intelligence combined with sensor network not only has high recognition rate, but also can reduce the operation cost. The practical results show that the accuracy of the system to identify dead and sick chickens is 95.6%, and the cost of the system running for 4 years can be reduced by 25% compared with manual operation.Yiqin Bao Hongbing LuQiang ZhaoZhongxue YangWenbin XuAIMS Pressarticleartificial intelligencethree dimensional total variancemachine learningclassification algorithmsensor networksBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 5, Pp 6117-6135 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence three dimensional total variance machine learning classification algorithm sensor networks Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	artificial intelligence three dimensional total variance machine learning classification algorithm sensor networks Biotechnology TP248.13-248.65 Mathematics QA1-939 Yiqin Bao Hongbing Lu Qiang Zhao Zhongxue Yang Wenbin Xu Detection system of dead and sick chickens in large scale farms based on artificial intelligence
description	With the continuous enrichment of scientific and technological means, the production of most chicken farms has been able to achieve automation, but for the dead and sick chickens in the farm, there is no automatic monitoring step, only through continuous manual inspection and discovery. In the face of this problem, there are many solutions to identify dead and sick chickens through sound and image, but they can not achieve the ideal effect. In this paper, a sensor detection method based on artificial intelligence is proposed. This method 1) The maximum displacement of chicken activity is measured by fastening a foot ring on each chicken, and the three-dimensional total variance is designed and calculated to represent the chicken activity intensity. 2) The detection terminal collects the sensing data of foot ring through ZigBee network. 3) The state of chicken (dead chicken and sick chicken) can be identified by machine learning algorithm. This method of artificial intelligence combined with sensor network not only has high recognition rate, but also can reduce the operation cost. The practical results show that the accuracy of the system to identify dead and sick chickens is 95.6%, and the cost of the system running for 4 years can be reduced by 25% compared with manual operation.
format	article
author	Yiqin Bao Hongbing Lu Qiang Zhao Zhongxue Yang Wenbin Xu
author_facet	Yiqin Bao Hongbing Lu Qiang Zhao Zhongxue Yang Wenbin Xu
author_sort	Yiqin Bao
title	Detection system of dead and sick chickens in large scale farms based on artificial intelligence
title_short	Detection system of dead and sick chickens in large scale farms based on artificial intelligence
title_full	Detection system of dead and sick chickens in large scale farms based on artificial intelligence
title_fullStr	Detection system of dead and sick chickens in large scale farms based on artificial intelligence
title_full_unstemmed	Detection system of dead and sick chickens in large scale farms based on artificial intelligence
title_sort	detection system of dead and sick chickens in large scale farms based on artificial intelligence
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/a71d6f90e155458f9ebb74f458aa4506
work_keys_str_mv	AT yiqinbao detectionsystemofdeadandsickchickensinlargescalefarmsbasedonartificialintelligence AT hongbinglu detectionsystemofdeadandsickchickensinlargescalefarmsbasedonartificialintelligence AT qiangzhao detectionsystemofdeadandsickchickensinlargescalefarmsbasedonartificialintelligence AT zhongxueyang detectionsystemofdeadandsickchickensinlargescalefarmsbasedonartificialintelligence AT wenbinxu detectionsystemofdeadandsickchickensinlargescalefarmsbasedonartificialintelligence
_version_	1718439611288518656

Detection system of dead and sick chickens in large scale farms based on artificial intelligence

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