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

被引:13
作者
Bao, Yiqin [1 ,2 ]
Lu, Hongbing [2 ]
Zhao, Qiang [3 ]
Yang, Zhongxue [1 ]
Xu, Wenbin [4 ]
机构
[1] Nanjing XiaoZhuang Univ, Coll Informat Engn, Nanjing 211171, Peoples R China
[2] Nanjing Univ, Coll Software, Nanjing 210093, Peoples R China
[3] Schulich Sch Business, Dept Informat Syst, Toronto, ON 416647, Canada
[4] Nanjing Huazhu Ind Intelligent Equipment Co Ltd, Nanjing 211175, Peoples R China
关键词
artificial intelligence; three dimensional total variance; machine learning; classification algorithm; sensor networks; PREDICTION; BROILERS;
D O I
10.3934/mbe.2021306
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页码:6117 / 6135
页数:19
相关论文
共 33 条
[1]   Unsupervised automated monitoring of dairy cows' behavior based on Inertial Measurement Unit attached to their back [J].
Achour, Brahim ;
Belkadi, Malika ;
Aoudjit, Rachida ;
Laghrouche, Mourad .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2019, 167
[2]   Development of an early detection system for lameness of broilers using computer vision [J].
Aydin, A. .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2017, 136 :140-146
[3]   Using 3D vision camera system to automatically assess the level of inactivity in broiler chickens [J].
Aydin, A. .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2017, 135 :4-10
[4]  
[包宗铭 Bao Zongming], 2019, [计算机科学, Computer Science], V46, P267
[5]  
Bi C. G., 2015, COMPUT SCI, V42, P544
[6]   Vocal expression of emotions in mammals: mechanisms of production and evidence [J].
Briefer, E. F. .
JOURNAL OF ZOOLOGY, 2012, 288 (01) :1-20
[7]  
Du L. Q., MACH TOOLS HYD, V49, P1
[8]   Obstructive sleep apnea syndrome detection based on ballistocardiogram via machine learning approach [J].
Gao, Weidong ;
Xu, Yibin ;
Li, Shengshu ;
Fu, Yujun ;
Zheng, Dongyang ;
She, Yingjia .
MATHEMATICAL BIOSCIENCES AND ENGINEERING, 2019, 16 (05) :5672-5686
[9]   Modal identification of a high-rise building subjected to a landfall typhoon via both deterministic and Bayesian methods [J].
He, Yuncheng ;
Liu, Zhen ;
Li, Zhi ;
Wu, Jiurong ;
Fu, Jiyang .
MATHEMATICAL BIOSCIENCES AND ENGINEERING, 2019, 16 (06) :7155-7176
[10]   Method for detecting avian influenza disease of chickens based on sound analysis [J].
Huang, Junduan ;
Wang, Wenqing ;
Zhang, Tiemin .
BIOSYSTEMS ENGINEERING, 2019, 180 :16-24