Research on an Identification and Grasping Device for Dead Yellow-Feather Broilers in Flat Houses Based on Deep Learning

Chengrui Xin, Hengtai Li, Yuhua Li, Meihui Wang, Weihan Lin, Shuchen Wang, Wentian Zhang, Maohua Xiao and Xiuguo Zou ()
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Chengrui Xin: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Hengtai Li: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Yuhua Li: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Meihui Wang: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Weihan Lin: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Shuchen Wang: School of Electrical and Control Engineering, Xuzhou University of Technology, Xuzhou 221018, China
Wentian Zhang: Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
Maohua Xiao: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Xiuguo Zou: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China

Agriculture, 2024, vol. 14, issue 9, 1-19

Abstract: The existence of dead broilers in flat broiler houses poses significant challenges to large-scale and welfare-oriented broiler breeding. To ensure the timely identification and removal of dead broilers, a mobile device based on visual technology for grasping them was meticulously designed in this study. Among the multiple recognition models explored, the YOLOv6 model was selected due to its exceptional performance, attaining an impressive 86.1% accuracy in identification. This model, when integrated with a specially designed robotic arm, forms a potent combination for effectively handling the task of grasping dead broilers. Extensive experiments were conducted to validate the efficacy of the device. The results reveal that the device achieved an average grasping rate of dead broilers of 81.3%. These findings indicate that the proposed device holds great potential for practical field deployment, offering a reliable solution for the prompt identification and grasping of dead broilers, thereby enhancing the overall management and welfare of broiler populations.

Keywords: dead broiler recognition and grasping; deep learning; visual technology; mechanical arm (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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