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Identifying Images of Dead Chickens with a Chicken Removal System Integrated with a Deep Learning Algorithm.

Hung-Wei Liu¹, Chia-Hung Chen¹, Yao-Chuan Tsai¹

¹Department of Bio-Industrial Mechatronics Engineering, National Chung Hsing University, Taichung 402, Taiwan.

Sensors (Basel, Switzerland)

|June 2, 2021

Summary

A novel automated system efficiently removes dead chickens from Taiwanese poultry houses using robotic arms and AI-powered detection. This innovation enhances biosecurity by minimizing human contact and preventing disease spread in the chicken industry.

Keywords:

YOLO v4 broiler dead chicken poultry house removal system

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Area of Science:

Agricultural Engineering
Robotics
Artificial Intelligence

Background:

The Taiwanese chicken industry relies on manual inspection of broiler health, posing biosecurity risks.
Traditional methods of removing deceased poultry increase the potential for disease transmission.

Purpose of the Study:

To design and construct an automated system for removing dead chickens from poultry houses.
To enhance biosecurity and reduce human-animal contact in chicken farming.

Main Methods:

A novel system integrating walking, removal, and storage mechanisms was developed.
Robotic arms, conveyor belts, and a tracked vehicle facilitate chicken removal.
Deep learning, specifically the YOLO v4 algorithm, was employed for automated dead chicken identification via camera monitoring.

Main Results:

The system achieved a precision of 95.24% in identifying dead chickens.
The automated system successfully transported dead chickens to a storage cache.
The system demonstrated efficient operation with a walking speed of 3.3 cm/s and capacity for two chickens per operation.

Conclusions:

The developed automated system significantly improves biosecurity in poultry houses.
Integration of AI and robotics offers a viable solution for enhancing automation in the chicken industry.
Reduced human-poultry contact is a key benefit for disease prevention.