Research into Heat Stress Behavior Recognition and Evaluation Index for Yellow-Feathered Broilers, Based on Improved Cascade Region-Based Convolutional Neural Network

Yungang Bai, Jie Zhang, Yang Chen, Heyang Yao, Chengrui Xin, Sunyuan Wang, Jiaqi Yu, Cairong Chen, Maohua Xiao and Xiuguo Zou ()
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Yungang Bai: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Jie Zhang: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Yang Chen: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Heyang Yao: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Chengrui Xin: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Sunyuan Wang: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Jiaqi Yu: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
Cairong Chen: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Maohua Xiao: College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
Xiuguo Zou: College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China

Agriculture, 2023, vol. 13, issue 6, 1-17

Abstract: The heat stress response of broilers will adversely affect the large-scale and welfare of the breeding of broilers. In order to detect the heat stress state of broilers in time, make reasonable adjustments, and reduce losses, this paper proposed an improved Cascade R-CNN (Region-based Convolutional Neural Networks) model based on visual technology to identify the behavior of yellow-feathered broilers. The improvement of the model solved the problem of the behavior recognition not being accurate enough when broilers were gathered. The influence of different iterations on the model recognition effect was compared, and the optimal model was selected. The final average accuracy reached 88.4%. The behavioral image data with temperature and humidity data were combined, and the heat stress evaluation model was optimized using the PLSR (partial least squares regression) method. The behavior recognition results and optimization equations were verified, and the test accuracy reached 85.8%. This proves the feasibility of the heat stress evaluation optimization equation, which can be used for reasonably regulating the broiler chamber.

Keywords: yellow-feathered broilers; behavior recognition; deep learning; temperature and humidity index; heat stress evaluation equation (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: 2023
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