A Lightweight Neural Network-Based Method for Detecting Estrus Behavior in Ewes

Longhui Yu, Yuhai Pu, Honglei Cen, Jingbin Li (), Shuangyin Liu (), Jing Nie, Jianbing Ge, Linze Lv, Yali Li, Yalei Xu, Jianjun Guo, Hangxing Zhao and Kang Wang
Additional contact information
Longhui Yu: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
Yuhai Pu: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
Honglei Cen: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
Jingbin Li: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
Shuangyin Liu: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
Jing Nie: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
Jianbing Ge: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
Linze Lv: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
Yali Li: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
Yalei Xu: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
Jianjun Guo: College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Hangxing Zhao: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
Kang Wang: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China

Agriculture, 2022, vol. 12, issue 8, 1-21

Abstract: We propose a lightweight neural network-based method to detect the estrus behavior of ewes. Our suggested method is mainly proposed to solve the problem of not being able to detect ewe estrus behavior in a timely and accurate manner in large-scale meat sheep farms. The three main steps of our proposed methodology include constructing the dataset, improving the network structure, and detecting the ewe estrus behavior based on the lightweight network. First, the dataset was constructed by capturing images from videos with estrus crawling behavior, and the data enhancement was performed to improve the generalization ability of the model at first. Second, the original Darknet-53 was replaced with the EfficientNet-B0 for feature extraction in YOLO V3 neural network to make the model lightweight and the deployment easier, thus shortening the detection time. In order to further obtain a higher accuracy of detecting the ewe estrus behavior, we joined the feature layers to the SENet attention module. Finally, the comparative results demonstrated that the proposed method had higher detection accuracy and FPS, as well as a smaller model size than the YOLO V3. The precision of the proposed scheme was 99.44%, recall was 95.54%, F1 value was 97%, AP was 99.78%, FPS was 48.39 f/s, and Model Size was 40.6 MB. This study thus provides an accurate, efficient, and lightweight detection method for the ewe estrus behavior in large-scale mutton sheep breeding.

Keywords: behavior detection; deep learning; EfficientNet; ewe estrus; YOLO v3 (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: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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