Cargando…
A Recognition Method of Ewe Estrus Crawling Behavior Based on Multi-Target Detection Layer Neural Network
SIMPLE SUMMARY: The timely and accurate detection of ewe estrus behavior in precision animal husbandry is an important research topic. The timely detection of estrus ewes in mutton sheep breeding can not only protect the welfare of ewes themselves, but also improve the interests of breeding enterpri...
Autores principales: | , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9913191/ https://www.ncbi.nlm.nih.gov/pubmed/36766301 http://dx.doi.org/10.3390/ani13030413 |
_version_ | 1784885365035761664 |
---|---|
author | Yu, Longhui Guo, Jianjun Pu, Yuhai Cen, Honglei Li, Jingbin Liu, Shuangyin Nie, Jing Ge, Jianbing Yang, Shuo Zhao, Hangxing Xu, Yalei Wu, Jianglin Wang, Kang |
author_facet | Yu, Longhui Guo, Jianjun Pu, Yuhai Cen, Honglei Li, Jingbin Liu, Shuangyin Nie, Jing Ge, Jianbing Yang, Shuo Zhao, Hangxing Xu, Yalei Wu, Jianglin Wang, Kang |
author_sort | Yu, Longhui |
collection | PubMed |
description | SIMPLE SUMMARY: The timely and accurate detection of ewe estrus behavior in precision animal husbandry is an important research topic. The timely detection of estrus ewes in mutton sheep breeding can not only protect the welfare of ewes themselves, but also improve the interests of breeding enterprises. With the continuous increase in the scale of mutton sheep breeding and the gradual intensification of breeding methods, the traditional manual detection methods require high labor intensity, and the contact sensor detection methods will cause stress reaction problems in ewes. In recent years, the rapid development of deep learning technology has brought on new possibilities. We propose a method for the recognition ewe estrus based on a multi-target detection layer neural network. The results show that the method can meet the requirements of timely and accurate detection of ewe estrus behavior in large-scale mutton sheep breeding. ABSTRACT: There are some problems with estrus detection in ewes in large-scale meat sheep farming: mainly, the manual detection method is labor-intensive and the contact sensor detection method causes stress reactions in ewes. To solve the abovementioned problems, we proposed a multi-objective detection layer neural network-based method for ewe estrus crawling behavior recognition. The approach we proposed has four main parts. Firstly, to address the problem of mismatch between our constructed ewe estrus dataset and the YOLO v3 anchor box size, we propose to obtain a new anchor box size by clustering the ewe estrus dataset using the K-means++ algorithm. Secondly, to address the problem of low model recognition precision caused by small imaging of distant ewes in the dataset, we added a 104 × 104 target detection layer, making the total target detection layer reach four layers, strengthening the model’s ability to learn shallow information and improving the model’s ability to detect small targets. Then, we added residual units to the residual structure of the model, so that the deep feature information of the model is not easily lost and further fused with the shallow feature information to speed up the training of the model. Finally, we maintain the aspect ratio of the images in the data-loading module of the model to reduce the distortion of the image information and increase the precision of the model. The experimental results show that our proposed model has 98.56% recognition precision, while recall was 98.04%, F1 value was 98%, mAP was 99.78%, FPS was 41 f/s, and model size was 276 M, which can meet the accurate and real-time recognition of ewe estrus behavior in large-scale meat sheep farming. |
format | Online Article Text |
id | pubmed-9913191 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99131912023-02-11 A Recognition Method of Ewe Estrus Crawling Behavior Based on Multi-Target Detection Layer Neural Network Yu, Longhui Guo, Jianjun Pu, Yuhai Cen, Honglei Li, Jingbin Liu, Shuangyin Nie, Jing Ge, Jianbing Yang, Shuo Zhao, Hangxing Xu, Yalei Wu, Jianglin Wang, Kang Animals (Basel) Article SIMPLE SUMMARY: The timely and accurate detection of ewe estrus behavior in precision animal husbandry is an important research topic. The timely detection of estrus ewes in mutton sheep breeding can not only protect the welfare of ewes themselves, but also improve the interests of breeding enterprises. With the continuous increase in the scale of mutton sheep breeding and the gradual intensification of breeding methods, the traditional manual detection methods require high labor intensity, and the contact sensor detection methods will cause stress reaction problems in ewes. In recent years, the rapid development of deep learning technology has brought on new possibilities. We propose a method for the recognition ewe estrus based on a multi-target detection layer neural network. The results show that the method can meet the requirements of timely and accurate detection of ewe estrus behavior in large-scale mutton sheep breeding. ABSTRACT: There are some problems with estrus detection in ewes in large-scale meat sheep farming: mainly, the manual detection method is labor-intensive and the contact sensor detection method causes stress reactions in ewes. To solve the abovementioned problems, we proposed a multi-objective detection layer neural network-based method for ewe estrus crawling behavior recognition. The approach we proposed has four main parts. Firstly, to address the problem of mismatch between our constructed ewe estrus dataset and the YOLO v3 anchor box size, we propose to obtain a new anchor box size by clustering the ewe estrus dataset using the K-means++ algorithm. Secondly, to address the problem of low model recognition precision caused by small imaging of distant ewes in the dataset, we added a 104 × 104 target detection layer, making the total target detection layer reach four layers, strengthening the model’s ability to learn shallow information and improving the model’s ability to detect small targets. Then, we added residual units to the residual structure of the model, so that the deep feature information of the model is not easily lost and further fused with the shallow feature information to speed up the training of the model. Finally, we maintain the aspect ratio of the images in the data-loading module of the model to reduce the distortion of the image information and increase the precision of the model. The experimental results show that our proposed model has 98.56% recognition precision, while recall was 98.04%, F1 value was 98%, mAP was 99.78%, FPS was 41 f/s, and model size was 276 M, which can meet the accurate and real-time recognition of ewe estrus behavior in large-scale meat sheep farming. MDPI 2023-01-26 /pmc/articles/PMC9913191/ /pubmed/36766301 http://dx.doi.org/10.3390/ani13030413 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yu, Longhui Guo, Jianjun Pu, Yuhai Cen, Honglei Li, Jingbin Liu, Shuangyin Nie, Jing Ge, Jianbing Yang, Shuo Zhao, Hangxing Xu, Yalei Wu, Jianglin Wang, Kang A Recognition Method of Ewe Estrus Crawling Behavior Based on Multi-Target Detection Layer Neural Network |
title | A Recognition Method of Ewe Estrus Crawling Behavior Based on Multi-Target Detection Layer Neural Network |
title_full | A Recognition Method of Ewe Estrus Crawling Behavior Based on Multi-Target Detection Layer Neural Network |
title_fullStr | A Recognition Method of Ewe Estrus Crawling Behavior Based on Multi-Target Detection Layer Neural Network |
title_full_unstemmed | A Recognition Method of Ewe Estrus Crawling Behavior Based on Multi-Target Detection Layer Neural Network |
title_short | A Recognition Method of Ewe Estrus Crawling Behavior Based on Multi-Target Detection Layer Neural Network |
title_sort | recognition method of ewe estrus crawling behavior based on multi-target detection layer neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9913191/ https://www.ncbi.nlm.nih.gov/pubmed/36766301 http://dx.doi.org/10.3390/ani13030413 |
work_keys_str_mv | AT yulonghui arecognitionmethodofeweestruscrawlingbehaviorbasedonmultitargetdetectionlayerneuralnetwork AT guojianjun arecognitionmethodofeweestruscrawlingbehaviorbasedonmultitargetdetectionlayerneuralnetwork AT puyuhai arecognitionmethodofeweestruscrawlingbehaviorbasedonmultitargetdetectionlayerneuralnetwork AT cenhonglei arecognitionmethodofeweestruscrawlingbehaviorbasedonmultitargetdetectionlayerneuralnetwork AT lijingbin arecognitionmethodofeweestruscrawlingbehaviorbasedonmultitargetdetectionlayerneuralnetwork AT liushuangyin arecognitionmethodofeweestruscrawlingbehaviorbasedonmultitargetdetectionlayerneuralnetwork AT niejing arecognitionmethodofeweestruscrawlingbehaviorbasedonmultitargetdetectionlayerneuralnetwork AT gejianbing arecognitionmethodofeweestruscrawlingbehaviorbasedonmultitargetdetectionlayerneuralnetwork AT yangshuo arecognitionmethodofeweestruscrawlingbehaviorbasedonmultitargetdetectionlayerneuralnetwork AT zhaohangxing arecognitionmethodofeweestruscrawlingbehaviorbasedonmultitargetdetectionlayerneuralnetwork AT xuyalei arecognitionmethodofeweestruscrawlingbehaviorbasedonmultitargetdetectionlayerneuralnetwork AT wujianglin arecognitionmethodofeweestruscrawlingbehaviorbasedonmultitargetdetectionlayerneuralnetwork AT wangkang arecognitionmethodofeweestruscrawlingbehaviorbasedonmultitargetdetectionlayerneuralnetwork AT yulonghui recognitionmethodofeweestruscrawlingbehaviorbasedonmultitargetdetectionlayerneuralnetwork AT guojianjun recognitionmethodofeweestruscrawlingbehaviorbasedonmultitargetdetectionlayerneuralnetwork AT puyuhai recognitionmethodofeweestruscrawlingbehaviorbasedonmultitargetdetectionlayerneuralnetwork AT cenhonglei recognitionmethodofeweestruscrawlingbehaviorbasedonmultitargetdetectionlayerneuralnetwork AT lijingbin recognitionmethodofeweestruscrawlingbehaviorbasedonmultitargetdetectionlayerneuralnetwork AT liushuangyin recognitionmethodofeweestruscrawlingbehaviorbasedonmultitargetdetectionlayerneuralnetwork AT niejing recognitionmethodofeweestruscrawlingbehaviorbasedonmultitargetdetectionlayerneuralnetwork AT gejianbing recognitionmethodofeweestruscrawlingbehaviorbasedonmultitargetdetectionlayerneuralnetwork AT yangshuo recognitionmethodofeweestruscrawlingbehaviorbasedonmultitargetdetectionlayerneuralnetwork AT zhaohangxing recognitionmethodofeweestruscrawlingbehaviorbasedonmultitargetdetectionlayerneuralnetwork AT xuyalei recognitionmethodofeweestruscrawlingbehaviorbasedonmultitargetdetectionlayerneuralnetwork AT wujianglin recognitionmethodofeweestruscrawlingbehaviorbasedonmultitargetdetectionlayerneuralnetwork AT wangkang recognitionmethodofeweestruscrawlingbehaviorbasedonmultitargetdetectionlayerneuralnetwork |