摘要
在种鸡养殖和管理过程中,借助非接触式、连续的声音检测手段和智能化设备,饲养员可以全面了解蛋鸡的健康状况以及个体需求,为提高生产效率并同时改善种鸡福利化养殖,提出了一种基于轻量级卷积神经网络的种鸡发声分类识别方法,以海兰褐种鸡为研究对象,收集种鸡舍内常见的5类声音,再将其声音一维信号转换为二维图像信号,利用卷积神经网络建立轻量级的深度学习模型,80%数据进行训练,20%数据进行测试,该模型实现了动物声音信号从输入端到识别结果输出端的高效检测。对比已有研究,本文方法对种鸡舍内常见的5类声音识别整体准确率提高3.7个百分点。试验结果表明,该方法平均准确率为95.7%,模型对饮水声、风机噪声、产蛋叫声识别召回率均达到100%,其中风机噪声和产蛋叫声精确率和F1值也均达到100%,而应激叫声召回率最低,为88.3%。本研究可为规模化无人值守鸡舍的智能装备研发提供一定理论参考。
In the process and management of breeding birds breeding,with the help of non-contact and continuous sound detection as well as some intelligent equipment,the breeder can fully understand the health status and individual needs of breeding birds,which can improve production efficiency as well as animal welfare.A kind of lightweight convolution neural networks for breeding birds voice recognition was proposed.The sound of the Hy-line brown breeding birds was taken as the research object,and five kinds of common sounds in the breeding bird house were collected,then the one-dimensional signal of sound was converted into two-dimensional image signal.Based on the great advantages of convolutional neural network in image recognition,a lightweight deep learning model was established,with 80%data as training and 20%data as testing.This model realized the efficient detection process of animal sound signal from input to output of recognition results.By comparing and analyzing the recognition methods of previous studies,the proposed method greatly improved the overall accuracy rate of recognition of five kinds of common sounds in breeding birds’house by 3.7 percentage points.The experimental results showed that the average accuracy rate of this method was as high as 95.7%.The recall rate of the model for drinking water,fan noise and laying call were all up to 100%,and the precision rate and F1 value of fan noise and laying call were also up to 100%.While,the recall rate of stress call was the lowest value of 88.3%.The research result provided some theoretical reference for the research and development of unmanned intelligent equipment in the future large-scale chicken house.
作者
杜晓冬
滕光辉
刘慕霖
赵雨晓
周振宇
祝鹏飞
DU Xiaodong;TENG Guanghui;LIU Mulin;ZHAO Yuxiao;ZHOU Zhenyu;ZHU Pengfei(New Hope Liuhe Co.,Ltd.,Beijing 100102,China;College of Water Resources and Civil Engineering,China Agricultural University,Beijing 100083,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2022年第10期271-276,共6页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家重点研发计划项目(2016YFD0700204)
关键词
种鸡
动物声音识别
声谱图
轻量级卷积神经网络
深度学习
breeding birds
animal sound recognition
spectrogram
lightweight convolutional neural networks
deep learning
