Research Progress of Deep Learning in Typical Behavior Recognition of Livestock and Poultry
The typical behaviors,such as feeding,drinking,standing and fighting,are closely related to the production capacity,health status as well as welfare of livestock and poultry,which affecting the production and economic benefits of livestock and poultry in farms.In fact,the traditional manual observation of livestock and poultry is not only time-consuming and laborious,but also highly subjective.So currently,the trend of large-scale livestock and poultry farming is accelerating.With the rapid development of machine learning as well as continuous optimization of neural networks,algorithms and computility,technologies such as computer vision,speech recognition,biometric recognition and natural language processing can accurately and efficiently monitor the information of livestock and poultry as well as analyze the physiological and health status of livestock and poultry,showing broad application prospects in the field of livestock and poultry.This article introduced the development history of deep learning technology,and then expounded the research progress of deep learning technology in behavior recognition of common livestock and poultry species such as cattle,pig,sheep and chicken,providing technical reference for future researches and practical applications.Meanwhile,this article summarized the problems and improvement strategies of deep learning technology in behavior recognition of common livestock and poultry from aspects of model versatility,data set diversity as well as the comprehensiveness of digital behavior results,aiming to provide theoretical reference for technicians to promote the further development of deep learning in the application of typical behavior of livestock and poultry.
livestock and poultrydeep learningbehavior recognition
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