深度学习在畜禽典型行为识别中的研究进展

Research Progress of Deep Learning in Typical Behavior Recognition of Livestock and Poultry

采食、饮水、站立和打斗等典型行为与畜禽生产能力、健康状况和福利密切相关,影响畜禽产量与经济效益。当前,畜禽养殖规模化趋势加速,传统人工观察畜禽行为不仅费时费力,而且主观性较强。随着机器学习的快速发展,神经网络、算法和算力不断优化,计算机视觉、语音识别、生物识别、自然语言处理等技术能准确高效地监测畜禽信息,分析畜禽生理和健康状况,在畜禽领域展现出广阔的应用前景。介绍了深度学习技术的发展历程,阐述了深度学习技术在常见畜禽种类(牛、猪、羊、鸡)行为识别方面的研究进展,为未来研究和实际应用提供了技术参考;总结了深度学习技术在畜禽行为识别中关于模型通用性、数据集多样性和数字化行为结果全面性等方面存在的问题并提出改进策略,旨在推动深度学习在畜禽典型行为中的进一步应用。

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 timeconsuming 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.