Research And Application Of Animal Behavior Recognition Based On Deep Learning

In today’s era,artificial intelligence technology is rapidly developing,and deep learning and convolutional neural network technology are constantly penetrating various fields of agriculture,accelerating the transformation of traditional aquaculture,especially in the field of animal husbandry.The animal husbandry and breeding industry in China is developing vigorously towards the goal of intensification,intelligence,and precision.The behavior recognition of individual livestock is the core content of precision animal husbandry,and intelligent detection and recognition of livestock behavior can provide important reference for disease prevention and efficient breeding of livestock.Computer vision technology,as an effective image processing technology,can provide a contactless,automated,and efficient way to monitor livestock behavior.In this regard,this article adopts a non-contact behavior recognition method and proposes an individual behavior recognition scheme based on deep learning.This article first introduces the research status of the development of animal husbandry in China,analyzes the shortcomings and shortcomings of the current animal husbandry industry in China from the domestic and international research status,and points out the important significance of computer vision for modern animal husbandry.And design a deep learning cattle daily behavior recognition scheme,including feasibility analysis of deep learning models,introduction of convolutional neural networks,and construction of deep learning models.Then,images and videos taken at the Qinhuangdao aquaculture farm are used as the experimental dataset sources to filter the dataset.2229 images including cows standing,lying down,drinking water,and feeding are selected as the dataset for this experiment.Finally,certain processing is performed on the dataset.This has laid a good data foundation for the research in this article.Secondly,machine learning technology is used to train the dataset.This study mainly uses two detection models,YOLOv3 and YOLOv5 s,to detect and recognize the daily behavior of cattle.After comparing and analyzing the detection speed and accuracy of the two algorithm models,it is found that the YOLOv5 s model has better performance,and it is also found that the YOLOv5 s network model have lower dependence on accurate feature extraction.To further validate the superiority of the YOLOv5 s model,this article conducted training experiments on the YOLOv5 m model on the dataset.After comparative analysis,it is found that the YOLOv5 s model have the best detection speed and accuracy.Therefore,the YOLOv5 s algorithm is the most suitable for meeting the practical needs of modern aquaculture farms.Finally,the design of the daily behavior time statistical algorithm of cattle is carried out,and the error between the algorithm and the manual statistical time is analyzed.The statistics of cattle daily behavior time and the detection and identification of cattle daily behavior can reflect the health and living conditions of cattle in the farm to a certain extent,which provides a certain data reference for the realization of intelligent management of the farm,and injects impetus into the realization of intelligent breeding in China’s modern breeding industry.