Detecting Dairy Cow Behavior Using Vision Technology

John Mcdonagh,Kimberley R Slinger,Georgios Tzimiropoulos,Zoë J Huggett,Matt J Bell,Peter M Down

doi:10.3390/agriculture11070675

John Mcdonagh, Kimberley R Slinger + Show 4 more

Open Access

https://doi.org/10.3390/agriculture11070675

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Abstract

The aim of this study was to investigate using existing image recognition techniques to predict the behavior of dairy cows. A total of 46 individual dairy cows were monitored continuously under 24 h video surveillance prior to calving. The video was annotated for the behaviors of standing, lying, walking, shuffling, eating, drinking and contractions for each cow from 10 h prior to calving. A total of 19,191 behavior records were obtained and a non-local neural network was trained and validated on video clips of each behavior. This study showed that the non-local network used correctly classified the seven behaviors 80% or more of the time in the validated dataset. In particular, the detection of birth contractions was correctly predicted 83% of the time, which in itself can be an early warning calving alert, as all cows start contractions several hours prior to giving birth. This approach to behavior recognition using video cameras can assist livestock management.

Highlights

At a time when the general public has concerns about how livestock are managed and their welfare, tools that can improve animal welfare standards and increase the public acceptance of farming are required
The use of cameras to monitor animals and their behaviors manually has been available for decades, with animal behavior and welfare concerns commonly directed at housed livestock production, such as dairy cows [1,2]
Adaptations of neural networks for analyzing video can be used for a number of tasks such as recognition of specific animal behaviors [6]

Summary

Introduction

At a time when the general public has concerns about how livestock are managed and their welfare, tools that can improve animal welfare standards and increase the public acceptance of farming are required. The expectation has been for each stockperson to look after more animals, as input costs (including labor) have increased and finding skilled farm workers has become more challenging, and with the increased size of the average dairy herd. With these challenges have come high-quality digital camera systems that provide 24 h video surveillance capabilities, and the opportunity for farmers to monitor their livestock remotely and whilst carrying out other farm tasks. Automated image analysis techniques have developed that allow continuous monitoring during the day and night, and require no prior training by the user other than interpreting the output. Vision technology that can continuously monitor individual animals can potentially provide an objective assessment of an abnormal behavioral state to allow early intervention and improved awareness by a stockperson

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