Early Prediction of Heat Stress in Dairy Cattle Using Artificial Intelligence for Sustainable Livestock

Project Overview

GNC22-354
Project Type: Graduate Student
Funds awarded in 2022: $14,993.00
Projected End Date: 05/05/2023
Grant Recipient: University of Wisconsin-Madison
Region: North Central
State: Wisconsin
Faculty Advisor:
Dr. Younghyun Kim
University of Wisconsin-Madison

Commodities

No commodities identified

Practices

No practices identified

Proposal abstract:

Dairy cows are particularly sensitive to heat stress, which can affect profitability due to reduced milk production and reproductive performance, and significantly deteriorate animal welfare. Climate change is likely to increase average temperatures and the frequency of severe heat waves across the US, and recent research suggests that the North Central Region is not an exception to this near-future crisis. The increasing pressure of heat stress demands more active methods of cooling, such as strong ventilation fans and spray water, which use large amounts of electricity and water. The rising cost of the electricity and change in rainfall patterns make it more challenging to maintain profitability and sustainability of the dairy industry under severe heat stress.

For the optimal mitigation of heat stress, it is crucial to detect the signs of heat stress early before it makes long-term impacts. Traditionally, farmers have used dairy cattle’s individual behavior–how they move, how much they eat and drink, where they spend time, etc.–and herd behavior–how they interact with each other–to indirectly gauge the level of heat stress and take necessary mitigation measures. However, it is becoming less viable as more farms are consolidated and the number of cows managed per farmer rapidly grows.

This research proposes to use computer vision and artificial intelligence (AI) for early detection of heat stress in dairy cattle for more sustainable and profitable dairy farming in the North Central Region. We will use state-of-the-art computer vision and AI technologies to monitor and analyze dairy cattle’s individual and herd behavior to help farmers and ranchers to make optimal decisions to mitigate heat stress in a timely manner. More specifically, we propose to i) Identify and locate individual cattle in real-time video stream ii) Recognize activities and postures of individual cattle and iii) Predict heat stress in cattle using behavior analysis and notify farmers/ranchers using AI. The proposed methods will be tested and evaluated at our partner farm in Watertown, WI.

The outcomes of the proposed research will be disseminated to local farmers through our outreach and extension network. For more wide-spread adoption, we will publicly open the hardware and software outcomes of the research, including collected and labeled datasets, trained computer vision models, source code and documentation for training, testing, and deploying the models on edge devices such as NVIDIA’s Jetson family of devices.

Project objectives from proposal:

Learning outcomes: 

Farmers/ranchers will learn how AI can be leveraged for early prediction of heat stress in dairy cattle. They will learn about the changes in behavior in cattle which can be early indicators of heat stress and how automation in the task of behavior analysis using AI can be used in heat stress prediction. It will make them knowledgeable in performing data-driven decision-making to maximize profitability with minimal resources. The project will spread awareness among farmers and ranchers about reducing energy and water consumption through precision livestock and efficient cooling mechanisms. They will learn how they can play a central role in reducing energy and water footprint of a barn leading to sustainable livestock practices. 

Researchers can use the dataset collected and labeled as a part of this project to advance their research in the domains of heat stress mitigation, animal welfare, and sustainable agriculture using AI. 

Action outcomes:  

Farmers/ranchers will use the outputs of the project to mitigate financial losses incurred due to heat stress in cattle using efficient cooling techniques, for continuous monitoring of activities and location of the cattle, and to improve animal welfare. Apart from the direct utility for farmers/ranchers, the output of the project can also be used by engineers and researchers to design energy-efficient, optimal, and cost-effective cooling mechanisms to mitigate heat stress in dairy cattle. Eventually, the outputs of this project will also encourage farmers to move toward more energy-efficient and precise heat stress abatement techniques, contributing to sustainable livestock practices.

Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture or SARE.