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Abstract
Purpose
Artificial intelligence (AI) refers to the ability of a digital computer or computer-controlled robot to perform reasoning tasks commonly associated with intelligent beings. Powered by novel and affordable software and hardware capabilities, AI is finding its way into dairy farms. The purpose of this review is to give examples of research on AI for dairy farms and highlight some emerging opportunities. Many AI applications are based on machine learning, including the technique of deep learning.
Sources
We used literature sources and our own experiences with AI applications in dairy farms.
Synthesis
We found that machine-learning methods enable applications such as real-time analysis of video images to identify cattle, measure body condition and temperature, and detect changes in feed topography to measure feed availability and intake. Changes in behavior can be detected as early warning alerts for disease such as lameness or as an indication of estrus. Such AI applications can mimic human reasoning and enhance human tasks. Machine-learning methods may also be able to use heterogeneous data sets to predict future performance such as fertility. For example, predictions of conception rates may be improved by combining health events, changes in body energy reserves, genetic data, behavioral data, milk analyses, and environmental information. Perhaps AI methods could be used to monitor compliance of execution of protocols in dairy farms and inform training.
Conclusions and Applications
Artificial intelligence, made possible with advances in hardware and software, will make intelligent use of new big data a reality and will change the dairy sector by enabling improved work environments and removing or minimizing the need for manual human processing of repetitive tasks. A hurdle for development and application of some AI is the problem that various dairy data often exist in silos that are not connected.
Key words
References
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Footnotes
☆The authors have not declared any conflicts of interest.
☆☆*This invited article resulted from the presentation given at the ARPAS Symposium, “Artificial Intelligence and Machine Learning in Dairy Production Systems,” Kansas City, Missouri, June 2022.
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