Artificial Intelligence for Livestock: A Review of Computer Vision and Language Models in Animal Farming
Author Information
Author(s): Guilherme L. Menezes, Gustavo Mazon, Rafael E.P. Ferreira, Victor E. Cabrera, Joao R.R. Dorea
Primary Institution: University of Wisconsin-Madison
Hypothesis
How can computer vision systems and large language models improve decision-making in animal farming?
Conclusion
The review highlights the potential of AI technologies, particularly computer vision systems and large language models, to enhance decision-making and efficiency in dairy farming.
Supporting Evidence
- AI is already integrated into many daily tasks, including facial recognition and airport security, and its use is expanding into agriculture.
- Computer vision systems are applied in dairy farming for animal identification, behavior monitoring, feeding intake, body weight estimation, and disease detection.
- There is a significant knowledge gap in the use of large language models in animal farming, presenting opportunities for new research and technological advancements.
Takeaway
This study shows that computers can help farmers take better care of their animals by using cameras and smart programs to watch over them and understand their needs.
Methodology
The review discusses various applications of computer vision systems and large language models in dairy farming, focusing on their capabilities in monitoring animal health, behavior, and productivity.
Limitations
The review identifies a significant knowledge gap in the use of large language models in animal farming and highlights challenges in implementing computer vision systems in commercial settings.
Digital Object Identifier (DOI)
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