Computer vision (CV), a non-intrusive and cost-effective technology, has furthered the development of precision livestock farming by enabling optimized decision-making through timely and individualized animal care. The availability of affordable two- and three-dimensional camera sensors, combined with various machine learning and deep learning algorithms, has provided a valuable opportunity to improve livestock production systems. However, despite the availability of various CV tools in the public domain, applying these tools to animal data can be challenging, often requiring users to have programming and data analysis skills, as well as access to computing resources. Moreover, the rapid expansion of precision livestock farming is creating a growing need to educate and train animal science students in CV. This presents educators with the challenge of efficiently demonstrating the complex algorithms involved in CV. Thus, the objective of this study was to develop ShinyAnimalCV, an open-source cloud-based web application designed to facilitate CV teaching in animal science. This application provides a user-friendly interface for performing CV tasks, including object segmentation, detection, three-dimensional surface visualization, and extraction of two- and three-dimensional morphological features. Nine pre-trained CV models using top-view animal data are included in the application. ShinyAnimalCV has been deployed online using cloud computing platforms. The source code of ShinyAnimalCV is available on GitHub, along with detailed documentation on training CV models using custom data and deploying ShinyAnimalCV locally to allow users to fully leverage the capabilities of the application. ShinyAnimalCV can help to support the teaching of CV, thereby laying the groundwork to promote the adoption of CV in the animal science community.
Keywords: computer vision; morphological features; object detection; object segmentation; shiny application; three-dimensional visualization.
The integration of cameras and data science has great potential to revolutionize livestock production systems, making them more efficient and sustainable by replacing human-based management with real-time individualized animal care. However, applying these digital tools to animal data presents challenges that require computer programming and data analysis skills, as well as access to computing resources. Additionally, there is a growing need to train animal science students to analyze image or video data using data science algorithms. However, teaching computer programming to all types of students from the ground up can prove complicated and challenging. Therefore, the objective of this study was to develop ShinyAnimalCV, a user-friendly online web application that supports users to learn the application of data science to analyze animal digital video data, without the need for complex coding. The application includes nine pre-trained models for detecting and segmenting animals in image data and can be easily accessed through a web browser. We have also made the source code and detailed documentation available online for advanced users who wish to use the application locally. This software tool facilitates the teaching of digital animal data analysis in the animal science community, with potential benefits to livestock production systems.
© The Author(s) 2023. Published by Oxford University Press on behalf of the American Society of Animal Science.