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An Introduction to Automated Visual Sensemaking for Animal Production Systems  Public Access

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  pp. 1-7
Authors:   Divya Handa, Armin Moghadam, Abdullah Sourav, Ryan Jeon, Julia Chen, Joshua Peschel
Keywords:   Computer vision, Machine learning, Livestock, Video, R-CNN, Education.

Students learn basic concepts for a machine learning algorithm used in the context of computer vision

Opportunities for student reflection and improvement of a data pipeline and workflow are presented

This learning exercise utilizes a primary video data source that could also be collected by students

Abstract. This paper presents a learning exercise for automatically identifying and tracking individual livestock animals within a video data set. Consistent, reliable, and accurate tracking and monitoring of individual animals is one of the biggest challenges faced by the livestock industry today; furthermore, access to these data for analysis and modeling purposes may be limited by security protocols and/or the format in which the data might be available. The approach of this work uses a computer vision approach with a machine learning algorithm (Mask R-CNN) to automatically identify and visually track individual animals in a pen. Results are converted into a tabular format that can be translated into geometric input files for modeling purposes. This fast, approximation approach to assembling individual animal data could be of interest to animal science-related researchers and practitioners, to improve understanding and operation in animal production systems. The exercise is part of a special topics graduate class taught to students in science and engineering, but who have a limited background in programming; the exercise occurs approximately half-way into the semester and basic familiarity with image and video data, along with some classical computer vision knowledge, is required.

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