\u3ci\u3eCharacterizing Feedlot Feed using Depth Cameras and Imaging Technology\u3c/i\u3e

Imaging technology is a growing field that provides solutions in many areas from manufacturing to agriculture. Through previous research, imaging technologies have been studied in livestock farming to monitor the animal’s health and welfare in the production process. However, the feedlot industry is still behind in validating the feasibility to use some of these technologies nor to adopt such technologies to address challenges the industry is facing, such as lack of skilled labor.This work proposes using novel imaging methods to identify feed types and estimate the amount of feed remaining in a typical Midwestern feedlot feed bunk. These methods have promising potential to provide alternative tools to feedlot operations to alleviate labor requirements for tasks like bunk calling, feed sourcing, and feed mixing. This approach, if successful, provides an alternative option that allows existing systems to incorporate these methods into their framework to accurately perform daily tasks.The main contribution of this work is to leverage imaging technologies, specifically, depth imaging and machine learning techniques to build and validate models that can be used in the feedlot production systems in the Midwestern U.S. To date, several studies have explored the use of imaging technologies and machine learning to monitor individual cow intake in dairy production, but there is limited research body comprehensively conducted to explore these technologies for feedlot applications.The proposed methods were used to collect imagery data for eleven common feedlot ingredients and seven diets. Collected images were processed (a) to estimate the weights of residual feed in the bunk, (b) to evaluate the accuracy of depth cameras in estimating residual feed, (c) to characterize the different feed textures, and (d) to classify feed textures using machine learning techniques. Regression models using pixel transformation were developed to correlate image-model-estimated and the scale-measured feed weights, whereas texture analysis techniques and residual neural network model in 10 classes were used to identify the individual ingredients. Methodologies and results are presented in this thesis as a paper format. The major findings indicate that using low-cost depth cameras and machine learning techniques is promising in the development of alternative tools to estimate the amount of residual feed in concrete bunks and identify individual feed ingredients commonly used in commercial feedlots in the U.S. Advisor: Yijie Xion