Video Processing for Agricultural Applications
Abstract
Cameras are widely used as sensors for a variety of engineering applications. In a typical video-based application, spatial segmentation is a fundamental step which provides the spatial positions of different targets for further analysis. In this thesis, we focus on videos analytics applied to the agricultural industry and describe several video segmentation methods in the context of two practical projects: autonomous farming vehicles and analysis of dairy cow health. In the autonomous farming vehicle project, we propose three spatial segmentation methods based on traditional video features to isolate the regions of the video frame where critical information appears. Two applications that apply the segmentation method are presented: farming activity classification and header-height control for a combine harvester. In the project on cow health, we propose a cow structural model based on the keypoints of joints from a side-view cow video. A detection system is developed using deep learning techniques to automatically extract the structural model from the videos. Based on this model, we also present a preliminary application which estimates the cow’s weight based on video information.
Degree
Ph.D.
Advisors
Reibman, Purdue University.
Subject Area
Agriculture|Agronomy|Transportation
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