<i>Development of a Machine Learning-based Assistance System for Computer-Aided Process Optimization within a Self-Propelled Sugar Beet Harvester</i>

Steffen Schwich; Jan Schattenberg; Dr.-Ing.; Ludger Frerichs; Prof. Dr.

doi:10.13031/aim.202000952

Click on “Download PDF” for the PDF version or on the title for the HTML version.

If you are not an ASABE member or if your employer has not arranged for access to the full-text, Click here for options.

Development of a Machine Learning-based Assistance System for Computer-Aided Process Optimization within a Self-Propelled Sugar Beet Harvester

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

Citation:  2020 ASABE Annual International Virtual Meeting 2000952.(doi:10.13031/aim.202000952)
Authors:   Steffen Schwich, Jan Schattenberg, Dr.-Ing., Ludger Frerichs, Prof. Dr.
Keywords:   Assistance System, Machine Learning, Computer Vision, Computer Aided Process Automation, Deep Learning, Harvesting Quality Estimation

Abstract. Self-Propelled Sugar Beet harvesters are complex machines. To improve system performance and support operators the development of an assistance system for process optimization was investigated. This article summarizes the results of the application of modern machine learning tools and states out a development approach which integrates them into the development process. Starting from a harvesting process description, a quality sensor gets integrated into the machine, the algorithm concept for the sensor evaluation is shown, as well as the machine learning-based process optimization and first qualitative evaluation of the overall system are presented.

(Download PDF) (Export to EndNotes)

Authors, please use the Guide for Authors when creating your articles.

Public Access Information
= Public Access (PA)
Public Access Limited Time = PA Limited Time
= Open Access
= Contact Us For Purchase