Sensor system development for low-damage sugar beet harvesting

Sensor system development for low-damage sugar beet harvesting – state and perspectives

Autor/in: Wilczek, U., B. Kulig, H.-J. Koch, R. Kälberloh, O. Hensel
Jahr: 2020
Zeitschrift: Sugar Industry (2020) 145
Seite/n: 299-306
ISBN: doi: 10.36961/si24377
Stichworte: Zuckerrübenroder, Spitzenbruch, Oberflächenbeschädigung, Langzeitlagerung, Zuckerverlust, Sieb-stern, Messrübe, Körperschall, Hochgeschwindigkeitskamera, Machine-Learning

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Abstract

The SmartBeet project aimed to develop a sensor system feasible to detect beet damages occurring in the harvester cleaning system. Sensor information should allow to design driver assistance systems safeguarding low-damage beets most suitable for long-term storage. Long-term storage trials in climate containers revealed that root tip breakage caused by turbine cleaning correlated sufficiently close with sugar losses, and thus can serve as an overall damage indicator. In a systematic drop test, heavier beets (>700g), beets impacting the ground with the root tip ahead and dropping from 2.5m caused largest tip breakage. Field experiments were conducted with measuring bobs which were shaped like beets and equipped with accelerometers and surface pressure sensors. They showed that type and form of impacts affect damage severity in addition to impact intensity. Moreover, the turbines exerted less impact compared to the lifter, sieve conveyor and auger conveyor. Results imply that the beet throughput level through the cleaning section significantly affects the occurrence of damages. In addition, the structure-borne sound of the beet guiding grates of the turbines was recorded. Single beet damage events were identified from videos taken by high speed cameras and synchronized with the associated sound frequency spectra. In future, time segments and synchronized Fast-Fourier-transformed frequency spectra will be used to derive specific trait variables in order to develop a Machine-Learning-Model.

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