• Forschung - einfache Suche
  • Projektsuche
  • Publikationssuche

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


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.
FaLang translation system by Faboba
IfZ - Institut für Zuckerrübenforschung · Holtenser Landstr. 77 · 37079 Göttingen · mail@ifz-goettingen.de · Impressum · Datenschutz previous_page

Wir nutzen Cookies auf unserer Website. Einige von ihnen sind essenziell für den Betrieb der Seite, während andere uns helfen, diese Website und die Nutzererfahrung zu verbessern (Tracking Cookies). Sie können selbst entscheiden, ob Sie die Cookies zulassen möchten. Bitte beachten Sie, dass bei einer Ablehnung womöglich nicht mehr alle Funktionalitäten der Seite zur Verfügung stehen.