Aarhus University Seal / Aarhus Universitets segl

Evaluation of grain quality-based simulated selective harvest performed by an autonomous agricultural robot

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

DOI

Grain price differences due to protein content can have economic effects on the farm as well as environmental effects when alternative protein sources are imported. Grain protein variability can vary from year to year due to environmental factors and can be addressed by site-specific management practices. Alternatively, it can be addressed at harvest time by selective harvest. Agricultural autonomous robots can accurately follow alternative harvesting routes that are subject to grain quality maps, making them suitable choices for selective harvest. This study addresses therefore the potential revenue of selective harvest performed by the route planner of an autonomous field robot. The harvest capacity and potential economic revenues of selective harvest in a Danish context were studied for a set of 20 winter wheat fields with four hypothetical scenarios. The results showed significant differences in harvest capacity between conventional and selective harvest. Even though in some scenarios selective harvest did not require notable additional harvest times, the cost–benefit analysis showed small economic returns of up to 46 DKK ha−1 for the best scenarios, and for most cases losses up to 464 DKK ha−1 . Additionally, the location of the high protein content areas has great influence on the profitability of selective harvest.

Original languageEnglish
Article number1728
JournalAgronomy
Volume11
Issue9
ISSN2073-4395
DOIs
Publication statusPublished - Sep 2021

Bibliographical note

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

    Research areas

  • Agricultural field robots, Autonomous agricultural robot, Grain quality orientated harvest, Harvest automation, Internet of Things, Optimised route planning, Selective harvest, Smart farming

See relations at Aarhus University Citationformats

ID: 223148777