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Multi-Temporal Site-Specific Weed Control of Cirsium arvense (L.) Scop. and Rumex crispus L. in Maize and Sugar Beet Using Unmanned Aerial Vehicle Based Mapping

Author

Listed:
  • Robin Mink

    (Department of Weed Science, Institute of Phytomedicine, University of Hohenheim, 70599 Stuttgart, Germany)

  • Avishek Dutta

    (Faculty Geomatics, Computer Science and Mathematics, University of Applied Sciences Stuttgart, 70174 Stuttgart, Germany)

  • Gerassimos G. Peteinatos

    (Department of Weed Science, Institute of Phytomedicine, University of Hohenheim, 70599 Stuttgart, Germany)

  • Markus Sökefeld

    (Department of Weed Science, Institute of Phytomedicine, University of Hohenheim, 70599 Stuttgart, Germany)

  • Johannes Joachim Engels

    (Faculty Geomatics, Computer Science and Mathematics, University of Applied Sciences Stuttgart, 70174 Stuttgart, Germany)

  • Michael Hahn

    (Faculty Geomatics, Computer Science and Mathematics, University of Applied Sciences Stuttgart, 70174 Stuttgart, Germany)

  • Roland Gerhards

    (Department of Weed Science, Institute of Phytomedicine, University of Hohenheim, 70599 Stuttgart, Germany)

Abstract

Sensor-based weed mapping in arable fields is a key element for site-specific herbicide management strategies. In this study, we investigated the generation of application maps based on Unmanned Aerial Vehicle imagery and present a site-specific herbicide application using those maps. Field trials for site-specific herbicide applications and multi-temporal image flights were carried out in maize ( Zea mays L.) and sugar beet ( Beta vulgaris L.) in southern Germany. Real-time kinematic Global Positioning System precision planting information provided the input for determining plant rows in the geocoded aerial images. Vegetation indices combined with generated plant height data were used to detect the patches containing creeping thistle ( Cirsium arvense (L.) Scop.) and curled dock ( Rumex crispus L.). The computed weed maps showed the presence or absence of the aforementioned weeds on the fields, clustered to 9 m × 9 m grid cells. The precision of the correct classification varied from 96% in maize to 80% in the last sugar beet treatment. The computational underestimation of manual mapped C. arvense and R. cripus patches varied from 1% to 10% respectively. Overall, the developed algorithm performed well, identifying tall perennial weeds for the computation of large-scale herbicide application maps.

Suggested Citation

  • Robin Mink & Avishek Dutta & Gerassimos G. Peteinatos & Markus Sökefeld & Johannes Joachim Engels & Michael Hahn & Roland Gerhards, 2018. "Multi-Temporal Site-Specific Weed Control of Cirsium arvense (L.) Scop. and Rumex crispus L. in Maize and Sugar Beet Using Unmanned Aerial Vehicle Based Mapping," Agriculture, MDPI, vol. 8(5), pages 1-14, April.
    • Handle: RePEc:gam:jagris:v:8:y:2018:i:5:p:65-:d:143816
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    Cited by:

    1. Bhoomin Tanut & Rattapoom Waranusast & Panomkhawn Riyamongkol, 2021. "High Accuracy Pre-Harvest Sugarcane Yield Forecasting Model Utilizing Drone Image Analysis, Data Mining, and Reverse Design Method," Agriculture, MDPI, vol. 11(7), pages 1-21, July.

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