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Verification of a machine learning model for weed detection in maize (Zea mays) using infrared imaging

Author

Listed:
  • Adam Hruška

    (Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, Czech Republic)

  • Pavel Hamouz

    (Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, Czech Republic)

Abstract

The potential of the framework of precision agriculture points towards the emergence of site-specific weed control. In light of the phenomena, the search for a cost-effective approach can help the discipline to accelerate the practical implementation. The paper presents a near-infrared data-driven machine learning model for real-time weed detection in wide-row cultivated maize (Zea mays) fields. The basis of the model is a dataset of 5 120 objects including 18 species of weeds significant in the context of wide-row crop production in the Czech Republic. The custom model was subsequently compared with a state-of-the-art machine learning tool You only look once (version 3). The custom model achieved 94.5 % identification accuracy while highlighting the practical limitations of the dataset.

Suggested Citation

  • Adam Hruška & Pavel Hamouz, 2023. "Verification of a machine learning model for weed detection in maize (Zea mays) using infrared imaging," Plant Protection Science, Czech Academy of Agricultural Sciences, vol. 59(3), pages 292-297.
    • Handle: RePEc:caa:jnlpps:v:59:y:2023:i:3:id:131-2022-pps
    DOI: 10.17221/131/2022-PPS
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