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

Adam Hruška and Pavel Hamouz
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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

Plant Protection Science, 2023, vol. 59, issue 3, 292-297

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.

Keywords: computer vision; NIR images; machine learning; visual analysis; neural networks (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:caa:jnlpps:v:59:y:2023:i:3:id:131-2022-pps

DOI: 10.17221/131/2022-PPS

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