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Proceedings of the 18th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 35

Recognition of Weeds in Cornfields

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DOI: http://dx.doi.org/10.15439/2023F2610

Citation: Proceedings of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 35, pages 995–999 ()

Full text

Abstract. In terms of weed control, existing precision spraying solutions seek to reduce the unwanted impact of spraying by separate field scanning from mostly birds' eye view. In our study, we propose a hybrid approach in which the mechanical hoeing and the spraying is done simultaneously accomplished by weed recognition from a lower position where the plants' leaves do not cover weeds. We demonstrate the line and the weed recognition methods on a dataset collected from corn fields and compare different convolutional neural networks. We also investigate the feasibility on two widely known embedded platforms.

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