Vision based detection of volunteer potatoes as weeds in sugar beet and cereal fields

In: Precision Agriculture ‘05
Authors:
A.T. Nieuwenhuizen Farm Technology Group, Wageningen University, P.O. box 17, 6700 AA Wageningen, The Netherlands Ard.Nieuwenhuizen@wur.nl

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J.H.W. van den Oever Farm Technology Group, Wageningen University, P.O. box 17, 6700 AA Wageningen, The Netherlands Ard.Nieuwenhuizen@wur.nl

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L. Tang Farm Technology Group, Wageningen University, P.O. box 17, 6700 AA Wageningen, The Netherlands Ard.Nieuwenhuizen@wur.nl

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J.W. Hofstee Farm Technology Group, Wageningen University, P.O. box 17, 6700 AA Wageningen, The Netherlands Ard.Nieuwenhuizen@wur.nl

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J. Müller Farm Technology Group, Wageningen University, P.O. box 17, 6700 AA Wageningen, The Netherlands Ard.Nieuwenhuizen@wur.nl

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The possible spread of late blight from volunteer potato plants requires that these plants be removed from arable fields. Because of high labour, energy and chemical demands, automatic detection and removal is needed. Two methods for colour based machine vision detection of volunteer potato plants in sugar beet and cereal fields were compared. An Adaptive Neural Network and a combination of K-means clustering/Bayes classifier gave almost the same results. The best result was obtained in detecting volunteer potato plants in sugar beet fields when the classification results were judged at plant level instead of pixel level. Actually these results at plant level are the required input for a plant specific removal system.

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