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Broad leaf weed detection in pasture

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Academic year: 2020

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Figure

Figure 2.  An example of a labelled weed image where the red region is the mask for the weeds
Figure 3.  Architecture of our CNN consisting of six convolutional layers with alternating dropout and max-pooling layers
Figure 5.  The normalised confustion matrix of the quadratic SVM. This method correctly identifies weeds with 80% accuracy
Figure 7.  Visual examples of classification results. (a) successful identification of weed, (b) successful identification of grass, (c) weed identified as grass, and (d) grass identified as weed

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