Accurate Classification of Associated Weeds in Farmland under Background of UAV Deep Imaging
There are various background interferences in farmland images,and the appearance of weeds varies greatly under different growth cycles and environmental conditions,which makes it difficult to accurately identify weeds during the classification process and reduces their classification accuracy.Therefore,a precise classification al-gorithm for associated weeds in farmland under unmanned aerial vehicle deep imaging is proposed.This method is based on unmanned aerial vehicles to obtain depth images of associated weeds in farmland,and then to denoised and enhanced the clarity of the images.Based on the processing results,Hue,Saturation,Value(HSV)decomposition is performed on the depth image of farmland.Moreover,an improved two-dimensional local entropy algorithm is com-bined with human vision to calculate the density of image contrast,saturation,and contour information,thus construc-ting an image saliency matrix.Furthermore,the region of interest in the image is extracted,and then the background is segmented.Based on the scale invariant features,the associated farmland features can be extracted.Finally,the image classifier is used to classify the extracted features,thereby achieving the precise classification of associated weeds in farmland.The experimental results show that this method has good denoising effect and complete contour extraction.The classification accuracy is more than 99％ .
UAV depth imagingImage of associated weeds in farmlandImage denoisingClassification methodFeature extraction
NETL
NSTL
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