Abstract
Weed recognition in field is a challenging and hard research field, due to the diversity and changeability of the weed in field. A weed recognition approach is proposed by sparse representation classification (SRC). The method is different from the existing weed recognition methods, instead of extracting a lot of features from each weed image, weed is recognized by SRC directly through the weed image captured in the field, which can reduce the computing cost and recognition time, and improve the recognition performance. The proposed approach is tested on the weed image dataset and is compared with four feature extraction based weed recognition methods. The recognition rate of the proposed algorithm is 94.52%. The experimental result validates that the proposed method is effective for the weed recognition.
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