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1 March 2017 Discrimination of Common Ragweed (Ambrosia artemisiifolia) and Mugwort (Artemisia vulgaris) Based on Bag of Visual Words Model
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Abstract

Common ragweed is a plant species causing allergic and asthmatic symptoms in humans. To control its propagation, an early identification system is needed. However, due to its similar appearance with mugwort, proper differentiation between these two weed species is important. Therefore, we propose a method to discriminate common ragweed and mugwort leaves based on digital images using bag of visual words (BoVW). BoVW is an object-based image classification that has gained acceptance in many areas of science. We compared speeded-up robust features (SURF) and grid sampling for keypoint selection. The image vocabulary was built using K-means clustering. The image classifier was trained using support vector machines. To check the robustness of the classifier, specific model runs were conducted with and without damaged leaves in the trainings dataset. The results showed that the BoVW model allows the discrimination between common ragweed and mugwort leaves with high accuracy. Based on SURF keypoints with 50% of 788 images in total as training data, we achieved a 100% correct recognition of the two plant species. The grid sampling resulted in slightly less recognition accuracy (98 to 99%). In addition, the classification based on SURF was up to 31 times faster.

Nomenclature: Common ragweed, Ambrosia artemisiifolia L.; mugwort, Artemisia vulgaris L.

© Weed Science Society of America, 2017
Anton Ustyuzhanin, Karl-Heinz Dammer, Antje Giebel, Cornelia Weltzien, and Michael Schirrmann "Discrimination of Common Ragweed (Ambrosia artemisiifolia) and Mugwort (Artemisia vulgaris) Based on Bag of Visual Words Model," Weed Technology 31(2), 310-319, (1 March 2017). https://doi.org/10.1614/WT-D-16-00068.1
Received: 26 April 2016; Accepted: 1 July 2016; Published: 1 March 2017
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