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

Published online by Cambridge University Press:  06 March 2017

Anton Ustyuzhanin ,
Karl-Heinz Dammer ,
Antje Giebel ,
Cornelia Weltzien  and
Michael Schirrmann
Anton Ustyuzhanin*
Affiliation:
Scientist, Scientist, Research Technician, Senior Scientist, and Senior Scientist, Department of Engineering for Crop Production, Leibniz Institute for Agricultural Engineering and Bioeconomy, Max-Eyth-Allee 100, 14469 Potsdam, Germany
Karl-Heinz Dammer
Affiliation:
Scientist, Scientist, Research Technician, Senior Scientist, and Senior Scientist, Department of Engineering for Crop Production, Leibniz Institute for Agricultural Engineering and Bioeconomy, Max-Eyth-Allee 100, 14469 Potsdam, Germany
Antje Giebel
Affiliation:
Scientist, Scientist, Research Technician, Senior Scientist, and Senior Scientist, Department of Engineering for Crop Production, Leibniz Institute for Agricultural Engineering and Bioeconomy, Max-Eyth-Allee 100, 14469 Potsdam, Germany
Cornelia Weltzien
Affiliation:
Scientist, Scientist, Research Technician, Senior Scientist, and Senior Scientist, Department of Engineering for Crop Production, Leibniz Institute for Agricultural Engineering and Bioeconomy, Max-Eyth-Allee 100, 14469 Potsdam, Germany
Michael Schirrmann
Affiliation:
Scientist, Scientist, Research Technician, Senior Scientist, and Senior Scientist, Department of Engineering for Crop Production, Leibniz Institute for Agricultural Engineering and Bioeconomy, Max-Eyth-Allee 100, 14469 Potsdam, Germany
*
*Corresponding author’s E-mail: [email protected]
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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.

Ambrosia artemisiifolia es una especie vegetal que causa síntomas de alergia y asma en humanos. Para controlar su propagación, se necesita un sistema de identificación temprana. Sin embargo, debido a su semejanza a Artemisia vulgaris, la identificación adecuada entre estas dos especies de malezas es importante. Por esto proponemos un método para discriminar las hojas de A. artemisiifolia y A. vulgaris con base en imágenes digitales usando el modelo de bolsa de palabras visuales (BoVW). BoVW es una clasificación de imágenes con base en objetos definidos que ha ganado aceptación en muchas áreas de la ciencia. Comparamos las características robustas aceleradas (SURF) y el muestreo en cuadrícula para la selección de puntos clave. El vocabulario de imágenes fue construido usando el agrupamiento de promedios K. El clasificador de imágenes fue entrenado usando máquinas vectoriales de apoyo. Para examinar la robustez del clasificador, se realizaron corridas de modelos específicos con y sin daño en las hojas en los set de datos para entrenamiento. Los resultados mostraron que el modelo BoVW permite la discriminación entre A. artemisiifolia y A. vulgaris con alta exactitud. Con base en los puntos clave SURF, con 50% del total de 788 imágenes como datos de entrenamiento, logramos un 100% de reconocimiento correcto de estas dos especies de plantas. El muestreo en cuadrícula resultó en una exactitud de reconocimiento ligeramente menor (98 a 99%). Adicionalmente, la clasificación basada en SURF fue hasta 31 veces más rápida.

Keywords

Common ragweed, Ambrosia artemisiifolia L.;mugwort, Artemisia vulgaris L.Weed speciesdigital imagesmachine learningspeeded-up robust featuresplant recognition
Type
Weed Management-Techniques
Information
Weed Technology , Volume 31 , Issue 2 , March 2017 , pp. 310 - 319
Copyright
© Weed Science Society of America, 2017 

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Footnotes

Associate Editor for this paper: Andrew Kniss, University of Wyoming

References

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