Abstract
In Gebhardt et al. (2006) an object-oriented image classification algorithm was introduced for detecting Rumex obtusifolius (RUMOB) and other weeds in mixed grassland swards, based on shape, colour and texture features. This paper describes a new algorithm that improves classification accuracy. The leaves of the typical grassland weeds (RUMOB, Taraxacum officinale, Plantago major) and other homogeneous regions were segmented automatically in digital colour images using local homogeneity and morphological operations. Additional texture and colour features were identified that contribute to the differentiation between grassland weeds using a stepwise discriminant analysis. Maximum-likelihood classification was performed on the variables retained after discriminant analysis. Classification accuracy was improved by up to 83% and Rumex detection rates of 93% were achieved. The effect of image resolution on classification results was investigated. The eight million pixel images were upscaled in six stages to create images with decreasing pixel resolution. Rumex detection rates of over 90% were obtained at almost all resolutions, and there was only moderate misclassification of other objects to RUMOB. Image processing time ranged from 45 s for the full resolution images to 2.5 s for the lowest resolution ones.
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Acknowledgements
The authors gratefully appreciate Dr. John Bailey for its valuable input to the manuscript. The research was funded by the German Research Group (DFG), within the Research Training Group (Graduiertenkolleg) 722 “Information Techniques for Precision Crop Protection” at the University of Bonn (Germany).
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Gebhardt, S., Kühbauch, W. A new algorithm for automatic Rumex obtusifolius detection in digital images using colour and texture features and the influence of image resolution. Precision Agric 8, 1–13 (2007). https://doi.org/10.1007/s11119-006-9024-7
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DOI: https://doi.org/10.1007/s11119-006-9024-7