Abstract
Plant disease leaf image segmentation plays an important role in the plant disease detection through leaf symptoms. A novel segmentation method of plant disease leaf image is proposed based on a hybrid clustering. The whole color leaf image is firstly divided into a number of compact and nearly uniform superpixels by superpixel clustering, which can provide useful clustering cues to guide image segmentation to accelerate the convergence speed of the expectation maximization (EM) algorithm, and then, the lesion pixels are quickly and accurately segmented from each superpixel by EM algorithm. The experimental results and the comparison results with similar approaches demonstrate that the proposed method is effective and has high practical value for plant disease detection.
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Acknowledgements
This work was partially supported by China National Natural Science Foundation under Grant No. 61473237. It was also supported by the Shaanxi Natural Science Foundation Research Project under Grant No. 2016GY-141. The authors would like to thank all the editors and anonymous reviewers for their constructive advices.
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SWZ conceived the algorithm, carried out analyses, prepared the data sets, carried out experiments and wrote the manuscript; ZHY and XWW designed, performed and analyzed experiments and wrote the manuscript; all authors read and approved the final manuscript.
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Zhang, S., You, Z. & Wu, X. Plant disease leaf image segmentation based on superpixel clustering and EM algorithm. Neural Comput & Applic 31 (Suppl 2), 1225–1232 (2019). https://doi.org/10.1007/s00521-017-3067-8
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DOI: https://doi.org/10.1007/s00521-017-3067-8