Abstract
A novel unsupervised color image segmentation method is proposed in this paper. First, the image is converted to HSI color model and then it is divided into 5x5 grid matrix resulting into 25 blocks of original leaf image. Each block is then processed separately and passed under an unsupervised segmentation. This segmentation is based on minimizing the energy of each region in the image. This gives the better result in cased of diseased leaf image dataset. This automated system is very much applicable in research work by the botanists specially working with crop diseases and production.
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Prasad, S., Kumar, P., Jain, A. (2011). Detection of Disease Using Block-Based Unsupervised Natural Plant Leaf Color Image Segmentation. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2011. Lecture Notes in Computer Science, vol 7076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27172-4_49
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DOI: https://doi.org/10.1007/978-3-642-27172-4_49
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