Abstract
This paper presents a novel and efficient way to detect the presence and identification of disease in wheat leaf from its image. The system applies FCM on data-points consisting of selected features of a set of Wheat Leaf images. In the first step, number of clusters is fixed to 2, in order to divide the input into sets of diseased and undiseased leaf images. The diseased leaf set is further classified into 4 sets corresponding to possibility of occurrence of known 4 types of disease, by applying FCM on this set with number of clusters fixed to 4. We have proposed an efficient method for selection of feature set based on inter and intra-class variance. Although testing has been done only on wheat leaf images, this method can also be applied on other leaf images through careful selection of the feature set.
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Majumdar, D., Ghosh, A., Kole, D.K., Chakraborty, A., Majumder, D.D. (2015). Application of Fuzzy C-Means Clustering Method to Classify Wheat Leaf Images Based on the Presence of Rust Disease. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-319-11933-5_30
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DOI: https://doi.org/10.1007/978-3-319-11933-5_30
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11932-8
Online ISBN: 978-3-319-11933-5
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