Abstract
Nowadays, crop nutrient deficiency is common in most of the agricultural fields in India due to inadequate and imbalanced fertilization. The main aim of this work is to segment and calculate the percentage of nutrient deficiency which helps to predict the rate of fertilization needed for that crop. In this paper, a new intuitionistic fuzzy c-means color clustering algorithm (IFCM) is introduced using intuitionistic fuzzy sets (IFSs) with its distance function defined from similarity measure. Initially, all the experimental images are preprocessed. Then the preprocessed images are segmented by using the proposed clustering algorithm. The experimental results obtained by IFCM algorithm are compared with fuzzy c-means algorithm (FCM) to show the effectiveness of the proposed algorithm. Comparison results reveal that the proposed segmentation method is capable of segmenting uncertain crop images with nutrient deficiency.
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References
Camargo, A., Smith, J.S.: An image processing based algorithm to automatically identify plant disease visual symptoms. Biosyst. Eng. 102, 9–21 (2009)
Mao, H.P., Zhang, Y.C., Hu, B.: Segmentation of crop disease leaf images using fuzzy c-means clustering algorithm. Trans. Chin. Soc. Agric. Eng. 24, 136–140 (2008)
Hu, J., Li, D., Chen, G., Duan, Q., Han, Y.: Image segmentation method for crop nutrient deficiency based on fuzzy c-means algorithm. Intell. Autom. Soft Comput. 18, 1145–1155 (2012)
Atanassov, K.T.: Intuitionistic fuzzy sets. Fuzzy Sets and Syst. 20, 87–96 (1986)
Chaira, T.: A novel intuitionistic fuzzy c means clustering algorithm and its application to medical images. Appl. Soft Comput. 11, 1711–1717 (2011)
Bustince, H., Kacpryzk, J., Mohedano, Z.: Intuitionistic fuzzy generators application to intuitionistic fuzzy complementation. Fuzzy Sets and Syst. 114, 485–504 (2000)
Burillo, P., Bustince, H.: Entropy on intuitionistic fuzzy sets and on interval- valued fuzzy set. Fuzzy Sets ans Syst. 78, 305–316 (1996)
Pelekis, N., Iakovidis, D.K., Kotsifakos, E.E., Kopanakis, I.: Fuzzy clustering of intuitionistic fuzzy data. Int. J. Bus. Intell. Data Min. 3, 45–65 (2007)
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Balasubramaniam, P., Ananthi, V.P. (2013). Segmentation of Crop Nutrient Deficiency Using Intuitionistic Fuzzy C-Means Color Clustering Algorithm. In: Prasath, R., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8284. Springer, Cham. https://doi.org/10.1007/978-3-319-03844-5_12
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DOI: https://doi.org/10.1007/978-3-319-03844-5_12
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03843-8
Online ISBN: 978-3-319-03844-5
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