To Recognize the Crop Growth Rate in Agricultural Land By Using K-Means Clustering Algorithm and Contrast Enhancement Algorithm

Authors

  • T. Williams  Department of Computer Science Pondicherry University, Kalapet, Puducherry, Tamil Nadu, India
  • Dr. P. ShanthiBala  Department of Computer Science Pondicherry University, Kalapet, Puducherry, Tamil Nadu, India

Keywords:

Image processing, k-means clustering, algorithm, contrast enhanced algorithm, MATLAB.

Abstract

Agriculture is pillars of the entire world. Over 58 percent of the country homes depend on agriculture as their major means of living. Planting high quality crop is the source by which agriculture yield is increased. Because of the vast range of associated sub domain it is having the current attention of the researchers. In this paper, the exploration of different domains associated with agricultural image processing is defined. MATLAB tool used for identifying the crop growth. Contrast Enhancement algorithm help us to find the difference of crop images. K-means clustering algorithms can categorize 1….n. Grey scale images is converted black & white images. After converting Pixel area is calculated. Future work is to find out the varieties of crops present in the particular area depending upon the growth.

References

  1. Chahal, Neetu. "A study on agricultural image processing along with classification model." Advance Computing Conference (IACC), 2015 IEEE International. IEEE, 2015.
  2. Yan, Man, et al. "K-means cluster algorithm based on color image enhancement for cell segmentation." Biomedical Engineering and Informatics (BMEI), 2012 5th International Conference on. IEEE, 2012.
  3. Kussul, Nataliia, et al. "Deep learning classification of land cover and crop types using remote sensing data." IEEE Geoscience and Remote Sensing Letters 14.5 (2017): 778-782.
  4. Linli, Tu, Deng Yanni, and Chu Siyong. "A K-Means Clustering Algorithm Based on Double Attributes of Objects." Measuring Technology and Mechatronics Automation (ICMTMA), 2015 Seventh International Conference on. IEEE, 2015.
  5. Farhang, Yousef. "Face Extraction from Image based on K-Means Clustering Algorithms." INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 8.9 (2017): 96-107.
  6. Belsare, P. P., and S. K. Shah. "Evaluation of seedling growth rate using image processing." Computational Intelligence and Computing Research (ICCIC), 2013 IEEE International Conference on. IEEE, 2013.
  7. Isa, Nor Ashidi Mat, Samy A. Salamah, and Umi Kalthum Ngah. "Adaptive fuzzy moving K-means clustering algorithm for image segmentation." IEEE Transactions on Consumer Electronics 55.4 (2009).
  8. Xing, Tao, et al. "Refined SAR image segmentation algorithm based on K-means clustering." Radar (RADAR), 2016 CIE International Conference on. IEEE, 2016.
  9. North, Heather, et al. "Classifying agricultural land uses with time series of satellite images." Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International. IEEE, 2012.
  10. Malyszko, Dariusz, and Slawomir T. Wierzchon. "Standard and genetic k-means clustering techniques in image segmentation." Computer Information Systems and Industrial Management Applications, 2007. CISIM'07. 6th International Conference on. IEEE, 2007.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-05-30

Issue

Volume 3, Issue 5, May-June-2018

Section

Research Articles

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Copyright (c) IJSRCSEIT

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
T. Williams, Dr. P. ShanthiBala, " To Recognize the Crop Growth Rate in Agricultural Land By Using K-Means Clustering Algorithm and Contrast Enhancement Algorithm, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 5, pp.41-47, May-June-2018.