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Rule base Knowledge and Fuzzy Approach for Classification of Specific Crop and Acreage Estimation

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
S. S. Thorat, K. V. Kale, , S. C. Mehrotra, Y. D. Rajendra, V. B. Waghmare
10.5120/ijca2017913900

S S Thorat, K V Kale, S C Mehrotra, Y D Rajendra and V B Waghmare. Rule base Knowledge and Fuzzy Approach for Classification of Specific Crop and Acreage Estimation. International Journal of Computer Applications 165(6):38-47, May 2017. BibTeX

@article{10.5120/ijca2017913900,
	author = {S. S. Thorat and K. V. Kale and and S. C. Mehrotra and Y. D. Rajendra and V. B. Waghmare},
	title = {Rule base Knowledge and Fuzzy Approach for Classification of Specific Crop and Acreage Estimation},
	journal = {International Journal of Computer Applications},
	issue_date = {May 2017},
	volume = {165},
	number = {6},
	month = {May},
	year = {2017},
	issn = {0975-8887},
	pages = {38-47},
	numpages = {10},
	url = {http://www.ijcaonline.org/archives/volume165/number6/27581-2017913900},
	doi = {10.5120/ijca2017913900},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Estimation of specific crop and acreage plays a vital role in the field of crop planning, monitoring, crop condition, yield forecasting and acreage estimation. There have been several studies conducted to classify the crops at continental to the regional level, but still, work is needed to map small area covered by different crops using Remote Sensing technology.  The main objective of the present study is to explore whether the Fuzzy classifier can improve the accuracy of crop classification as compared to other traditional Classifiers, such as Maximum likelihood, Mahalanobis etc. The attempt has been done to classify different crops at a smaller scale. The Landsat time series 8 band OLI data was used to investigate multiple crop phenomena. Two scenes were acquired in Kharif seasons (September 28 and October 30, 2014). Three indices such as NDVI, SAVI, and RVI, were used to know vegetation condition. The Spectral signatures generated from data for the residues of Sugarcane and Maize based on prior knowledge of the field work. Four techniques based on Maximum Likelihood, Mahalanobis Classifier, Knowledge classifier and fuzzy classification techniques were used to extract the crops information based on the signatures. The resulting overall classification accuracy was calculated using stratified random sampling method. The corresponding performance efficiency of these four methods was found to be 84%, 85%, 87% and 90.67%, respectively, indicating the fuzzy method to be the most efficient as compared with other classification techniques.

References

  1. Chanussot, J., Benediktsson, J. A., & Fauvel, M. 2006. Classification of remote sensing images from urban areas using a fuzzy possibilistic model.Geoscience and Remote Sensing Letters, IEEE, 3(1), 40-44.
  2. Dhumal, Rajesh K; Rajendra, Yogesh D; Kale, KV; 2013 Classification of Crops from remotely sensed Images: A review International Journal of Engineering Research and Applications (IJERA) www.ijera.com pp.758-761 Issue 3
  3. Rajendra, YD; Mehrotra, SC; Kale, KV; Manza, RR; Dhumal, RK; Nagne, AD; Vibhute, AD; 2014 Evaluation of Partially Overlapping 3D Point Cloud's Registration by using ICP variant and CloudCompare. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences Copernicus GmbH 891 8 40
  4. Susaki, J., & SHIBASAKI, R. 2000. Maximum Likelihood Method Modified in Estimating a Prior Probability and in Improving Misclassification Errors.International Archives of Photogrammetry and Remote Sensing, 33(B7/4; PART 7), 1499-1504.
  5. Zadeh, L. A. 1965. Fuzzy sets. Information and control, 8(3), 338-353.
  6. Malik, V., Gautam, A., Sahai, A., Jha, A., & Singh, A. 2013. Satellite Image Classification Using Fuzzy Logic. International Journal of Recent Technology and Engineering (IJRTE), 2(2), 204-207.
  7. Choodarathnakara, A. L., Kumar, T. A., Koliwad, S., & Patil, C. G. 2012. Mixed Pixels: A Challenge in Remote Sensing Data Classification for Improving Performance. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 1(9), pp-261.
  8. Tzionas, Panagiotis, Stelios E. Papadakis, and DimitrisManolakis 2005 "Plant leaves classification based on morphological features and a fuzzy surface selection technique." Fifth International Conference on Technology and Automation, Thessaloniki, Greece.
  9. Nagne, Ajay D; Dhumal, Rajesh K; Vibhute, Amol D; Rajendra, Yogesh D; Kale, KV; Mehrotra, SC; 2014 Suitable sites identification for solid waste dumping using RS and GIS approach: A case study of Aurangabad,(MS) India India Conference (INDICON), 2014 Annual IEEE IEEE 42887
  10. Vibhute, Amol D; Dhumal, Rajesh K; Nagne, Ajay D; Rajendra, Yogesh D; Kale, KV; Mehrotra, SC; 2016 Analysis, Classification, and Estimation of Pattern for Land of Aurangabad Region Using High-Resolution Satellite Image Proceedings of the Second International Conference on Computer and Communication Technologies Springer India 413-427
  11. Van Niel, T. G., & McVicar, T. R. 2004. Determining temporal windows for crop discrimination with remote sensing: a case study in south-eastern Australia. Computers and Electronics in Agriculture, 45(1), 91-108.
  12. Yang, C., Everitt, J. H., Fletcher, R. S., & Murden, D. 2007. Using high resolution QuickBird imagery for crop identification and area estimation.Geocarto International, 22(3), 219-233.
  13. Musande, V., Kumar, A., Roy, P. S., & Kale, K. 2013. Evaluation of fuzzy-based classifiers for cotton crop identification. Geocarto International, 28(3), 243-257.
  14. Okeke, F., & Karnieli, A. 2006. Methods for fuzzy classification and accuracy assessment of historical aerial photographs for vegetation change analyses. Part I: Algorithm development. International journal of remote sensing, 27(1), 153-176.
  15. Murmu, S., & Biswas, S. 2015. Application of Fuzzy Logic and Neural Network in Crop Classification: A Review. Aquatic Procedia, 4, 1203-1210.
  16. McMahan, J. B., Weber, K. T., & Sauder, J. D. 2002. Using Remotely Sensed Data in Urban Sprawl and Green Space nalyses. Intermountain Journal of Sciences, 8(1), 30-37.
  17. Palamuleni, L., Annegarn, H., Kneen, M., & Landmann, T. 2007, July. Mapping rural savanna woodlands in malawi: a comparison of maximum likelihood and fuzzy classifiers. In Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International (pp. 1260-1264). IEEE.
  18. Leite, P., Feitosa, R. Q., Formaggio, A. R., Costa, G. A. O. P., Pakzad, K., & Sanches, I. 2008, October. Crop type recognition based on Hidden Markov Models of plant phenology. In Computer Graphics and Image Processing, 2008. SIBGRAPI'08. XXI Brazilian Symposium on (pp. 27-34). IEEE
  19. Vieira, M. A., Formaggio, A. R., Rennó, C. D., Atzberger, C., Aguiar, D. A., & Mello, M. P. 2012. Object based image analysis and data mining applied to a remotely sensed Landsat time-series to map sugarcane over large areas.Remote Sensing of Environment, 123, 553-562.
  20. Singh, N. J., Kudrat, M., Jain, K., & Pandey, K. 2011. Cropping pattern of Uttar Pradesh using IRS-P6 (AWiFS) data. International journal of remote sensing, 32(16), 4511-4526.
  21. Galford, G. L., Mustard, J. F., Melillo, J., Gendrin, A., Cerri, C. C., & Cerri, C. E. 2008. Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil. Remote sensing of environment, 112(2), 576-587.
  22. Singh, R. P., et. al 2005. Village level crop inventory using remote sensing and field survey data. Journal of the Indian Society of Remote Sensing, 33(1), 93-98.
  23. Dhumal, Rajesh K; Vibhute, Amol D; Nagne, Ajay D; Rajendra, Yogesh D; Kale, Karbhari V; Mehrotra, Suresh C; 2015 Advances in Classification of Crops using Remote Sensing Data International Journal of Advanced Remote Sensing and GIS pp. 1410-1418 1 4
  24. Rajendra, Yogesh; Thorat, Sandip; Nagne, Ajay; Dhumal, Rajesh; Vibhute, Amol; Varpe, Amarsinh; Kale, KV; 2016 Foundations and Frontiers in Computer, Communication and Electrical Engineering
  25. Vyas, S. P., Oza, M. P., & Dadhwal, V. K. 2005. Multi-crop separability study of Rabi crops using multi-temporal satellite data. Journal of the Indian Society of Remote Sensing, 33(1), 75-79.
  26. Nagne, Ajay D; Dhumal, Rajesh K; Vibhute, Amol D; Rajendra, Yogesh D; Gaikwad, Sandeep; Kale, KV; Mehrotra, SC; 2017 Performance evaluation of urban areas Land Use classification from Hyperspectral data by using Mahalanobis classifier Intelligent Systems and Control (ISCO), 2017 11th International Conference on IEEE 388-392
  27. Dhumal, Rajesh K; Vibhute, Amol D; Nagne, Ajay D; Rajendra, Yogesh D; Kale, Karbhari V; Mehrotra, Suresh C; 2017 Fuzzy convolution tactic for classification of spatial pattern and crop area Intelligent Systems and Control (ISCO), 2017 11th International Conference on IEEE 379-382
  28. Jia, Kun, et al. 2014, Land cover classification using Landsat 8 Operational Land Imager data in Beijing, China. Geocarto International 29.8:941-951.
  29. Panigrahy, R. K., Ray, S. S., & Panigrahy, S. 2009. Study on the utility of IRS-P6 AWIFS SWIR band for crop discrimination and classification. Journal of the Indian Society of Remote Sensing, 37(2), 325-333.
  30. Vassilev, V. 2010. An approach for accuracy assessment comparison between per-pixel supervised and object-oriented classifications on a QuickBird image. In Proceedings of the 30th EARSeL Symposium: Remote Sensing for Science, Education and Culture, Paris, France (Vol. 31).
  31. Droj, G. A. B. R. I. E. L. A. 2007. The applicability of fuzzy theory in remote sensing image classification. Informatica L II, 89-96.
  32. Berchtold, M., Riedel, T., Beigl, M., & Decker, C. 2008. Awarepen-classification probability and fuzziness in a context aware application. InUbiquitous Intelligence and Computing (pp. 647-661). Springer Berlin Heidelberg.
  33. Tong, H., & Kang, Z. (Eds.). 2010. Computational Intelligence and Intelligent Systems: 5th International Symposium, ISICA 2010, Wuhan, China, October 2010, Proceedings (Vol. 107). Springer.
  34. Lagacherie, P., McBratney, A. and Voltz, M.,2006. Digital soil mapping: an introductory perspective (Vol. 31). Elsevier.
  35. Rajendra, Yogesh; Thorat, Sandip; Nagne, Ajay; Dhumal, Rajesh; Vibhute, Amol; Varpe, Amarsinh; Mehrotra, SC; Kale, KV; 2016 Mapping forest cover of Gautala Autramghat ecosystems using geospatial technology Foundations and Frontiers in Computer, Communication and Electrical Engineering: Proceedings of the 3rd International Conference C2E2, Mankundu, West Bengal, India, 15th-16th January, 2016. CRC Press 391
  36. Varpe, Amarsinh B; Rajendra, Yogesh D; Vibhute, Amol D; Gaikwad, Sandeep V; Kale, KV; 2015 Identification of plant species using non-imaging hyperspectral data Man and Machine Interfacing (MAMI), 2015 International Conference on IEEE 42826
  37. Rajendra, Yogesh D; Thorat, Sandip S; Nagne, Ajay D; Vibhute, Amol D; Dhumal, Rajesh K; Varpe, Amarsinh B; Mehrotra, SC; Kale, KV; 2016 Understanding the dynamics of Gautala Autramghat forest: A digital image classification approach Computing, Communication and Automation (ICCCA), 2016 International Conference on IEEE 1166-1169
  38. Jensen, J. R. 1986. Introductory digital image processing: a remote sensing perspective. Univ. of South Carolina, Columbus.
  39. Vassilev, V. 2013. Crop investigation using high-resolution WorldView-1 and QuickBird-2 satellite images on a test site in Bulgaria. Aerospace Research in Bulgaria, 25, 154-171.
  40. Melesse, A. M., & Jordan, J. D. 2002. A comparison of fuzzy vs. augmented-ISODATA classification algorithms for cloud-shadow discrimination from Landsat images. Photogrammetric Engineering and Remote Sensing, 68(9), 905-912.
  41. Galvão, L. S., Formaggio, A. R., & Tisot, D. A. 2005. Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data. Remote Sensing of Environment, 94(4), 523-534.
  42. Silleos, N. G., Alexandridis, T. K., Gitas, I. Z., & Perakis, K. 2006. Vegetation indices: advances made in biomass estimation and vegetation monitoring in the last 30 years. Geocarto International, 21(4), 21-28.
  43. Thorat, S. S., Rajendra, Y. D., Kale, K. V., & Mehrotra, S. C. 2015. Estimation of Crop and Forest Areas using Expert System based Knowledge Classifier Approach for Aurangabad District. International Journal of Computer Applications, 121(23).
  44. Hussain, M., Chen, D., Cheng, A., Wei, H., & Stanley, D. 2013. Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80, 91-106.
  45. Ray, Shibendu Shankar. "Remote sensing applications: Indian experience." Pp 251-264
  46. Wang, Guangxing, and QihaoWeng, eds. 2013, Remote sensing of natural resources.CRC Press,
  47. Huete, A. R. 1988. A soil-adjusted vegetation index (SAVI). Remote sensing of environment, 25(3), 295-309.
  48. Rajendra, Yogesh D; Thorat, Sandip S; Nagne, Ajay D; Baheti, Manasi R; Dhumal, Rajesh K; Varpe, Amarsinh B; Mehrotra, SC; Kale, KV; 2017 Application of Remote Sensing for Assessing Forest Cover Conditions of Aurangabad,(MS), India Proceedings of International Conference on Communication and Networks Springer, Singapore 313-322.

Keywords

Crop Classification, Fuzzy Classifier, Knowledge Classifier, Landsat Data, NDVI.