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International Journal of Current Microbiology and Applied Sciences (IJCMAS)
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National Academy of Agricultural Sciences (NAAS)
NAAS Score: *5.38 (2019)
[Effective from January 1, 2019]
For more details click here

ICV 2017: 100.00
Index Copernicus ICI Journals Master List 2017 - IJCMAS--ICV 2017: 100.00
For more details click here

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Original Research Articles

PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
Publisher : Excellent Publishers
Email : editorijcmas@gmail.com / submit@ijcmas.com
Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2017: 100.00
NAAS RATING 2018: 5.38

Int.J.Curr.Microbiol.App.Sci.2019.8(8): 2472-2479
DOI: https://doi.org/10.20546/ijcmas.2019.808.287


Tomato Crop Mapping Using Object Oriented Classification at Shoolagiri and Surroundings, Krishnagiri District, Tamil Nadu, India
V.A. Archana1*, S. Rama Subramoniam2, K. Ganesha Raj3 and V. ANandhi4
1Agricultural Information Technology, Tamil Nadu Agricultural University, Coimbatore, India
2RRSC-South, NRSC/ISRO, Bengaluru, India
3RRSC-South, NRSC/ISRO, Bengaluru, India
4Tamil Nadu Agricultural University, Coimbatore, India
*Corresponding author
Abstract:

The object-oriented image analysis delineates segments of homogeneous image areas. The delineated segments are classified to real world objects based on spectral, textural, neighbourhood an object specific shape parameter. Object-oriented classification of high-resolution imagery is a challenging job for the remote sensing community. Identification of the object-oriented classes based on objects leads to better classification. In this project, the object-oriented classification for tomato in Shoolagiri and surrounding villages of Krishnagiri district in Tamil Nadu, India. In rule-based classification, there are six parameters were attempted:i) Normalized Difference Vegetation Index ii) Maximum Difference iii) Mean-Layer3, Layer2, Layer1 iv) Asymmetry v) Shape Index vi) Border Index. Based on the Maximum Difference, it classifies Tomato for rule-based classification. LISS-IV data was used to classify tomato in the study area using object-oriented classification techniques. Object-oriented classification techniques were investigated using Support Vector Machine and K-Nearest Neighbour approaches. The accuracy of the classification gained through rule-based classification.


Keywords: Object-oriented classification, Support Vector Machine, K-Nearest Neighbour, Rule-based classification
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How to cite this article:

Archana, V.A., S. Rama Subramoniam, K. Ganesha Raj and ANandhi, V. 2019. Tomato Crop Mapping Using Object Oriented Classification at Shoolagiri and Surroundings, Krishnagiri District, Tamil Nadu, India.Int.J.Curr.Microbiol.App.Sci. 8(8): 2472-2479. doi: https://doi.org/10.20546/ijcmas.2019.808.287