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Survey on Classification Techniques for Soil Data Prediction to Better Yielding of Crops

S. Manimekalai1 , K. Nandhini2

  1. Department of Computer Science, Chikkanna Government Arts College Tirupur, Tamilnadu, India.
  2. Department of Computer Science, Chikkanna Government Arts College Tirupur, Tamilnadu, India.

Correspondence should be addressed to: manimekalaipari@gmail.com.

Section:Survey Paper, Product Type: Journal Paper
Volume-6 , Issue-1 , Page no. 203-206, Jan-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i1.203206

Online published on Jan 31, 2018

Copyright © S. Manimekalai, K. Nandhini . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: S. Manimekalai, K. Nandhini, “Survey on Classification Techniques for Soil Data Prediction to Better Yielding of Crops,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.203-206, 2018.

MLA Style Citation: S. Manimekalai, K. Nandhini "Survey on Classification Techniques for Soil Data Prediction to Better Yielding of Crops." International Journal of Computer Sciences and Engineering 6.1 (2018): 203-206.

APA Style Citation: S. Manimekalai, K. Nandhini, (2018). Survey on Classification Techniques for Soil Data Prediction to Better Yielding of Crops. International Journal of Computer Sciences and Engineering, 6(1), 203-206.

BibTex Style Citation:
@article{Manimekalai_2018,
author = {S. Manimekalai, K. Nandhini},
title = {Survey on Classification Techniques for Soil Data Prediction to Better Yielding of Crops},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {1 2018},
volume = {6},
Issue = {1},
month = {1},
year = {2018},
issn = {2347-2693},
pages = {203-206},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1659},
doi = {https://doi.org/10.26438/ijcse/v6i1.203206}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i1.203206}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1659
TI - Survey on Classification Techniques for Soil Data Prediction to Better Yielding of Crops
T2 - International Journal of Computer Sciences and Engineering
AU - S. Manimekalai, K. Nandhini
PY - 2018
DA - 2018/01/31
PB - IJCSE, Indore, INDIA
SP - 203-206
IS - 1
VL - 6
SN - 2347-2693
ER -

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Abstract

Yield prediction is a significant contribution for agriculture data mining to the proper choice of crops for sowing. This makes the difficulty of predicting the yielding of crops a remarkable challenge. Earlier yield prediction was performed by considering the farmer`s experience on a selected field and crop. The main thing of the crop yielding is soil. This work presents the use of classification techniques to predict the soil datasets. The predicted results will express the yielding of crops. The issue of predicting the soil data is recognized as data mining technique. The soil is classified by using these techniques Naive Bayes, Decision Tree, fuzzy and neural network are used. The set of rules JRip is applied and validated on this paper using weka tool.

Key-Words / Index Term

Data mining, Fuzzy, neural network, decision tree, soil dataset

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