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Number 6

An Efficient Method for Soil Analysis using FARM-IT Approach

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  • Melvin Martis and Paul Lobo and Shreesh Chavan 2016. An Efficient Method for Soil Analysis using FARM-IT Approach. International Journal of Applied Information Systems. 11, 6 (Nov 2016), 16-19. DOI=http://dx.doi.org/10.5120/ijais451392 
  • @article{10.5120/ijais2017451568,
author = {Melvin Martis and Paul Lobo and Shreesh Chavan},
title = {An Efficient Method for Soil Analysis using FARM-IT Approach},
journal = {International Journal of Applied Information Systems},
issue_date = {Nov 2016},
volume = {11},
number = {},
month = {Nov},
year = {2016},
issn = {2249-0868},
pages = {16-19},
numpages = {4},
url = {/archives/volume11/number6/947-947-2016451618},
doi = {10.5120/ijais2016451618},
publisher = {Publisher: Foundation of Computer Science (FCS), NY, USA},
address = {}
}

  • %1 451392
%A Manish M Potey
%A C.  A.  Dhote
%A Deepak Sharma
%T An Efficient Method for Soil Analysis using FARM-IT Approach
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 11
%N 
%P 16-19
%D 2016
%I Publisher: Foundation of Computer Science (FCS), NY, USA

Abstract

Agriculture has benefited from improvement in data mining and automation, in recent times. Based on training dataset, this paper suggests a data mining method to analyze soil content such as pH, Field Capacity, Wilting Point, Nitrogen, Potassium, Phosphorus, etc. and helps determine the amount of fertilizer required. Additionally, an irrigation facility based on automated detection of moisture can help optimize the use of water. This system uses a server. The sensors output to server through a distributed network connected wirelessly via a tool such as, ZigBee. This project aims at optimizing the use of fertilizer and water, and accordingly, suggest proper actions.

References

  1. Nagarajapandian M , Ram Prasanth U , Selva Kumar G, Tamil Selvan S, “Automatic irrigation system on sensing soil moisture content”, International Journal Of Innovative Research In Electrical, Electronics, Instrumentation And Control Engineering, Vol. 3, Issue 1, January 2015.
  2. Jay Gholap, “Soil Data Analysis Using Classification Techniques and Soil Attribute Prediction” Asian Journal of Computer Science and Information Technology Vol. 2(8) (2012) pp. 251– 252.
  3. William Myers, Seth Linden, and Gerry Wiener, “A data mining approach to soil temperature and moisture prediction” Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd.
  4. P. Bhargavi, Dr.S. Jyothi, “Applying Naive Bayes Data Mining Technique for Classification of Agricultural Land Soils”, IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.8, August 2009.
  5. “Soil attributes as predictors of crop production under standardized conditions”, Stenberg, B. Biol. Fertil. Soils (1998) 27: 104.
  6. Hitarthi Bhatt, Shraddha Mehta, Lynette R. D'mello, Dept. of Computer Engineering, D.J. Sanghvi College of Engineering, “Use of ID3 Decision Tree Algorithm for Placement Prediction” (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (5), 2015, 4785-478
  7. Haiyi Zhang, Di Li Jodrey School of Computer Science Acadia University, Canada, “Naïve Bayes Text Classifier” 2007 IEEE International Conference on Granular Computing DOI 10.1109/GrC.2007.40
  8. Ray, S (2015, September 13). 6 Easy Steps to Learn Naive Bayes Algorithm (with code in Python). Retrieved from https://www.analyticsvidhya.com/blog/2015/09/naive-bayes-explained/

Keywords

Naïve Bayes, Iterative Dichotomiser 3 (ID3), WEKA tool, Data mining, NPK values, Decision tree, Algorithms

Index Terms

Computer Science
Information Sciences