Evaluation of predictive data mining algorithms in soil data classification for optimized crop recommendation

Agricultural research has strengthened the optimized economical profit, internationally and is very vast and important field to gain more benefits. However, it can be enhanced by the use of different technological resources, tool, and procedures. Today, the term data mining [1][2] is an interdisciplinary process of analyzing, processing and evaluating the real-world datasets and prediction on the basis of the findings. Our case-based analysis provides empirical evidence that we can use different data mining classification algorithms to classify the dataset of agricultural regions on the basis of soil properties. Additionally, we have investigated the most performing algorithm having powerful prediction accuracy to recommend the best crop for better yield.

[1]  Andreas Holzinger,et al.  Data Mining with Decision Trees: Theory and Applications , 2015, Online Inf. Rev..

[2]  Christian Maaß,et al.  Multi-value Classification of Very Short Texts , 2008, KI.

[3]  Ian H. Witten,et al.  Weka-A Machine Learning Workbench for Data Mining , 2005, Data Mining and Knowledge Discovery Handbook.

[4]  Donald K. Wedding,et al.  Discovering Knowledge in Data, an Introduction to Data Mining , 2005, Inf. Process. Manag..

[5]  A. E. Eiben,et al.  Genetic algorithms with multi-parent recombination , 1994, PPSN.

[6]  Sven F. Crone,et al.  The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing , 2006, Eur. J. Oper. Res..

[7]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[8]  Neeraj Bhargava,et al.  Decision Tree Analysis on J48 Algorithm for Data Mining , 2013 .

[9]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[10]  Geoff Holmes,et al.  The development of Holte's 1R classifier , 1995, Proceedings 1995 Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems.

[11]  James R. Anderson,et al.  A land use and land cover classification system for use with remote sensor data , 1976 .

[12]  Timothy B. Parkin,et al.  Defining and Assessing Soil Quality , 1994 .

[13]  Mohammed N. Al-Kabi,et al.  A Comparison Study between Data Mining Tools over some Classification Methods , 2011 .

[14]  Alessandro Flammini,et al.  Agriculture, Forestry and Other Land Use Emissions by Sources and Removals by Sinks , 2014 .

[15]  Kalina Bontcheva,et al.  Adapting Support Vector Machines for F-term-based Classification of Patents , 2008, TALIP.

[16]  V. Ramesh,et al.  Classification of Agricultural Land Soils: A Data Mining Approach , 2011 .

[17]  D. A. Kumar,et al.  A Survey on Data Mining and Pattern Recognition Techniques for Soil Data Mining , 2011 .

[18]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[19]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[20]  Tok Wang Ling,et al.  Fast text classification: a training-corpus pruning based approach , 2003, Eighth International Conference on Database Systems for Advanced Applications, 2003. (DASFAA 2003). Proceedings..

[21]  Le Gruenwald,et al.  A survey of data mining and knowledge discovery software tools , 1999, SKDD.

[22]  B. Christensen.  Physical fractionation of soil and structural and functional complexity in organic matter turnover , 2001 .

[23]  安藤 寛,et al.  Cross-Validation , 1952, Encyclopedia of Machine Learning and Data Mining.

[24]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.