Classifying Agricultural Crop Pest Data Using Hadoop MapReduce Based C5.0 Algorithm

Authors

  • R. Revathy Department of Computer Applications, Kalasalingam University, Krishnankoil-626126, Tamil Nadu, India
  • S. Balamurali Department of Computer Applications, Kalasalingam University, Krishnankoil-626126, Tamil Nadu, India
  • R. Lawrance Department of Computer Applications, Ayya Nadar Janaki Ammal College, Sivakasi-626124, Tamil Nadu, India

DOI:

https://doi.org/10.13052/2245-1439.835

Keywords:

Data Mining, Data cleaning, Relief feature selector, MapReduce based C5.0 Classification

Abstract

Data mining is a methodology of exploring and processing large pre-existing databases in order to find the hidden information. In agriculture sector, data mining can help farmers to develop yield. Crops can be protected from vertebrate pests and diseases by predicting and enhancing crop cultivation through efficient data mining methods. The main aim of this research is to classify agricultural crop pests which are categorized by different colours. This research includes data cleaning, feature selection and execution of C5.0 algorithm using map reduce. Data cleaning has taken away the noisy data in crop pest data that offers improved accuracy. In feature selection, Relief filter is applied for selecting particular attributes of the crop pest data set instead of using full attribute set. It performs choosing attributes by calculating the attribute weights based upon distances. As the size of the pest dataset has attained terabyte range, typical data mining techniques cannot process the big data at logical time. Hadoop MapReduce programming model has been put into practice to compact with huge data set. It is a software framework for distributed processing of large amount of data. This research work proposed MapReduce implementation of C5.0 decision tree algorithm that gives more accurate result rapidly and holding less memory of huge crop pest data set.

 

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Author Biographies

R. Revathy, Department of Computer Applications, Kalasalingam University, Krishnankoil-626126, Tamil Nadu, India

R. Revathy received B.Sc., M.Sc. and M.Phil. in computer science from Madurai Kamarajar University, Tamil Nadu. She is pursuing Ph.D. in the department of computer applications at Kalsaslingam University, Krishnankoil, Tamil Nadu since 2017. Her current research interests include data mining and machine learning algorithms.

S. Balamurali, Department of Computer Applications, Kalasalingam University, Krishnankoil-626126, Tamil Nadu, India

S. Balamurali is a Professor of Statistics and Director of Computer Applications at the Kalasalingam Academy of Research and Education. He received his undergraduate, postgraduate and doctoral degrees in Statistics from Bharathiar University, India. His research interests include applied statistics, data mining, network security and bioinformatics.

R. Lawrance, Department of Computer Applications, Ayya Nadar Janaki Ammal College, Sivakasi-626124, Tamil Nadu, India

R. Lawrance has received B.Sc. & M.Sc. degree in Computer Science from St. Joseph’s College, Trichy in 1993 & 1998, M.Phil. Computer Science from M.S. University in 2003 and Ph.D. degree from the Vinayaka Missons University in 2011. He has joined Ayya Nadar Janaki Ammal College since 1998 as an assistant Professor. From 2011 onwards, he has been working as a Director in the Department of Computer Applications. His current research interest lies in data mining and machine learning Algorithms. He has produced 24 M.Phil. Scholars and one Ph.D. Scholar and guiding for 7 Ph.D. Scholars. He has published 25 National level conferences, 36 International level conferences and 8 International level Journals.

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Published

2018-01-04

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