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Crop Disease Protection Using Parallel Machine Learning Approaches

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Classification in BioApps

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 26))

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Abstract

Crop diseases are the most important biological hazards to challenge sustainable development in agricultural production for many years. Every year, 42% of the global agricultural yield is destroyed by disease. Bioinformatics techniques provide efficient methods with which to analyze and interpret the raw biological data, which helps to study the effect of a pathogen on a crop. Microarray gene expression data represent the expression levels of the genes of a cell (organism) maintained in a particular environment. Hence, significant gene prediction and pathogen–host interactions can be studied using gene expression data. Different machine learning techniques can be applied to extract useful information represented by the candidate genes. The approach proposed in this chapter consists of the preprocessing of gene expression data, gene selection or feature extraction using a parallel approach and classification. The feature selection methods have been analyzed for the extraction of candidate genes with biological significance for rice-related diseases; these are a support vector machine with recursive feature elimination (SVM-RFE), minimum redundancy maximum relevance (mRMR), principal component analysis (PCA), successive feature selection (SFS) and independent component analysis (ICA). In order to deal with computational complexity and the large volume of data, the combination of general-purpose graphics processing unit (GPGPU) computing and MapReduce programming on an Apache Hadoop framework is proposed. The experimental results show improved time efficiency in feature extraction and classification.

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Acknowledgements

This research is an outcome of University Grants Commission project. The work was carried out in PSG-Nokia Centre for Big Data Analytics. The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.

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Authors and Affiliations

  1. Department of CSE, PSG College of Technology, Coimbatore, India

    G. Sudha Sadasivam & Sasirekaa Madhesu

  2. Department of CSE, Government Engineering College, Idukki, Kerala, India

    O. Y. Mumthas & K. Dharani

Authors
  1. G. Sudha Sadasivam
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  2. Sasirekaa Madhesu
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  3. O. Y. Mumthas
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  4. K. Dharani
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Corresponding author

Correspondence to G. Sudha Sadasivam .

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Editors and Affiliations

  1. Department of Information Technology, Techno India College of Technology, Kolkata, India

    Nilanjan Dey

  2. Faculty of Engineering, Tanta University, Tanta, Egypt

    Amira S. Ashour

  3. K.S. Institute of Technology, Bangalore, Karnataka, India

    Surekha Borra

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Sadasivam, G.S., Madhesu, S., Mumthas, O.Y., Dharani, K. (2018). Crop Disease Protection Using Parallel Machine Learning Approaches. In: Dey, N., Ashour, A., Borra, S. (eds) Classification in BioApps. Lecture Notes in Computational Vision and Biomechanics, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-65981-7_9

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  • DOI: https://doi.org/10.1007/978-3-319-65981-7_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65980-0

  • Online ISBN: 978-3-319-65981-7

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