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International Conference on Advanced Informatics for Computing Research

ICAICR 2019: Advanced Informatics for Computing Research pp 359–368Cite as

Intelligent System to Classify Peanuts Varieties Using K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM)

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1075))

Abstract

In the world, India is the second biggest producer of peanuts or groundnuts, and it is also our country’s major oilseed crop. In India, the existing peanuts crop varieties are GAUG-1, Kuber, Amber, PG-1, BG-1, T-64, GAUG-10, BG-2, Chandra, Kadri-2, Chitra, Kadri-3, Prakash, T-28, Kaushal, etc. Presently, peanuts are having only 75–80% of India’s average market value. Because, the peanuts kernel quality assessment, as well as identifying varieties, are done manually by skilled labors, which leads costly. In this research, an affordable method is proposed to assess the peanuts kernel quality and identifying the different varieties quickly with undamaged, repeatability with low cost, and accurately with high distinguishing rate. Also, to meet the quality of peanuts kernel as per the international market standards and to increase the income of the former. The proposed system relies on computer vision and machine learning. The obtained overall accuracies were K-nearest neighbors (93.33%) and Support vector machine (93.82%). These percentages are discriminating peanuts variety as the best predictive model.

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Acknowledgments

The authors are greatly indebted to the Department of CSE MIT, MAHE, Manipal, INDIA-576104, for providing dedicated lab facilities to achieve this great work.

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Authors and Affiliations

  1. Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India

    V. G. Narendra & K. Govardhan Hegde

Authors
  1. V. G. Narendra
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  2. K. Govardhan Hegde
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Corresponding author

Correspondence to K. Govardhan Hegde .

Editor information

Editors and Affiliations

  1. Papua New Guinea University of Technology, Lae, Papua New Guinea

    Ashish Kumar Luhach

  2. Computer Science Department, Namibia University of Science and Technology, Windhoek, Namibia

    Dharm Singh Jat

  3. Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Kamarul Bin Ghazali Hawari

  4. School of Computing, University of Eastern Finland, Kuopio, Finland

    Xiao-Zhi Gao

  5. Department of Mathematics and Computing Science, Saint Mary's University, Halifax, Canada

    Pawan Lingras

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Narendra, V.G., Govardhan Hegde, K. (2019). Intelligent System to Classify Peanuts Varieties Using K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM). In: Luhach, A., Jat, D., Hawari, K., Gao, XZ., Lingras, P. (eds) Advanced Informatics for Computing Research. ICAICR 2019. Communications in Computer and Information Science, vol 1075. Springer, Singapore. https://doi.org/10.1007/978-981-15-0108-1_33

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  • DOI: https://doi.org/10.1007/978-981-15-0108-1_33

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0107-4

  • Online ISBN: 978-981-15-0108-1

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