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

V. G. Narendra, K. Govardhan Hegde

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationAdvanced Informatics for Computing Research - 3rd International Conference, ICAICR 2019, Revised Selected Papers
EditorsAshish Kumar Luhach, Dharm Singh Jat, Kamarul Bin Ghazali Hawari, Xiao-Zhi Gao, Pawan Lingras
PublisherSpringer Paris
Pages359-368
Number of pages10
ISBN (Print)9789811501074
DOIs
Publication statusPublished - 01-01-2019
Event3rd International Conference on Advanced Informatics for Computing Research, ICAICR 2019 - Shimla, India
Duration: 15-06-201916-06-2019

Publication series

NameCommunications in Computer and Information Science
Volume1075
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference3rd International Conference on Advanced Informatics for Computing Research, ICAICR 2019
CountryIndia
CityShimla
Period15-06-1916-06-19

Fingerprint

Intelligent systems
Intelligent Systems
Crops
Support vector machines
Nearest Neighbor
Support Vector Machine
Classify
India
Amber
kernel
Oilseeds
Groundnut
Computer vision
Learning systems
Quality Assessment
Repeatability
Predictive Model
Personnel
Computer Vision
Percentage

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)

Cite this

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 A. K. Luhach, D. S. Jat, K. B. G. Hawari, X-Z. Gao, & P. Lingras (Eds.), Advanced Informatics for Computing Research - 3rd International Conference, ICAICR 2019, Revised Selected Papers (pp. 359-368). (Communications in Computer and Information Science; Vol. 1075). Springer Paris. https://doi.org/10.1007/978-981-15-0108-1_33
Narendra, V. G. ; Govardhan Hegde, K. / Intelligent System to Classify Peanuts Varieties Using K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM). Advanced Informatics for Computing Research - 3rd International Conference, ICAICR 2019, Revised Selected Papers. editor / Ashish Kumar Luhach ; Dharm Singh Jat ; Kamarul Bin Ghazali Hawari ; Xiao-Zhi Gao ; Pawan Lingras. Springer Paris, 2019. pp. 359-368 (Communications in Computer and Information Science).
@inproceedings{731a92e24edf46de96c2d22cbf34f642,
title = "Intelligent System to Classify Peanuts Varieties Using K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM)",
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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Narendra, VG & Govardhan Hegde, K 2019, Intelligent System to Classify Peanuts Varieties Using K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM). in AK Luhach, DS Jat, KBG Hawari, X-Z Gao & P Lingras (eds), Advanced Informatics for Computing Research - 3rd International Conference, ICAICR 2019, Revised Selected Papers. Communications in Computer and Information Science, vol. 1075, Springer Paris, pp. 359-368, 3rd International Conference on Advanced Informatics for Computing Research, ICAICR 2019, Shimla, India, 15-06-19. https://doi.org/10.1007/978-981-15-0108-1_33

Intelligent System to Classify Peanuts Varieties Using K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM). / Narendra, V. G.; Govardhan Hegde, K.

Advanced Informatics for Computing Research - 3rd International Conference, ICAICR 2019, Revised Selected Papers. ed. / Ashish Kumar Luhach; Dharm Singh Jat; Kamarul Bin Ghazali Hawari; Xiao-Zhi Gao; Pawan Lingras. Springer Paris, 2019. p. 359-368 (Communications in Computer and Information Science; Vol. 1075).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Narendra VG, Govardhan Hegde K. Intelligent System to Classify Peanuts Varieties Using K-Nearest Neighbors (K-NN) and Support Vector Machine (SVM). In Luhach AK, Jat DS, Hawari KBG, Gao X-Z, Lingras P, editors, Advanced Informatics for Computing Research - 3rd International Conference, ICAICR 2019, Revised Selected Papers. Springer Paris. 2019. p. 359-368. (Communications in Computer and Information Science). https://doi.org/10.1007/978-981-15-0108-1_33