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Classification of chestnuts according to moisture levels using impact sound analysis and machine learning

  • Ferhat Kurtulmuş
  • Sencer Öztüfekçi
  • İsmail Kavdır
Original Paper
  • 2 Downloads

Abstract

In this study, a prototype system was designed, built and tested to classify chestnuts using impact sound signals and machine learning methods according to moisture contents. Briefly, the system consisted of a shotgun microphone, a sliding platform, an impact surface, a triggering system, a sound device and a computer. Sound signal data were acquired from 2028 chestnut samples with three different moisture levels. Acoustic signals from chestnut samples were filtered to alleviate negative effects of unwanted noise. Four machine learning classifiers using three different feature sets obtained from two feature groups applying feature reduction methods were trained and tested to classify pairs of chestnut moisture group categories as 35% versus 45%, 35% versus 55%, 45% versus 55% (classification with two outputs) and 35% versus 45% versus 55% (classification with three outputs), respectively. The highest classification success (88%) was achieved for the classification application category of 35 versus 55%.

Keywords

Chestnut classification Moisture level Impact acoustics Machine learning 

Notes

Acknowledgements

This work was supported by TUBİTAK, Administration Unit of Scientific Projects (Project No. 114O783, 2016).

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Biosystems Engineering, Faculty of AgricultureUludag UniversityBursaTurkey
  2. 2.Department of Soil Science and Plant Nutrition, Faculty of AgricultureUludag UniversityBursaTurkey
  3. 3.Department of Agricultural Machinery and Technologies Engineering, Faculty of AgricultureÇanakkale Onsekiz Mart UniversityÇanakkaleTurkey

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