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Classification of organic and conventional olives using convolutional neural networks

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Abstract

This paper presents a convolutional neural network (CNN) to classify between the conventionally and organically cultivated Memecik varieties of green olives. The image forming method called the rising paper chromatography is utilized in preparing the images of Memecik varieties of green olives for CNN. In the rising chromatography method, 20, 30, and 40% sample concentrations were determined as the suitable concentrations for both organic and conventional olives. The concentrations of AgNO3 and FeSO4 were determined as 0.25, 0.5, 0.75 and 1% for both conventional and organic samples. The visual differences used for differentiation of different types of Memecik green olives are usually determined according to the regional color differences, the vivid color occurrence, the width and the frequency of bowl occurrence, the thin line, and the picks at drop zone by the expert assessors. The testing results in this study verified the effectiveness of the CNN methodology in differentiating between the organically and conventionally cultivated Memecik green olives. The newly designed neural network achieved 100% accuracy. Furthermore, this high accuracy achieved by CNN might suggest that it can be effectively used in place of the expert assessors.

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Authors and Affiliations

  1. Department of Software Engineering, Yasar University, Izmir, Turkey

    Mehmet S. Unluturk

  2. Food Engineering Department, Ege University, 35040, Izmir, Turkey

    Secil Kucukyasar & Fikret Pazir

Authors
  1. Mehmet S. Unluturk
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  2. Secil Kucukyasar
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  3. Fikret Pazir
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Correspondence to Mehmet S. Unluturk.

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The authors, Mehmet S. Unluturk, Secil Kucukyasar, and Fikret Pazir declare that they have no conflict of interest.

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Unluturk, M.S., Kucukyasar, S. & Pazir, F. Classification of organic and conventional olives using convolutional neural networks. Neural Comput & Applic 33, 16733–16744 (2021). https://doi.org/10.1007/s00521-021-06269-z

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