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Development of an Optical System with an Orientation Module to Detect Surface Damage to Potato Tubers

Author

Listed:
  • Alexey Dorokhov

    (FSBSI “Federal Scientific Agronomic and Engineering Center VIM”, Moscow 109428, Russia)

  • Alexander Aksenov

    (FSBSI “Federal Scientific Agronomic and Engineering Center VIM”, Moscow 109428, Russia)

  • Alexey Sibirev

    (FSBSI “Federal Scientific Agronomic and Engineering Center VIM”, Moscow 109428, Russia)

  • Dmitry Hort

    (FSBSI “Federal Scientific Agronomic and Engineering Center VIM”, Moscow 109428, Russia)

  • Maxim Mosyakov

    (FSBSI “Federal Scientific Agronomic and Engineering Center VIM”, Moscow 109428, Russia)

  • Nikolay Sazonov

    (FSBSI “Federal Scientific Agronomic and Engineering Center VIM”, Moscow 109428, Russia)

  • Maria Godyaeva

    (FSBSI “Federal Scientific Agronomic and Engineering Center VIM”, Moscow 109428, Russia)

Abstract

This method is a variant of non-destructive multiparametric surface analysis and includes the implementation of hyperspectral and RGB image processing approaches from different angles. This work is based on a fundamental hyperspectral survey system for obtaining data on scanned biological objects in many spectral ranges and with several possible variants of assembling a system with different types of surface illumination with point light and diffuse illumination. The implementation of the technology through the use of a diffused light source provides the diffuse illumination of a biological object with pronounced symptoms of rarefaction on the surface of a biological object—in this case, potato tubers, due to the presence of signs of disease on the potato peel, as well as their deformation. Using broadband lamps, a short-pass filter is located between the source and the object (λ ≤ 400 nm, λ may vary depending on the excitation length), and a long-pass filter (λ ≥ 400 nm) between the root or onion and the chamber. The use of a vision system with a created database containing models of real defects in potato tubers showed a high sorting efficiency, providing an accuracy of sorting by size of 95.4%, and an accuracy by the presence of defects of 93.1%.

Suggested Citation

  • Alexey Dorokhov & Alexander Aksenov & Alexey Sibirev & Dmitry Hort & Maxim Mosyakov & Nikolay Sazonov & Maria Godyaeva, 2023. "Development of an Optical System with an Orientation Module to Detect Surface Damage to Potato Tubers," Agriculture, MDPI, vol. 13(6), pages 1-27, June.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:6:p:1188-:d:1162859
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    Cited by:

    1. Rui-Feng Wang & Wen-Hao Su, 2024. "The Application of Deep Learning in the Whole Potato Production Chain: A Comprehensive Review," Agriculture, MDPI, vol. 14(8), pages 1-30, July.

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