THE USE OF PULSED PHASE THERMOGRAPHY IN THE CONTROL OF POTATO TUBERS

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

The article deals with the problem of detecting potato tubers with phyto-diseases and mechanical damage in the early stages, which is critically important to prevent spoilage of the harvested crop during storage and transportation. The authors explore the method of active thermal control as a promising alternative to traditional methods (visual, multispectral, hyperspectral). The main idea of the method is to periodically heat the tuber surface with infrared radiation and then analyze not only the amplitude but also the phase of temperature fluctuations on the surface using a thermal imaging camera. Numerical modeling in the COMSOL environment and subsequent experiments on real samples have shown that the proposed method makes it possible to effectively detect both surface and subsurface defects (for example, dry rot) at a depth of up to 2 mm, while reducing the influence of contamination, glare and uneven illumination due to the curvature of the surface. It has been established that low periodic heating frequencies (less than 0.1 Hz) and a heat flux density of up to 1700 W/m2 should be used for optimal detection of defects at various depths. The use of an artificial neural network to classify images based on amplitude and phase components has made it possible to achieve 88% error detection accuracy

About the authors

Alexander Divin

Tambov State Technical University

Author for correspondence.
Email: agdv@yandex.ru
ORCID iD: 0000-0001-7578-0505

профессор, профессор кафедры "Мехатроника и технологические измерения"

Russian Federation, 392000 Tambov, Sovetskaya str., 106

Pavel Balabanov

Tambov State Technical University

Email: pav-balabanov@yandex.ru
Russian Federation, 392000 Tambov, Sovetskaya str., 106

Andrey Egorov

Tambov State Technical University

Email: egorov.andrey@list.ru
Russian Federation, 392000 Tambov, Sovetskaya str., 106

Sergey Ponomarev

Tambov State Technical University

Email: svponom@yahoo.com
Russian Federation, 392000 Tambov, Sovetskaya str., 106

Dmitry Muromtsev

Tambov State Technical University

Email: nauka@tstu.ru
Russian Federation, 392000 Tambov, Sovetskaya str., 106

Victor Zabrovsky

Tambov State Technical University
Tambov State University named after G.R. Derzhavin

Email: sq90@mail.ru
Russian Federation, 392000 Tambov, Sovetskaya str., 106 392000 Tambov, Internatsionalnaya str., 36

Daria Lyubimova

Tambov State Technical University

Email: divinadar@yandex.ru
Russian Federation, 392000 Tambov, Sovetskaya str., 106

Ilya Grinko

Tambov State Technical University

Email: skyteks@coud.com
Russian Federation, 392000 Tambov, Sovetskaya str., 106

References

  1. Haq Z.A., Ashhad M., Jaffery Z.A., Mehfuz S. Infrared Imaging Technique and Multi-Input Neural Network for Potato Defect Detection / 2023 International Conference on Recent Advances in Electrical, Electronics and Digital Healthcare Technologies, REEDCON 2023. 2023. P. 196—200. doi: 10.1109/REEDCON57544.2023.10150501
  2. Zhu Q., Guan J., Huang M., Lu R., Mendoza F. Predicting bruise susceptibility of ‘Golden Delicious’ apples using hyperspectral scattering technique // Postharvest Biol. Technol. 2016. V. 114. P. 415—424. doi: 10.1016/j.postharvbio.2015.12.007
  3. Al Riza D. F., Widodo S., Yamamoto K., Ninomiya K., Suzuki T., Ogawa Y., Kondo N. External defects and severity level evaluation of potato using single and multispectral imaging in near infrared region // Inf. Process. Agric. 2024. V. 11. No. 1. P. 142—154. doi: 10.1016/j.inpa.2022.09.001
  4. Al Riza D.F., Suzuki T., Ogawa Y., Kondo N. Diffuse reflectance characteristic of potato surface for external defects discrimination // Postharvest Biol. Technol. 2017. V. 133. P. 88—96. doi: 10.1016/j.postharvbio.2017.07.006
  5. Ding J.G., Han D.H., Li Y.Y., Peng Y.K., Wang Q., Han X. Simultaneous Non-Destructive On-Line Detection of Potato Black-Heart Disease and Starch Content Based on Visible/Near Infrared Diffuse Transmission Spectroscopy // Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy Spectr. Anal. 2020. V. 40. No. 6. P. 1909—1915. doi: 10.3964/j.issn.1000-0593(2020)06-1909-07
  6. Gao H., Li X., Xu S., Huang T., Tao H., Li X. Transmission hyperspectral detection method for weight and black heart of potato // Nongye Gongcheng Xuebao/Transactions Chinese Soc. Agric. Eng. 2013. V. 29. No. 15. P. 254—261. doi: 10.3969/j.issn.1002-6819.2013.15.034
  7. Blasco J., Aleixos N., Moltó E. Machine vision system for automatic quality grading of fruit // Biosyst. Eng. 2003. V. 85. No. 4. P. 415—423. doi: 10.1016/S1537-5110(03)00088-6
  8. Keresztes J. C., Diels E., Goodarzi M., Nguyen-Do-Trong N., Goos P., Nicolai B., Saeys W. Glare based apple sorting and iterative algorithm for bruise region detection using shortwave infrared hyperspectral imaging // Postharvest Biol. Technol. 2017. V. 130. doi: 10.1016/j.postharvbio.2017.04.005
  9. Barnes M., Duckett T., Cielniak G., Stroud G., Harper G. Visual detection of blemishes in potatoes using minimalist boosted classifiers // J. Food Eng. 2010. V. 98. No. 3. doi: 10.1016/j.jfoodeng.2010.01.010
  10. Li J., Chen L., Huang W., Wang Q., Zhang B., Tian X., Fan S., Li B. Multispectral detection of skin defects of bi-colored peaches based on vis-NIR hyperspectral imaging // Postharvest Biol. Technol. 2016. V. 112. doi: 10.1016/j.postharvbio.2015.10.007
  11. Sharma R.R., Reddy S.V.R., Gajanan G. X-Ray Imaging for Quality Detection in Fruits and Vegetables // Sensor-Based Quality Assessment Systems for Fruits and Vegetables. 2020. P. 45—60.
  12. Chulkov A.O., Nesteruk D.A., Shagdyrov B.I., Vavilov V.P. Erratum to: Method and Equipment for Infrared and Ultrasonic Thermographic Testing of Large-Sized Complex-Shaped Composite Products // Russian Journal of Nondestructive Testing. 2021. V. 57. No. 9. P. 832. doi: 10.1134/S1061830921090114
  13. Bulanova V. O. Using the Method of Pulsed Heat Source to Control the Moisture of Plant Tissues of Apples // Vestn. Tambovskogo Gos. Teh. Univ. 2020. V. 26. No. 1. P. 026—032. doi: 10.17277/vestnik.2020.01.pp.026-032
  14. Kozelskaya S. O., Fedotov M. Y. The Development of Comprehensive Technology for Non-Destructive Testing of Implicit Defects and Service Life Assessment of Polymer Composite Materials // Vestn. Tambovskogo Gos. Teh. Univ. 2023. V. 29. No. 2. P. 216—229. doi: 10.17277/vestnik.2023.02.pp.216-229
  15. Zeng X., Miao Y., Ubaid S., Gao X., Zhuang S. Detection and classification of bruises of pears based on thermal images // Postharvest Biol. Technol. 2020. V. 161. doi: 10.1016/j.postharvbio.2019.111090
  16. Zeng X., Miao Y., Ubaid S., Gao X., Zhuang S. Detection and classification of bruises of pears based on thermal images // Postharvest Biol. Technol. 2020. V. 161. 111090. doi: 10.1016/j.postharvbio.2019.111090
  17. Vavilov V. P. Thermal nondestructive testing: traditional approaches and novel trends (review) // Defectoskopiya. 2023. No. 6. P. 38—58. doi: 10.31857/s0130308223060040
  18. Schmid S., Reinhardt J., Grosse C.U. Spatial and temporal deep learning for defect detection with lock-in thermography // NDT E. Int. 2024. V. 143. P. 103063. doi: 10.1016/j.ndteint.2024.103063
  19. Mezghani S., Perrin E., Vrabie V., Bodnar J.L., Marthe J., Cauwe B. Evaluation of paint coating thickness variations based on pulsed Infrared thermography laser technique // Infrared Phys. Technol. 2016. V. 76. P. 260—265. doi: 10.1016/j.infrared.2016.03.018
  20. Moskovchenko A., Vavilov V., Švantner M., Muzika L., Houdková Š. Active IR thermography evaluation of coating thickness by determining apparent thermal effusivity // Materials (Basel). 2020. V. 13. No. 18. P. 4057. doi: 10.3390/ma13184057
  21. Marinetti S., Robba D., Cernuschi F., Bison P.G., Grinzato E. Thermographic inspection of TBC coated gas turbine blades: Discrimination between coating over-thicknesses and adhesion defects // Infrared Phys. Technol. 2007. V. 49. No. 3 SPEC. ISS. P. 281—285. doi: 10.1016/j.infrared.2006.06.018
  22. Franke B., Sohn Y.H., Chen X., Price J.R., Mutasim Z. Monitoring damage evolution in thermal barrier coatings with thermal wave imaging // Surf. Coatings Technol. 2005. V. 200. No. 5—6. P. 1292—1297. doi: 10.1016/j.surfcoat.2005.07.090
  23. Liu B., Zhang H., Fernandes H., Maldague X. Quantitative evaluation of pulsed thermography, lock-in thermography and vibrothermography on foreign object defect (FOD) in CFRP // Sensors (Switzerland). 2016. V. 16. No. 5. P. 743. doi: 10.3390/s16050743
  24. Balabanov P., Egorov A., Divin A., Ponomarev S., Yudaev V., Baranov S., Abu Zetoonh. Mathematical Modeling of the Heat Transfer Process in Spherical Objects with Flat, Cylindrical and Spherical Defects // Computation. 2024. V. 12. No. 148. doi: 10.3390/computation12070148
  25. Divin A.G., Churikov A.A., Filatova A.G., Balabanov P.V., Mochalin S.N. Method and measuring device for monitoring the thermophysical characteristics of heterogeneous materials of plant origin // Vestn. Moscow State Technical University N.E. Bauman. Ser. Instrument engineering. 2018. No. 5 (122). P. 15—25.
  26. Zhadan V.Z. Thermophysical foundations of storage of succulent vegetable raw materials in food enterprises. Moscow: Food Industry, 1976. 200 p.

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2026 Russian Academy of Sciences

Согласие на обработку персональных данных

 

Используя сайт https://journals.rcsi.science, я (далее – «Пользователь» или «Субъект персональных данных») даю согласие на обработку персональных данных на этом сайте (текст Согласия) и на обработку персональных данных с помощью сервиса «Яндекс.Метрика» (текст Согласия).