The Use of The Temperature Wave Method in Quality Control of Potato Tubers
Source abstract
Abstract
Abstract The article deals with the problem of detecting potato tubers with phytodiseases 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.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Potato Plant Research · Food Drying and Modeling · Food Industry and Aquatic Biology
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (25)
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1. Haq, Z.A., Ashhad, M., Jaffery, Z.A., and Mehfuz, S., Infrared imaging technique and multi-input neural network for potato defect detection, Proc. 2023 Int. Conf. on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies, New Delhi, 2023, pp. 196–200. https://doi.org/10.1109/REEDCON57544.2023.1015050110.1109/REEDCON57544.2023.10150501 ↗
2. Zhu, Q., Guan, J., Huang, M., Lu, R., and Mendoza, F., Predicting bruise susceptibility of ‘Golden Delicious’ apples using hyperspectral scattering technique, Postharvest Biol. Technol., 2016, vol. 114, pp. 415–424. https://doi.org/10.1016/j.postharvbio.2015.12.00710.1016/j.postharvbio.2015.12.007 ↗
3. Al Riza, D. F., Widodo, S., Yamamoto, K., Ninomiya, K., Suzuki, T., Ogawa, Y., and Kondo, N., External defects and severity level evaluation of potato using single and multispectral imaging in near infrared region, Inf. Process. Agric., 2024, vol. 11, no. 1, pp. 142–154. https://doi.org/10.1016/j.inpa.2022.09.00110.1016/j.inpa.2022.09.001 ↗
4. Al Riza, D.F., Suzuki, T., Ogawa, Y., and Kondo, N., Diffuse reflectance characteristic of potato surface for external defects discrimination, Postharvest Biol. Technol., 2017, vol. 133, pp. 88–96. https://doi.org/10.1016/j.postharvbio.2017.07.00610.1016/j.postharvbio.2017.07.006 ↗
5. Ding, J.G., Han, D.H., Li, Y.Y., Peng, Y.K., Wang, Q., and 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, vol. 40, no. 6, pp. 1909–1915. https://doi.org/10.3964/j.issn.1000-0593(2020)06-1909-0710.3964/j.issn.1000-0593(2020)06-1909-07 ↗
6. Gao, H., Li, X., Xu, S., Huang, T., Tao, H., and Li, X., Transmission hyperspectral detection method for weight and black heart of potato, Nongye Gongcheng Xuebao/Trans. Chin. Soc. Agric. Eng., 2013, vol. 29, no. 15, pp. 254–261. https://doi.org/10.3969/j.issn.1002-6819.2013.15.03410.3969/j.issn.1002-6819.2013.15.034 ↗
7. Blasco, J., Aleixos, N., and Moltó, E., Machine vision system for automatic quality grading of fruit, Biosyst. Eng., 2003, vol. 85, no. 4, pp. 415–423. https://doi.org/10.1016/S1537-5110(03)00088-610.1016/S1537-5110(03)00088-6 ↗
8. Keresztes, J. C., Diels, E., Goodarzi, M., Nguyen-Do-Trong, N., Goos, P., Nicolai, B., and Saeys, W., Glare based apple sorting and iterative algorithm for bruise region detection using shortwave infrared hyperspectral imaging, Postharvest Biol. Technol., 2017, vol. 130, pp. 103–115. https://doi.org/10.1016/j.postharvbio.2017.04.00510.1016/j.postharvbio.2017.04.005 ↗
9. Barnes, M., Duckett, T., Cielniak, G., Stroud, G., and Harper, G., Visual detection of blemishes in potatoes using minimalist boosted classifiers, J. Food Eng., 2010, vol. 98, no. 3, pp. 339–346. https://doi.org/10.1016/j.jfoodeng.2010.01.01010.1016/j.jfoodeng.2010.01.010 ↗
10. Li, J., Chen, L., Huang, W., Wang, Q., Zhang, B., Tian, X., Fan, S., and Li, B., Multispectral detection of skin defects of bi-colored peaches based on vis-NIR hyperspectral imaging, Postharvest Biol. Technol., 2016, vol. 112, pp. 121–133. https://doi.org/10.1016/j.postharvbio.2015.10.00710.1016/j.postharvbio.2015.10.007 ↗
11. Sharma, R.R., Reddy, S.V.R., and Gajanan, G., X-ray imaging for quality detection in fruits and vegetables, in Sensor-Based Quality Assessment Systems for Fruits and Vegetables, Boca Raton: CRC, 2020, pp. 45–60.10.1201/9781003084174-9 ↗
12. Chulkov, A.O., Nesteruk, D.A., Shagdyrov, B.I., and Vavilov, V.P., Erratum to: Method and equipment for infrared and ultrasonic thermographic testing of large-sized complex-shaped composite products, Russ. J. Nondestr. Test., 2021, vol. 57, no. 9, p. 832. https://doi.org/10.1134/S106183092109011410.1134/S1061830921090114 ↗
13. Bulanova, V.O., Using the method of pulsed heat source to control the moisture of plant tissues of apples, Vestn. Tambov. Gos. Tekh. Univ., 2020, vol. 26, no. 1, pp. 26–32. https://doi.org/10.17277/vestnik.2020.01.pp.026-03210.17277/vestnik.2020.01.pp.026-032 ↗
14. Kozelskaya, S.O. and Fedotov, M.Y., The development of comprehensive technology for non-destructive testing of implicit defects and service life assessment of polymer composite materials, Vestn. Tambov. Gos. Tekh. Univ., 2023, vol. 29, no. 2, pp. 216–229. https://doi.org/10.17277/vestnik.2023.02.pp.216-22910.17277/vestnik.2023.02.pp.216-229 ↗
15. Zeng, X., Miao, Y., Ubaid, S., Gao, X., and Zhuang, S., Detection and classification of bruises of pears based on thermal images, Postharvest Biol. Technol., 2020, vol. 161, p. 111090. https://doi.org/10.1016/j.postharvbio.2019.11109010.1016/j.postharvbio.2019.111090 ↗
16. Vavilov, V.P., Thermal nondestructive testing: Development of conventional directions and new trends (a review), Russ. J. Nondestr. Test., 2023, vol. 59, no. 6, pp. 702–723. https://doi.org/10.1134/S106183092370043210.1134/S1061830923700432 ↗
17. Schmid, S., Reinhardt, J., and Grosse, C.U., Spatial and temporal deep learning for defect detection with lock-in thermography, NDT and E Int., 2024, vol. 143, p. 103063. https://doi.org/10.1016/j.ndteint.2024.10306310.1016/j.ndteint.2024.103063 ↗
18. Mezghani, S., Perrin, E., Vrabie, V., Bodnar, J.L., Marthe, J., and Cauwe, B., Evaluation of paint coating thickness variations based on pulsed Infrared thermography laser technique, Infrared Phys. Technol., 2016, vol. 76, pp. 260–265. https://doi.org/10.1016/j.infrared.2016.03.01810.1016/j.infrared.2016.03.018 ↗
19. Moskovchenko, A., Vavilov, V., Švantner, M., Muzika, L., and Houdková, Š., Active IR thermography evaluation of coating thickness by determining apparent thermal effusivity, Materials (Basel), 2020, vol. 13, no. 18, p. 4057. https://doi.org/10.3390/ma1318405710.3390/ma13184057 ↗
20. Marinetti, S., Robba, D., Cernuschi, F., Bison, P.G., and Grinzato, E., Thermographic inspection of TBC coated gas turbine blades: Discrimination between coating over-thicknesses and adhesion defects, Infrared Phys. Technol., 2007, vol. 49, no. 3, pp. 281–285. https://doi.org/10.1016/j.infrared.2006.06.01810.1016/j.infrared.2006.06.018 ↗
21. Franke, B., Sohn, Y.H., Chen, X., Price, J.R., and Mutasim, Z., Monitoring damage evolution in thermal barrier coatings with thermal wave imaging, Surf. Coat. Technol., 2005, vol. 200, no. 5–6, pp. 1292–1297. https://doi.org/10.1016/j.surfcoat.2005.07.09010.1016/j.surfcoat.2005.07.090 ↗
22. Liu, B., Zhang, H., Fernandes, H., and Maldague, X., Quantitative evaluation of pulsed thermography, lock-in thermography and vibrothermography on foreign object defect (FOD) in CFRP, Sensors, 2016, vol. 16, no. 5, p. 743. https://doi.org/10.3390/s1605074310.3390/s16050743 ↗
23. Balabanov, P., Egorov, A., Divin, A., Ponomarev, S., Yudaev, V., Baranov, S., and Abu Zetoohn, Mathematical modeling of the heat transfer process in spherical objects with flat, cylindrical and spherical defects, Computation, 2024, vol. 12, no. 148. https://doi.org/10.3390/computation1207014810.3390/computation12070148 ↗
24. Divin, A.G., Churikov, A.A., Filatova, A.G., Balabanov, P.V., and Mochalin, S.N., Method and measuring device for monitoring the thermophysical characteristics of heterogeneous materials of plant origin, Vestn. Mosk. Gos. Tekh. Univ. im. N.E. Baumana, Ser. Priborostr., 2018, no. 5 (122), pp. 15–25.
25. Zhadan, V.Z., Teplofizicheskie osnovy khraneniya sochnogo rastitel’nogo syr’ya na pishchevykh predpriyatiyakh (Thermophysical Foundations of Storage of Succulent Vegetable Raw Materials in Food Enterprises), Moscow: Pishch. Prom-st., 1976.