DOI: https://doi.org/10.32515/2414-3820.2020.50.196-205
Method and Device for Automatic Recognition of Unconditional Potato Tubes
About the Authors
Volodimir Vetokhin, Associate Professor, Doctor in Technics (Doctor of Technics Sciences), Poltava State Agrarian Academy, Poltava, Ukrain, e-mail: veto.vladim@gmail.com
Viktor Goldyban, PhD in Technics (Candidate of Technics Sciences), RUE «SPC NAS of Belarus for Agriculture Mechanization», Minsk, Republic of Belarus, e-mail: labpotato@mail.ru, ORCID ID: 0000-0002-5332-926X
M. Kurylovich, RUE «SPC NAS of Belarus for Agriculture Mechanization», Minsk, Republic of Belarus, ORCID ID: 0000-0003-1067-1310
Abstract
The aim of the article is to improve the quality and productivity of sorting by developing a method and an intelligent device for automatic recognition and inspection of substandard potato tubers.
The article describes a prototype of an automatic sorting machine designed to recognize external defects in potato tubers and automatically inspect them with a jet of compressed air. The recognition process consisted of three main modules: segmentation, tracking a potato moving in a frame along a conveyor belt, and classification using a trained artificial neural network. For the segmentation of potato tubers against the background of the transporting conveyor in real time, a method based on the calculation of the color threshold was used. The centroid tracking algorithm was used to track moving potato tubers. To train the artificial neural network, we created our own dataset consisting of images of marketable and defective potato tubers.
A prototype of an automatic sorting machine has been developed, which is based on the concept of intelligent data analysis, according to which the images of potato tubers obtained from a video camera are processed and formed into images with subsequent recognition and signaling to the executive device of the automatic inspection system in the form of a single pulse signal when determining the tuber as substandard.
Keywords
club potato, defect, automatic sorting, machine vision
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References
1. Noordam, J.C., Otten G.W., Timmermans, A.J.M., van Zwol, B.H. (2017). High speed potato grading and quality inspection based on a color vision system. Control Systems. 2017. P. 15–24. [in English].
2. Byshov N.V. et al. (2013). Sovershenstvovaniye tekhnologicheskogo protsessa sortirovki klubney kartofelya po tsvetovoy informatsii [Improvement of the technological process of sorting potato tubers by color information]. Nauchnyy zhurnal KubGAU – Scientific journal of KubSAU, № 89 (05), 1–12. [in Russian].
3. Golmohammadi, A., Bejaei F., Behfar H. (2013). Design, Development and Evaluation of an Online Potato Sorting System Using Machine Vision. International Journal of Agriculture and Crop Sciences. University of Tabriz, Tabriz, Iran. Vol 6 (7). P. 396–402. [in English].
4. Martelli, R.(2015). Image Analysis Implementation for Evaluation of External Potato Damage. Applied Mathematical Sciences. Vol. 9. №. 81. P. 4029–4041. [in English].
5. Tavakoli, M., Mohsen N. (2015). Application of the Image Processing Technique for Sepa-rating Sprouted Potatoes in the Sorting Line. Journal of Applied Environmental and Biological Sciences. Vol. 4(11S). P. 223–227. [in English].
6. Barnes, M., Cielniak G., Tom D.(2006). Minimalist AdaBoost for blemish identification in potatoes. UK Journal of Food Engineering. Vol. 78. P.597–605. [in English].
7. Donggang, Hu. (2012). Potato shape detection based on stable direct least square method of ellipses fitting and it’s application prospect in Land Science. International Journal of Digital Content Technology and its Applications. 2012. Vol. 6. P. 161–171. [in English].
8. Ahmed, M. Rady, Daniel E. Guyer. (2015). Rapid and/or nondestructive quality evaluation methods for potatoes. Computers and electronics in agriculture. P. 31–48. [in English].
9. Fang, Tian, Yankun P., Wensong W. (2016). Nondestructive and rapid detection of potato black heart based on machine vision technology. Sensing for Agriculture and Food Quality and Safety. VIII. V(2). China, P. 83–94. [in English].
10. Elbatawi, I.E. (2008). An acoustic impact method to detect hollow heart of potato tubers. Biosystems engineering. Giza University. Egypt, P. 206–213. [in English].
11. Kartofel' svezhiy dlya pererabotki na produkty pitaniya. Tekhnicheskiye usloviya [Fresh potatoes for processing into food. Specifications]. (2010). HOST 26832-86-2010 from 06 January 1987. Moscow : Standartinform [in Russian].
12. Prokopovich, G.A. (2016). Razrabotka sistemy tekhnicheskogo zreniya dlya servisnogo mobil'nogo robota [Development of a vision system for a service mobile robot]. Tretiy vserossiyskiy nauchno-prakticheskiy seminar «Bespilotnyye transportnyye sredstva s elementami iskusstvennogo intellekta», Inno-polis, Respublika Tatarstan, 22–23 sentyabrya 2015 g. / Un-t Innopolis, redkol.: V.Ye. Pavlovskiy [et al.]. Innopolis, 2016. S. 127–136. [in Russian].
13. Kortylewski, A. [et al.] (2018). Training deep face recognition systems with synthetic data. Cornell University Library. 2018. URL: https://arxiv.org/pdf/1802.05891.pdf. [in English].
14. Chigorin, A. & Moiseyev, B. (2012). Klassifikatsiya avtodorozhnykh znakov na osnove svortochnoy neyroseti, obuchennoy na sinteticheskikh dannykh [Classification of road signs based on a convolutional neural network trained on synthetic data]. The 22nd International Conference on Computer Graphics and Vision. Moskva, Rossiya, 1–5 oktyabrya 2012 g. S. 284–287. [in Russian].
Citations
- High speed potato grading and quality inspection based on a color vision system / J.C. Noordam, G.W. Otten, A.J.M. Timmermans, B.H. van Zwol. Control Systems. 2017. P. 15–24.
- Совершенствование технологического процесса сортировки клубнейкартофеля по цветовой информации / Н.В. Бышов и др. Научный журнал КубГАУ. № 89 (05). 2013. С. 1–12.
- Golmohammadi, A., Bejaei F., Behfar H. Design, Development and Evaluation of an Online Potato Sorting System Using Machine Vision. International Journal of Agriculture and Crop Sciences. University of Tabriz, Tabriz, Iran. Vol 6 (7). P. 396–402.
- Martelli, R. Image Analysis Implementation for Evaluation of External Potato Damage. Applied Mathematical Sciences. Vol. 9. 2015. №. 81. P. 4029–4041.
- Tavakoli, M., Mohsen N. Application of the Image Processing Technique for Sepa-rating Sprouted Potatoes in the Sorting Line. Journal of Applied Environmental and Biological Sciences. 2015. Vol. 4(11S). P. 223–227.
- Barnes, M., Cielniak G., Tom D. Minimalist AdaBoost for blemish identification in potatoes. UK Journal of Food Engineering. 2006. Vol. 78. P. 597–605.
- Donggang, Hu. Potato shape detection based on stable direct least square method of ellipses fitting and it’s application prospect in Land Science. International Journal of Digital Content Technology and its Applications. 2012. Vol. 6. P. 161–171.
- Ahmed, M. Rady, Daniel E. Guyer. Rapid and/or nondestructive quality evaluation methods for potatoes. Computers and electronics in agriculture. 2015. P. 31–48.
- Fang, Tian, Yankun P., Wensong W. Nondestructive and rapid detection of potato black heart based on machine vision technology. Sensing for Agriculture and Food Quality and Safety. VIII. V(2). China, 2016. P. 83–94.
- Elbatawi, I.E. An acoustic impact method to detect hollow heart of potato tubers. Biosystems engineering. – Giza University. Egypt, 2008. P. 206–213.
- Картофель свежий для переработки на продукты питания. Технические условия : ГОСТ 26832-86-2010. Введ. 06.01.1987. М. : Стандартинформ, 2010. 5 с.
- Прокопович, Г.А. Разработка системы технического зрения для сервисного мобильного робота. Третий всероссийский научно-практический семинар «Беспилотные транспортные средства с элементами искусственного интеллекта», Иннополис, Республика Татарстан, 22–23 сентября 2015 г. / Ун-т Иннополис, редкол.: В.Е. Павловский [и др.]. Иннополис, 2016. С. 127–136.
- Training deep face recognition systems with synthetic data / A. Kortylewski [et al.]. Cornell University Library [Электронный ресурс]. 2018. URL: https://arxiv.org/pdf/1802.05891.pdf. (дата обращения: 16.04.2018).
- Чигорин, А., Моисеев Б. Классификация автодорожных знаков на основе свёрточной нейросети, обученной на синтетических данных. The 22nd International Conference on Computer Graphics and Vision. Москва, Россия, 1–5 октября 2012 г. С. 284–287.
Copyright (c) 2020 Volodimir Vetokhin, Viktor Goldyban, M. Kurylovich
Method and Device for Automatic Recognition of Unconditional Potato Tubes
About the Authors
Volodimir Vetokhin, Associate Professor, Doctor in Technics (Doctor of Technics Sciences), Poltava State Agrarian Academy, Poltava, Ukrain, e-mail: veto.vladim@gmail.com
Viktor Goldyban, PhD in Technics (Candidate of Technics Sciences), RUE «SPC NAS of Belarus for Agriculture Mechanization», Minsk, Republic of Belarus, e-mail: labpotato@mail.ru, ORCID ID: 0000-0002-5332-926X
M. Kurylovich, RUE «SPC NAS of Belarus for Agriculture Mechanization», Minsk, Republic of Belarus, ORCID ID: 0000-0003-1067-1310
Abstract
Keywords
Full Text:
PDFReferences
1. Noordam, J.C., Otten G.W., Timmermans, A.J.M., van Zwol, B.H. (2017). High speed potato grading and quality inspection based on a color vision system. Control Systems. 2017. P. 15–24. [in English].
2. Byshov N.V. et al. (2013). Sovershenstvovaniye tekhnologicheskogo protsessa sortirovki klubney kartofelya po tsvetovoy informatsii [Improvement of the technological process of sorting potato tubers by color information]. Nauchnyy zhurnal KubGAU – Scientific journal of KubSAU, № 89 (05), 1–12. [in Russian].
3. Golmohammadi, A., Bejaei F., Behfar H. (2013). Design, Development and Evaluation of an Online Potato Sorting System Using Machine Vision. International Journal of Agriculture and Crop Sciences. University of Tabriz, Tabriz, Iran. Vol 6 (7). P. 396–402. [in English].
4. Martelli, R.(2015). Image Analysis Implementation for Evaluation of External Potato Damage. Applied Mathematical Sciences. Vol. 9. №. 81. P. 4029–4041. [in English].
5. Tavakoli, M., Mohsen N. (2015). Application of the Image Processing Technique for Sepa-rating Sprouted Potatoes in the Sorting Line. Journal of Applied Environmental and Biological Sciences. Vol. 4(11S). P. 223–227. [in English].
6. Barnes, M., Cielniak G., Tom D.(2006). Minimalist AdaBoost for blemish identification in potatoes. UK Journal of Food Engineering. Vol. 78. P.597–605. [in English].
7. Donggang, Hu. (2012). Potato shape detection based on stable direct least square method of ellipses fitting and it’s application prospect in Land Science. International Journal of Digital Content Technology and its Applications. 2012. Vol. 6. P. 161–171. [in English].
8. Ahmed, M. Rady, Daniel E. Guyer. (2015). Rapid and/or nondestructive quality evaluation methods for potatoes. Computers and electronics in agriculture. P. 31–48. [in English].
9. Fang, Tian, Yankun P., Wensong W. (2016). Nondestructive and rapid detection of potato black heart based on machine vision technology. Sensing for Agriculture and Food Quality and Safety. VIII. V(2). China, P. 83–94. [in English].
10. Elbatawi, I.E. (2008). An acoustic impact method to detect hollow heart of potato tubers. Biosystems engineering. Giza University. Egypt, P. 206–213. [in English].
11. Kartofel' svezhiy dlya pererabotki na produkty pitaniya. Tekhnicheskiye usloviya [Fresh potatoes for processing into food. Specifications]. (2010). HOST 26832-86-2010 from 06 January 1987. Moscow : Standartinform [in Russian].
12. Prokopovich, G.A. (2016). Razrabotka sistemy tekhnicheskogo zreniya dlya servisnogo mobil'nogo robota [Development of a vision system for a service mobile robot]. Tretiy vserossiyskiy nauchno-prakticheskiy seminar «Bespilotnyye transportnyye sredstva s elementami iskusstvennogo intellekta», Inno-polis, Respublika Tatarstan, 22–23 sentyabrya 2015 g. / Un-t Innopolis, redkol.: V.Ye. Pavlovskiy [et al.]. Innopolis, 2016. S. 127–136. [in Russian].
13. Kortylewski, A. [et al.] (2018). Training deep face recognition systems with synthetic data. Cornell University Library. 2018. URL: https://arxiv.org/pdf/1802.05891.pdf. [in English].
14. Chigorin, A. & Moiseyev, B. (2012). Klassifikatsiya avtodorozhnykh znakov na osnove svortochnoy neyroseti, obuchennoy na sinteticheskikh dannykh [Classification of road signs based on a convolutional neural network trained on synthetic data]. The 22nd International Conference on Computer Graphics and Vision. Moskva, Rossiya, 1–5 oktyabrya 2012 g. S. 284–287. [in Russian].
Citations
- High speed potato grading and quality inspection based on a color vision system / J.C. Noordam, G.W. Otten, A.J.M. Timmermans, B.H. van Zwol. Control Systems. 2017. P. 15–24.
- Совершенствование технологического процесса сортировки клубнейкартофеля по цветовой информации / Н.В. Бышов и др. Научный журнал КубГАУ. № 89 (05). 2013. С. 1–12.
- Golmohammadi, A., Bejaei F., Behfar H. Design, Development and Evaluation of an Online Potato Sorting System Using Machine Vision. International Journal of Agriculture and Crop Sciences. University of Tabriz, Tabriz, Iran. Vol 6 (7). P. 396–402.
- Martelli, R. Image Analysis Implementation for Evaluation of External Potato Damage. Applied Mathematical Sciences. Vol. 9. 2015. №. 81. P. 4029–4041.
- Tavakoli, M., Mohsen N. Application of the Image Processing Technique for Sepa-rating Sprouted Potatoes in the Sorting Line. Journal of Applied Environmental and Biological Sciences. 2015. Vol. 4(11S). P. 223–227.
- Barnes, M., Cielniak G., Tom D. Minimalist AdaBoost for blemish identification in potatoes. UK Journal of Food Engineering. 2006. Vol. 78. P. 597–605.
- Donggang, Hu. Potato shape detection based on stable direct least square method of ellipses fitting and it’s application prospect in Land Science. International Journal of Digital Content Technology and its Applications. 2012. Vol. 6. P. 161–171.
- Ahmed, M. Rady, Daniel E. Guyer. Rapid and/or nondestructive quality evaluation methods for potatoes. Computers and electronics in agriculture. 2015. P. 31–48.
- Fang, Tian, Yankun P., Wensong W. Nondestructive and rapid detection of potato black heart based on machine vision technology. Sensing for Agriculture and Food Quality and Safety. VIII. V(2). China, 2016. P. 83–94.
- Elbatawi, I.E. An acoustic impact method to detect hollow heart of potato tubers. Biosystems engineering. – Giza University. Egypt, 2008. P. 206–213.
- Картофель свежий для переработки на продукты питания. Технические условия : ГОСТ 26832-86-2010. Введ. 06.01.1987. М. : Стандартинформ, 2010. 5 с.
- Прокопович, Г.А. Разработка системы технического зрения для сервисного мобильного робота. Третий всероссийский научно-практический семинар «Беспилотные транспортные средства с элементами искусственного интеллекта», Иннополис, Республика Татарстан, 22–23 сентября 2015 г. / Ун-т Иннополис, редкол.: В.Е. Павловский [и др.]. Иннополис, 2016. С. 127–136.
- Training deep face recognition systems with synthetic data / A. Kortylewski [et al.]. Cornell University Library [Электронный ресурс]. 2018. URL: https://arxiv.org/pdf/1802.05891.pdf. (дата обращения: 16.04.2018).
- Чигорин, А., Моисеев Б. Классификация автодорожных знаков на основе свёрточной нейросети, обученной на синтетических данных. The 22nd International Conference on Computer Graphics and Vision. Москва, Россия, 1–5 октября 2012 г. С. 284–287.