Mathematical modeling of the operation of аn automated machine for cleaning potato tubers

The aim of the study is to develop a mathematical model of a separation device for an automated machine designed to clean potato tubers from soil, with the goal of preventing the removal of the fertile soil layer from the field and the possible further spread of potato plant diseases through contaminated soil. The process of interaction between the rod of the separating conveyor and the soil lump is considered. Graphs of the change in kinetic energy for soil lumps of different masses, depending on the rotation frequency of the slatted conveyor drive, are constructed. During the modeling process in the Mathcad environment, the mass of lumps was determined with a variation from 0.2 to 0.8 kg; the unloading height of the tuber nest was 0.3 m; the mass of the slatted conveyor was 23 kg; the radius of the drive shaft was 0.1 m; the angle of inclination of the conveyor relative to the horizon was 0.3491 rad. A graphical dependence of the change in kinetic energy loss on the rotation frequency of the slatted conveyor drive for lumps of different masses was determined. It was found that with an increase in the rotation frequency of the slatted conveyor drive, an increase in kinetic energy loss as a result of impact action is observed, while with an increase in the mass of the soil lump, a linear increase in this indicator is observed. The loss of kinetic energy as a result of the impact interaction of the rod of the separating conveyor and the soil lump will be equal to the work spent on the destruction of the soil lump as a result of falling onto the surface of the slat conveyor. It was determined that the mass entering the slat conveyor includes tops, potato tubers and soil. In turn, the quality of separation of the incoming mass will be determined by the distance between the rods and soil particles of a certain size. The results of the conducted modeling established that an increase in the efficiency of the cleaning process is observed when varying the frequency of oscillations of the web in the range of 45–60 Hz and a constant amplitude value of 0.03 m.

1. Lobachevskiy Ya. P., Beylis V. M., Tsench Yu. S. Aspekty tsifrovizatsii sistemy tekhnologiy i mashin. Elektrotekhnologii i elektrooborudovaniye v APK. 2019;(3):40–45. (In Russ). URL: https://elibrary.ru/item.asp?id=41192528

2. Izmaylov A. Yu., Kolchin N. N., Lobachevskiy Ya. P., Kynev N. G. Modern technologies and special equipment for potato production. Sel'skokhozyaystvennyye mashiny i tekhnologii = Agricultural Machinery and Technologies. 2015;(3):43–47. (In Russ.). URL: https://www.vimsmit.com/jour/article/view/76/32

3. Lachuga Yu. F., Izmaylov A. Yu., Lobachevskiy Ya. P., Shogenov Yu. Kh. Intensive machine technologies, robotyzed equipment and digital systems for production of main groups of agricultural products. Tekhnika i oborudovaniye dlya sela = Machinery and Equipment for Rural Area. 2018;(7):2–7. (In Russ.). URL: https://elibrary.ru/item.asp?id=35303139

4. Ponomarev A. G., Kolchin N. N., Zernov V. N., Petukhov S. N. Mechanization is necessary for breeding and seed growing of potato. Kartofel' i ovoshchi = Potato and Vegetables. 2017;(3):22–24. (In Russ.). URL: http://potatoveg.ru/wp-content/uploads/2018/04/3_2017.pdf

5. Laryushin N. P., Kukharev O. N., Kiryukhina T. A. Original position when designing onion harvesting machines. Nauka v tsentral'noy Rossii = Science in Central Russia. 2015;(6(18)):48–58. (In Russ.). URL: https://elibrary.ru/item.asp?id=25118239

6. Lobachevskiy Ya. P., Tsench Yu. S. Principles of Forming Machine and Technology Systems for Integrated Mech-anization and Automation of Technological Processes in Crop Production. Sel'skokhozyaystvennyye mashiny i tekhnologii = Agricultural Machinery and Technologies. 2022;16(4):4-12. (In Russ.). DOI: https://doi.org/10.22314/2073-7599-2022-16-4-4-12

7. Truflyak E. V., Kurchenko N. Yu., Daybova L. A., Kreymer A. S., Podushin Yu. V., Belaya E. M. Monitoring and forecasting of scientific and technological development of agro-industrial complex in the field of precision agriculture, automation and robotics. Scientific support of the agro-industrial complex: collection of articles on the materials of the 72nd scientific and practical conference of teachers. Krasnodar: KubGAU im. I. T. Trubilina, 2017. pp. 335–336. URL: https://elibrary.ru/item.asp?id=29740008

8. Dongre A. U., Battase R., Dudhale S., Patil V. R., Chavan D. Development of Potato Harvesting Model. International Research Journal of Engineering and Technology (IRJET). 2017;4(10):1567–1570. URL: https://www.irjet.net/archives/V4/i10/IRJET-V4I10288.pdf

9. Lü J. Q., Shang Q. Q., Yang Y., Li Zh. Design optimization and experiment on potato haulm cutter. Transactions of the CSAM. 2016;47(5):106–114. DOI: https://doi.org/10.6041/j.issn.1000-1298.2016.05.015

10. Azizi P., Dehkordi N. S., Farhadi R. Design, Construction and Evaluation of Potato Digger with Rotary Blade. Cercetari Agronomice in Moldova. 2014;47(3):5–13. DOI: https://doi.org/10.2478/cerce-2014-0022

11. Abd El-Rahman M. M. A. Development and Performance Evaluation of a Simple Grading Machine Suitable for Onion Sets. Journal of Soil Sciences and Agricultural Engineering. 2014;2(2):213–226. DOI: https://doi.org/10.21608/jssae.2011.55418

12. Dandekar I., Joshi A., Patil V. Review Paper Based on Design and Development of an Onion Harvesting Machine. Journal of Information and Computational Science. 2020;9(12):333–337. URL: https://www.researchgate.net/publication/339201506

13. Jothi Shanmugam C., Senthilkumar G. Indigenous Development of Low Cost Harvesting Machine. ARPN Journal of Engineering and Applied Sciences. 2017;12(5):4489–4490. URL: http://www.arpnjournals.org/jeas/research_papers/rp_2017/jeas_0817_6236.pdf

14. Bachche S. Deliberation on Design Strategies of Automatic Harvesting Systems: A Survey. Robotics. 2015;4(2):194–222. DOI: https://doi.org/10.3390/robotics4020194

15. Sazonov N. V., Mosyakov M. A., Teterin V. S., Panferov N. S., Godyaeva M. M., Trunov M. S. Quality Metrics of Automated Machinery in Potato Plant Cultivation for Breeding and Seed Production. Sel'skokhozyaystvennyye mashiny i tekhnologii = Agricultural Machinery and Technologies. 2024;18(1):60–67. (In Russ.). DOI: https://doi.org/10.22314/2073-7599-2024-18-1-60-67

16. Smirnov I. G., Khort D. O., Kutyrev A. I. Intelligent Technologies and Robotic Machines for Garden Crops Cultivation. Sel'skokhozyaystvennyye mashiny i tekhnologii = Agricultural Machinery and Technologies. 2021;15(4):35–41. (In Russ.). DOI: https://doi.org/10.22314/2073-7599-2021-15-4-35-41

17. Kazakevich P. P., Yurin A. N., Prokopovich G. A. Technical vision system for apple defects recognition: justification, development, testing. Vestsі Natsyyanal’nay akademіі navuk Belarusі. Seryya agrarnykh navuk = Proceedings of the National Academy of Sciences of Belarus. Agrarian series. 2021;59(4):488–500. (In Belarus). DOI: https://doi.org/10.29235/1817-7204-2021-59-4-488-500

18. Lobachevskiy Ya. P., Lachuga Yu. F., Izmaylov A. Yu., Shogenov Yu. Kh. Scientific and technical achievements of agricultural engineering organizations in the context of digital transformation of agriculture. Tekhnika i oborudovaniye dlya sela = Machinery and Equipment for Rural Area. 2023;(4(310)):2–5. (In Russ.). DOI: https://doi.org/10.33267/2072-9642-2023-4-2-5

19. Ganganagowdar N. V., Gundad A. V. An intelligent computer vision system for vegetables and fruits quality inspection using soft computing techniques. Agricultural Engineering International. 2019;21(3):171–178.

20. Vorobyov N. I., Lysov A. K., Kornilov T. V., Hyutti A. V. Computational neural network for processing light-reflective spectra of plants and remote phytosanitary monitoring of potatoes. Agrarnaya nauka Evro-Severo-Vostoka = Agricultural Science Euro-North-East. 2024;25(2):283–292. (In Russ.). DOI: https://doi.org/10.30766/2072-9081.2024.25.2.283-292

21. Ibyatov R. I., Ziganshin B. G. On simulation of random processes in agricultural complex. Vestnik Kazanskogo gosudarstvennogo agrarnogo universiteta = Vestnik of the Kazan State Agrarian University. 2022;17(1(65)):50–55. (In Russ.). DOI: https://doi.org/10.12737/2073-0462-2022-50-55

22. Golikov A. A., Kostenko M. Yu., Rembalovich G. K., Uspenskiy I. A. Mathematical model of probabilistic assessment of the onset of technological failure of a potato harvester. Politematicheskiy setevoy elektronnyy nauchnyy zhurnal Kubanskogo gosudarstvennogo agrarnogo universiteta = Polythematic online scientific journal of Kuban State Agrarian University. 2014;(99):244–255. (In Russ.). URL: https://elibrary.ru/item.asp?id=21695002