Justification of innovative technology for variety and phytocleaning in breeding and seed plantings of potatoes and vegetable crops

The purpose of the research is to substantiate theoretically the process of variety and phytocleaning in breeding and seed plantings of potatoes and vegetable crops using machine vision technologies and robotic elements. The article analyzes modern non-destructive methods for detecting diseases of biological objects; technological processes and machines for removing the fruits of vegetable crops from plants in a digital agricultural production system with elements of robotization in the operations of caring for plants and collecting marketable products. The relevance of developing innovative technology and technical means for removing infected potato and vegetable plants in breeding and seed production has been established. To carry out health-improving techniques for the production of vegetable and potato seeds, an innovative technology and machine have been developed for removing infected potato and vegetable crop plants in breeding and seed-growing plantings, providing movement across the field using machine vision technologies with the identification of an infected plant or a plant that does not correspond to the varietal characteristics with its subsequent removal. In the process of the research (2021-2022), a morphological matrix for selecting technical means of using functioning elements for implementing innovative technology for varietal and phytocleaning of vegetable crops and potatoes, as well as the theoretical foundations of innovative technology for removing contaminated biological objects, were developed. An indicator of the effectiveness of the implementation of innovative phytotype cleaning technology has been identified, taking into account the parameters of economic and agrotechnical indicators, as well as metal intensity, energy intensity, environmental friendliness and reliability. Analytical studies of machine technology and technical means for removing infected vegetable and potato plants are presented. A substantiation of the innovative technology for varietal and phytocleaning of vegetable crops and potatoes has been carried out, in terms of the exclusion of an unmanned aerial vehicle in the technology for detecting infected potato plants with a qualitative assessment of the feasibility of choosing technical means when using the functioning elements of the implementation of the developed technology according to the criteria of economic and agrotechnical assessment, as well as metal intensity, energy intensity and reliability . An assessment of the feasibility of choosing technical means for the functioning of elements of innovative technology showed that, according to a set of criteria, the process of varietal and phytocleaning of vegetable crops and potatoes is advisable to carry out without the use of an unmanned aerial vehicle, using an optical system for identifying infected plants in the design of the machine.

1. Petukhov S. N. The state of technical and technological support of breeding and original potato seed production. Agrotekhnika i energoobespechenie. 2018;(4):76‒84. (In Russ.). URL: https://www.elibrary.ru/item.asp?id=36852656

2. 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/mexanizaciya/selekcii-i-semenovodstvu-kartofelya-neobxodima-mexanizaciya.html

3. Lobachevskiy Ya. P., Dorokhov A. S., Sibirev A. V. The current state of technological support for vegetable crops production in the Russian Federation. Ovoshchi Rossii = Vegetable crops of Russia. 2023;(5):5‒10. (In Russ.). DOI: https://doi.org/10.18619/2072-9146-2023-5-5-17

4. Krasnoshchekov N. V. Agroengineering strategy: from mechanization of agriculture to its intellectualization! Traktory i sel'khozmashiny. 2010;77(8):5‒8. (In Russ.). URL: https://journals.eco-vector.com/0321-4443/article/view/68902/ru_RU#!

5. Simakov E. A., Anisimov B. V., Korshunov A. V., Durkin M. L. About development of conception of the original, elite and reproductive potato seeds breeding in Russia. Kartofel' i ovoshchi = Potato and Vegetables. 2005;(2):2‒5. (In Russ.). URL: http://potatoveg.ru/wp-content/uploads/2013/03/kio_2_2005v.pdf

6. Fedorenko V. F., Mishurov N. P., Nemenushchaya L. A. Analysis of the state and prospects of development of breeding and seed production of vegetable crops: scientific and analytical overview. Mosow: Rosinformagrotekh, 2019. 96 p. URL: https://rosinformagrotech.ru/data/elektronnye-kopii-izdanij/rastenievodstvo/send/5-rastenievodstvo/1396-analizsostoyaniya-i-perspektivy-razvitiya-selektsii-i-semenovodstva-ovoshchnykh-kultur-2019

7. Lobachevskiy Ya. P., Tsench Yu. S. Principles of forming machine and technology systems for integrated mechanization and automation of technological processes in crop production. Sel'skokhozyaystvennye 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

8. 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 oborudovanie dlya sela = Machinery and Equipment for Rural Area. 2018;(7):2‒7. (In Russ.).

9. Lobachevskiy Ya. P., Dorokhov A. S. Digital technologies and robotic devices in the agriculture. Sel'skokhozyaystvennye mashiny i tekhnologii = Agricultural Machinery and Technologies. 2021;15(4):6‒10. (In Russ.). DOI: https://doi.org/10.22314/2073-7599-2021-15-4-6-10

10. Lobachevskiy Ya. P., Beylis V. M., Tsench Yu. S. Digitization aspects of the system of technologies and machines. Elektrotekhnologii i elektrooborudovanie v APK. 2019;(3(36)):40‒45. (In Russ.). URL: https://www.elibrary.ru/item.asp?id=41192528

11. Van Henten E. J., Van Tuijl B. A. J., Hemming J., Kornet J. G., Bontsema J., Van Os E. A. Field test of an autonomous cucumber picking robot. Biosystems Engineering. 2003;86(3):305‒313. DOI: https://doi.org/10.1016/J.BIOSYSTEMSENG.2003.08.002

12. Ji C., Feng Q., Yuan T., Li W. Development and performance analysis on cucumber harvesting robot system in greenhouse. Robot. 2011;33(6):726‒730. DOI: https://doi.org/10.3724/SP.J.1218.2011.00726

13. Tarrio P., Bernardos A. M., Casar J. R., Besada J. A. A harvesting robot for small fruit in bunches based on 3-D stereoscopic vision. Computers in Agriculture and Natural Resources, 4th World Congress Conference, Florida, 2006. pp. 270‒275. URL: https://elibrary.asabe.org/abstract.asp?JID=1&AID=21885&CID=canr2006&T=1

14. Feng Q., Wang X., Zheng W., Qiu Q., Jiang K. A new strawberry harvesting robot for elevated-trough culture. International Journal of Agricultural and Biological Engeneering. 2012;5(2):1‒8. URL: https://www.ijabe.org/index.php/ijabe/article/view/495

15. Dorokhov A. S., Aksenov A. G., Sibirev A. V., Mosyakov M. A. Analytical studies of machine-technological complexes for variety-phytocleaning of potato and vegetable crops in breeding and seed production. Agrarnyy nauchnyy zhurnal = The Agrarian Scientific Journal. 2022;(4):76‒82. (In Russ.). DOI: https://doi.org/10.28983/asj.y2022i4pp76-82

16. Feng Q. C., Zou W., Fan P. F., Zhang C. F., Wang X. Design and test of robotic harvesting system for cherry tomato. International Journal of Agricultural and Biological Engeneering. 2018;11(1):96‒100. DOI: https://doi.org/10.25165/j.ijabe.20181101.2853

17. Jin Х., Jie L., Wang Sh., Qi H. J., Li Sh. W. Classifying wheat hyperspectral pixels of healthy heads and fusarium head blight disease using a deep neural network in the wild field. Remote Sens. 2018;10(3):395. DOI: https://doi.org/10.3390/rs10030395

18. Militante S., Gerardo B., Medina R. Sugarcane Disease Recognition using Deep Learning. IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE), 2019. рp. 575‒578. DOI: https://doi.org/10.1109/ECICE47484.2019.8942690

19. Gold K. M., Townsend P. A., Chlus A., Herrmann I., Couture J. J., Larson E. R., Gevens A. J. Hyperspectral Measurements Enable Pre-Symptomatic Detection and Differentiation of Contrasting Physiological Effects of Late Blight and Early Blight in Potato. Remote Sensing. 2020;12(2):286. DOI: https://doi.org/10.3390/rs12020286

20. Zhang W., Zhu Q., Huang M., Guo Ya, Qin J. Detection and Classification of Potato Defects Using Multispectral Imaging System Based on Single Shot Method. Food Analytical Methods. 2019;12:2920‒2929. DOI: https://doi.org/10.1007/s12161-019-01654

21. Solovchenko A. E., Shurygin B. M., Kuzin A. I., Solovchenko O. V., Krylov A. S. Extraction of Quantitative Information from Hyperspectral Reflectance Images for Noninvasive Plant Phenotyping. Russian Journal of Plant Physiology. 2022;69:144. DOI: https://doi.org/10.1134/S1021443722601148