Development of an Automated Management System for Agricultural Technologies in Horticulture

The implementation of intelligent technologies in industrial horticulture is possible with the help of an automated system for managing production processes. (Research purpose) To develop and substantiate the parameters of an automated management system for agricultural technologies in horticulture with the ability to conduct land inspections using a mobile application. (Materials and methods) ADO.NET driver Npqsql was used for work with the database. Dapper was used as Object Relational Mapping. The web application used the Model View Controller design pattern, and Bootstrap as the css framework. Data visualization from the database was carried out using cloud technology, placing the site using a set of Internet Information Services. Jquery (a set of JavaScript functions) served as the main framework for working with the client-side of the program code. The authors also used the PostgreSql database management system. The mobile application was created in the Android studio integrated environment. (Results and discussion) The authors developed an automated system for managing agricultural technologies. They formed the structure of the hardware and software base. They created the system ability to operate in a dialogue mode with the user through forms, based on the algorithm for choosing the optimal options for technological processes in the horticultural products production. A mobile application was implemented to conduct digital land inspections. They determined the procedure for conducting land inspections by agronomists using a mobile application. (Conclusions) The authors developed a system for the automated technologies formation and management in horticulture, which provided operational processing of information flows in real time, reflecting the characteristics of the plants’ growth and state in critical phases of development. They provided modern recording devices and a mobile application operation. They showed that the system automatically optimized machine technologies for the cultivation of horticultural crops according to biological (realization of the potential biological productivity of crops) and economic (increasing the efficiency of using production resources) criteria.

1. Fountas S., Sorensen C.G., Tsiropoulos Z., Cavalaris C., Liakos V., Gemtos T. Farm machinery management information system. Computers and electronics in agriculture. 2015. Vol. 110. 131-138 (In English).

2. Kaloxylos A., Groumas A., Sarris V., Katsikas L., Magdalinos P., Antoniou E., Politopoulou Z., Wolfert S., Brewster C., Eigenmann R., Maestre Terol C. Acloud-based farm management system: architecture and implementation. Computers and electronics in agriculture. 2014. Vol. 100. 168-179 (In English).

3. Kaivosoja J., Jackenkroll M., Linkolehto R., Weis M., Gerhards R. Automaticcontrol of farming operations based on spatial web services. Computers and electronics in agriculture. 2014. Vol. 100. 110-115 (In English).

4. Paraforos D. S., Vassiliadis V., Kortenbruck D., Stamkopoulos K., Ziogas V., Sapounas A. A., Griepentrog H.W. Multi-level automation of farm management information systems. Computers and Electronics in Agriculture. 2017. Vol. 142. 504-514 (In English).

5. Ampatzidis Y., Tan L., Haley R., Whiting M.D. Cloud-based harvest managementinformation system for hand-harvested specialty crops. Computers and electronics in agriculture. 2016. Vol. 122. 161-167 (In English).

6. Wolfert S., Ge L., Verdouw C., Bogaardt M.-J. Big data in smart farming – areview. Agricultural systems. 2017. Vol. 153. 69-80 (In English).

7. Blank S., Bartolein C., Meyer A., Ostermeier R., Rostanin O. IGreen: a ubiquitous dynamic network to enable manufacturer independent data exchange in futureprecision farming. Computers and electronics in agriculture. 2013, Vol. 98. 109116 (In English).

8. Paraforos D.S., Vassiliadis V., Kortenbruck D., Stamkopoulos K., Ziogas V., Sapounas A.A., Griepentrog H.W. A farm management information system using future internet technologies. IFAC-PapersOnLine. 2016. Vol. 49. 324-329 (In English).

9. Artyushin A., Smirnov I.G., Khort D.O., Filippov R.A. Osobennosti razrabotki intellektual'noy sistemy upravleniya v sadovodstve [Features of the development of an intelligent control system in horticulture]. Vestnik Michurinskogo gosudarstvennogo agrarnogo universiteta. 2016. N2. 148-153 (In Russian).

10. Cymbal A.A., Khort D.O. Osvoenie printsipov programmirovaniya urozhaya pri avtomatizirovannom proektirovanii agrotehnologiy vozdelyvaniya chernoy smorodiny [Mastering the principles of programming the crop in the automated design of agricultural technologies for the cultivation of black currant]. Vestnik FGOU VPO MGAU imeni V.P. Goryachkina. 2013. N1(57). 27-29 (In Russian).

11. Khort D.O., Filippov R.A. Osobennosti funktsionirova­niya sistemy avtomatizirovannogo upravleniya produktsionnymi processami (ASUPP) v sadovodstve [Features of the functioning of the automated control system for production processes (ACSUP) in horticulture]. Innovatsii v sel'skom khozyaystve. 2013. N2(4). 70-74 (In Russian).

12. Zubina V.A., Kutyrev A.I. Development of a software package for the tractor fleet formation in agricultural organizations. MATEC Web of Conferences (ICMTMTE 2019). 2019. N00102 (In English).

13. Valge A.M., Papushin E.A., Pakskina E.G. Ispol'zovanie informatsionnyh tehnologiy pri proektirovanii processov proizvodstva produktsii rastenievodstva [The use of information technology in the design of crop production processes]. Mehanizatsiya i elektrifikatsiya sel'skogo khozyaystva. 2012. N3. 17-18 (In Russian).

14. Zykov A.V., Yunin V.A., Zaharov A.M. Model' optimizatsii sostava mashinno-traktornogo parka na osnove primene­niya adaptivnykh tehnologiy proizvodstva sel'skohozyaystvennoy produktsii v usloviyakh severo-zapadnogo regiona RF [A model for optimizing the composition of the machine and tractor fleet based on the use of adaptive technologies for the production of agricultural products in the northwestern region of the Russian Federation]. Internationalresearch journal. 2018. N11. 47-51 (In Russian).

15. Lichman G.I., Smirnov I.G., Belenkov A.I. Ispol'zovanie mobil'nogo telefona v sistemakh tochnogo zemledeliya [Using a mobile phone in precision farming systems]. Nivy Rossii. 2017. N2(146). 58-61 (In Russian).

16. Milrad M., Spikol D. Anytime, anywhere learning supported by smart phones: experiences and results from the musis project. Educational Technology and Society. 2007. Vol. 10. N4. 6270 (In English).

17. Chaovalit P., Saiprasert C., Pholprasit T. A method for driving event detection using sax with resource usage exploration on smartphone platform. Eurasip Journal on Wireless Communications and Networking. 2014. N1(135) (In English).

18. Palanisamy S., Selvaraj R., Ramesh T., Ramesh T., Ponnusamy J., Ponnusamy J. Applications of Smartphone-Based Sensors in Agriculture: A Systematic Review of Research. Journal of Sensors. 2015. Article ID 195308. 18 pages (In English).

19. Mosa A.S.M., Yoo I., Sheets L. A systematic review of healthcare applications for smartphones. BMC Medical Informatics and Decision Making. 2012. Vol. 12. N1(67) (In English).

20. Habib M.A., Mohktar M. S., Kamaruzzaman S.B., Lim K.S., Pin T.M., Ibrahim F. Smartphone-based solutions for fall detection and prevention: challenges and open issues. Sensors. 2014. Vol. 14, N4. 7181-7208 (In English).