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Application Possibilities of Data Science Tools in Agriculture: A Review

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Advances in Artificial Systems for Medicine and Education VI (AIMEE 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 159))

Abstract

Digitization of agricultural production processes is a prerequisite for a successful strategy for the development of the agricultural sector. The process of implementing Data Science methods and algorithms into business processes has already begun and is gradually accelerating. However, it is far from clear how Data Science could make agricultural more effective and profitable. Availability of data on each agricultural object combined with well-developed algorithms and methods. Data Science allows to build mathematical models of the agricultural sector and farms, accurately calculate the algorithm of actions and predict the result. AgroTech, AgroFinTech, FoodTech and other businesses are actively developing in the technologically underdeveloped agricultural sector. This makes the agricultural sector attractive for investment and opens up new prospects for attracting financial resources. We show that Data Science tools in agricultural does not simply replace analogue technologies used in traditional agriculture. It offers new options for agriculture, including opportunities for more effective tools for agriculture producers and management. It allows for in-depth analysis and understanding of business processes, facilitates the structuring of problems, systematization of the agricultural sector as whole and individual farms. However, the use of Data Science tools is accompanied by certain risks, which also discussed in the paper.

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References

  1. Angon, P.B., Salehin, I., Khan, M.M.R., Mondal, S.: Cropland mapping expansion for production forecast: rainfall, relative humidity and temperature estimation. Int. J. Eng. Manuf. (IJEM) 11(5), 25–40 (2021). https://doi.org/10.5815/ijem.2021.05.03

    Article  Google Scholar 

  2. Kumar, K.A., Aju, D.: An Internet of Thing based Agribot (IOT- Agribot) for precision agriculture and farm monitoring. Int. J. Educ. Manage. Eng. (IJEME) 10(4), 33–39 (2020). https://doi.org/10.5815/ijeme.2020.04.04

    Article  Google Scholar 

  3. Arakeri, M.P., Arun, M., Padmini, R.K.: Analysis of late blight disease in tomato leaf using image processing techniques. Int. J. Eng. Manuf. (IJEM) 5(4), 12–22 (2015). https://doi.org/10.5815/ijem.2015.04.02

    Article  Google Scholar 

  4. Bhagawati, K., Bhagawati, R., Jini, D.: Intelligence and its application in agriculture: techniques to deal with variations and uncertainties. Int. J. Intell. Syst. Appl. (IJISA) 8(9), 56–61 (2016). https://doi.org/10.5815/ijisa.2016.09.07

    Article  Google Scholar 

  5. Klerkx, L., Jakku, E., Labarthe, P.: A review of social science on digital agriculture, smart farming and agriculture 4.0: new contributions and a future research agenda. NJAS: Wageningen J. Life Sci. 90–91(1), 1–16 (2019). https://doi.org/10.1016/j.njas.2019.100315

    Article  Google Scholar 

  6. Izonin, I., et al.: An extended-input GRNN and its application. Procedia Comput. Sci. 160, 578–583 (2019)

    Article  Google Scholar 

  7. Yang, J., Guo, X., Li, Y., Marinello, F., Ercisli, S., Zhang, Z.: A survey of few-shot learning in smart agriculture: developments, applications, and challenges. Plant Methods 18(1), 1–12 (2022)

    Article  Google Scholar 

  8. Starychenko, Y., Skrypnyk, A., Babenko, V., Klymenko, N., Tuzhyk, K.: Food security indices in Ukraine: forecast methods and trends. Estudios de Economia Aplicada, 38(4), 1–14 (2020)

    Google Scholar 

  9. Davydenko N., Dibrova, A., Onishko, S., Fedoryshyna, L.: The influence of the gross regional product on the formation of the financial potential of the region. J. Optim. Industr. 14(Special Issue), 153–157 (2021)

    Google Scholar 

  10. Dibrova, A., Davydenko, N., Nehoda, Y., Titenko, Z.: Financial provision for competitiveness of agricultural enterprises. Intellect. Econ. 14(1), 67–75 (2020)

    Google Scholar 

  11. Kaminskyi, A., Nehrey, M., Rizun, N.: The impact of COVID-induced shock on the risk-return correspondence of agricultural ETFs. In: 2020 Special Edition of International Conference on Monitoring, Modeling and Management of Emergent Economy, M3E2-MLPEED 2020, pp. 204–218 (2020)

    Google Scholar 

  12. Morota, G., Ventura, R.V., Silva, F.F., Koyama, M., Fernando, S.C.: Big data analytics and precision animal agriculture symposium: machine learning and data mining advance predictive big data analysis in precision animal agriculture1. J. Animal Sci. 96(4), 1540–1550 (2018). https://doi.org/10.1093/jas/sky014

    Article  Google Scholar 

  13. Himesh, S., Prakasa Rao, E.V.S., Gouda, K.C., Ramesh, K.V., Rakesh, V., Mohapatra, G.N.: Digital revolution and Big Data: a new revolution in agriculture. CAB Rev. 13(21), 1–7 (2018)

    Google Scholar 

  14. Coble, K.H., Mishra, A.K., Ferrell, S., Griffin, T.: Big data in agriculture: a challenge for the future. Appl. Econ. Perspect. Policy 40(1), 79–96 (2018)

    Article  Google Scholar 

  15. MayaGopal, P.S., Chintala, B.R.: Big data challenges and opportunities in agriculture. Int. J. Agric. Environ. Inf. Syst. 11(1), 48–66 (2020). https://doi.org/10.4018/IJAEIS.2020010103

    Article  Google Scholar 

  16. Sadiku, M.N., Ashaolu, T.J., Musa, S.M.: Big data in agriculture. Int. J. Sci. Adv. 1(1), 1–10 (2020)

    Google Scholar 

  17. Matviychuk, A., Novoseletskyy, O., Vashchaiev, S., Velykoivanenko, H., Zubenko, I.: Fractal analysis of the economic sustainability of industrial enterprise. In: CEUR Workshop Proceedings, pp. 2422: 455–466 (2019)

    Google Scholar 

  18. Oliskevych, M., Beregova, G., Tokarchuk, V.: Fuel consumption in Ukraine: evidence from vector error correction model. Int. J. Energy Econ. Policy 8(5), 58–63 (2018)

    Google Scholar 

  19. Hryhoruk, P., Khrushch, N., Grygoruk, S.: Assessing the investment capacity of the agricultural sector: case of Ukraine. In: 2020 10th International Conference on Advanced Computer Information Technologies, ACIT 2020 - Proceedings, pp. 183–187, 9208927 (2020)

    Google Scholar 

  20. Guryanova, L., Bolotova, O., Gvozdytskyi, V., Olena, S.: Long-term financial sustainability: an evaluation methodology with threats considerations. Rivista di Studi sulla Sostenibilita 1, 47–69 (2020)

    Article  Google Scholar 

  21. Shakhovska, N., Montenegro, S., Kryvenchuk, Y., Zakharchuk, M.: The neurocontroller for satellite rotation. Int. J. Intell. Syst. App. 11(3), 1–10 (2019)

    Google Scholar 

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Authors and Affiliations

  1. National University of Life and Environment Science of Ukraine, Kyiv, 03041, Ukraine

    Maryna Nehrey, Tatiana Koval, Nataliy Rogoza & Liudmyla Galaieva

Authors
  1. Maryna Nehrey
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  2. Tatiana Koval
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  3. Nataliy Rogoza
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  4. Liudmyla Galaieva
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Corresponding author

Correspondence to Maryna Nehrey .

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Editors and Affiliations

  1. International Center of Informatics and Computer Science, Faculty of Applied Mathematics, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine

    Zhengbing Hu

  2. School of Computer Science, Hubei University of Technology, Wuhan, China

    Zhiwei Ye

  3. Halmos College of Arts and Sciences, Nova Southeastern University, Fort Lauderdale, FL, USA

    Matthew He

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Nehrey, M., Koval, T., Rogoza, N., Galaieva, L. (2023). Application Possibilities of Data Science Tools in Agriculture: A Review. In: Hu, Z., Ye, Z., He, M. (eds) Advances in Artificial Systems for Medicine and Education VI. AIMEE 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 159. Springer, Cham. https://doi.org/10.1007/978-3-031-24468-1_23

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