Machinery & Energetics

Digitalisation of field research data – the basis of farm management

Stanislav Nikolayenko, Mykola Volokha
Download article
Abstract

The efficiency of modern agricultural production management was shown to depend largely on the implementation of digitalisation processes, which transformed the paradigm of production process management and created conditions for increasing agricultural productivity through the use of innovative digital technologies. The purpose of the article was to review scientific publications devoted to the digitisation of field experiment databases in order to enhance the profitability of agricultural production through the development of effective management decisions in crop production based on data obtained from numerous sources of digital transformation. It was demonstrated that the diversity of technological processes, as well as input and output data involved in agricultural production, resulted in significant complexity and integrative challenges in assessing production sustainability. It was identified that the majority of farmers were not specialists in information technologies and were therefore unable to fully comprehend the complexity of the algorithms underlying modern digital solutions. Approaches to the use of big data in agriculture were systematised with regard to the evolution of the 4V concept towards the 5V framework, in which data value was emphasised as a critical requirement. A scientific and methodological approach aimed at improving agricultural production efficiency through the application of machine learning methods was proposed, including automatic crop recognition, disease and weed detection, and the forecasting of crop yield and product quality. It was established that, alongside material and technical support, the intellectualisation of production and management activities based on digitalisation constituted a priority factor in the development of the agro-industrial sector, being considered a highly effective means of increasing the return on investment in agriculture by 10-15%

Keywords

agricultural production efficiency, digitalisation, yield, soil fertility, moisture reserves

Suggested citation
Nikolayenko, S., & Volokha, M. (2025). Digitalisation of field research data – the basis of farm management. Machinery & Energetics, 16(4), 43-52. https://doi.org/10.31548/machinery/4.2025.43
References
  1. Almeida, G.M.D., Pereira, G.T., Bahia, A.S.R.D.S., Fernandes, K., & Marques, J.J. (2021). Machine learning in the prediction of sugarcane production environments. Computers and Electronics in Agriculture, 190, article number 106452. doi: 10.1016/j.compag.2021.106452.
  2. ЯкірAng, K.L.-M., & Seng, J.K.P. (2021). Big data and machine learning with hyperspectral information in agriculture. IEEE Access, 9, 36699-36718. doi: 10.1109/ACCESS.2021.3051196.
  3. Borodin, V., Bourtembourg, J., Hnaien, F., & Labadie, N. (2016). Handling uncertainty in agricultural supply chain management: A state of the art. European Journal of Operational Research, 254(2), 348-359. doi: 10.1016/j.ejor.2016.03.057.
  4. Chang, W.L., & Grady, N. (2018). NIST big data interoperability framework: Volume 1, big data definitions (NIST special publication 1500-1). Gaithersburg, MD: National Institute of Standards and Technology. doi: 10.6028/NIST.SP.1500-1.
  5. Choi, R.Y., Coyner, A.S., Kalpathy-Cramer, J., Chiang, M.F., & Campbell, J.P. (2020). Introduction to machine learning, neural networks, and deep learning. Translational Vision Science & Technology, 9(2), article number 14. doi: 10.1167/tvst.9.2.14.
  6. Cravero, A., & Sepúlveda, S. (2021). Use and adaptations of machine learning in Big Data-applications in real cases in agriculture. Electronics, 10, article number 552. doi: 10.3390/electronics10050552.
  7. Evstatiev, B.I., & Gabrovska-Evstatieva, K.G. (2020). Review on the methods for big data analysis in agriculture. IOP Conference Series: Materials Science and Engineering, 1032, article number 012053. doi: 10.1088/1757-899X/1032/1/012053.
  8. Fan, X., Nan, Z., Ma, Y., Zhang, Y., & Han, F. (2021). Research on the spatio-temporal impacts of environmental factors on the fresh agricultural product supply chain and the spatial differentiation issue – an empirical research on 31 Chinese provinces. International Journal of Environmental Research and Public Health, 18(22), article number 12141. doi: 10.3390/ijerph182212141.
  9. Gao, X., Gao, J., & Qureshi, W.A. (2025). Applications, trends, and challenges of precision weed control technologies based on deep learning and machine vision. Agronomy, 15(8), article number 1954. doi: 10.3390/agronomy15081954.
  10. Hasan, M., Marjan, M.A., Uddin, M.P., Afjal, M.I., Kardy, S., Ma, S., & Nam, Y. (2023). Ensemble machine learning-based recommendation system for effective prediction of suitable agricultural crop cultivation. Frontiers in Plant Science, 14, article number 1234555. doi: 10.3389/fpls.2023.1234555.
  11. Islam, M.R., Oliullah, K., Kabir, M.M., Alom, M., & Mridha, M.F. (2023). Machine learning enabled IoT system for soil nutrients monitoring and crop recommendation. Journal of Agriculture and Food Research, 14, article number 100880. doi: 10.1016/j.jafr.2023.100880.
  12. Jiang, L., Zeng, G., Wan, C., & Liu, Z. (2019). Research on the countermeasures for rural revitalization of the ethnic minorities in border areas: A case study of Heihe City in Heilongjiang Province. In Proceedings of the 3rd international conference on culture, education and economic development of modern society (ICCESE 2019) (pp. 657-660). Paris: Atlantis Press. doi: 10.2991/iccese-19.2019.144.
  13. Lampridi, M., Sørensen, C., & Bochtis, D. (2019). Agricultural sustainability: A review of concepts and methods. Sustainability, 11, article number 5120. doi: 10.3390/su11185120.
  14. Lazorenko, T., & Sholom, I. (2018). Digitalization as the main factor of business development. Proceedings of the international scientific and practical conference (pp. 50-51). Kyiv: Kyiv Polytechnic Institute.
  15. Li, D., Bai, L., Wang, R., & Ying, S. (2024). Research progress of machine learning in extending and regulating the shelf life of fruits and vegetables. Foods, 13(19), article number 3025. doi: 10.3390/foods13193025.
  16. Mauget, S.A., Adhikari, P., Leiker, G., Baumhardt, R.L., Thorp, K.R., & Ale, S. (2017). Modeling the impact of management and increase on cotton production in arid areas of West Texas. Agricultural and Forest Meteorology, 247, 385-398. doi: 10.1016/j.agrformet.2017.07.009.
  17. Mayuri, K.P. (2018). Role of image processing and machine learning techniques in disease recognition, diagnosis and yield prediction of crops: A review. International Journal of Advanced Research in Computer Science, 9(2), 788-795. doi: 10.26483/ijarcs.v9i2.5793.
  18. Neupane, J., & Guo, W. (2019). Agronomic bases and strategies of accurate water resources management: A review. Agronomy, 9(2), article number 87. doi: 10.3390/agronomy9020087.
  19. Ofosu, R., Agyemang, E.D., Márton, A., Pásztor, G., Taller, J., & Kazinczi, G. (2023). Herbicide resistance: Managing weeds in a changing world. Agronomy, 13, article number 1595. doi: 10.3390/agronomy13061595.
  20. Papageorgiou, E.I., Angelopoulou, K., Gemtos, T.A., & Nanos, G.D. (2018). Development and evaluation of a fuzzy inference system and a neuro-fuzzy inference system for grading apple quality. Applied Artificial Intelligence, 32(3), 253-280. doi: 10.1080/08839514.2018.1448072.
  21. Pounds, K., Bao, H., Luo, Y., De, J., Schneider, K., Correll, M., & Tong, Z. (2022). Real-time and rapid food quality monitoring using smart sensory films with image analysis and machine learning. ACS Food Science & Technology, 2(7), 1123-1134. doi: 10.1021/acsfoodscitech.2c00124.
  22. Rejeb, A., Rejeb, K., Zailani, S., Keogh, J.G., & Appolloni, A. (2022). Examining the interplay between artificial intelligence and the agri-food industry. Artificial Intelligence in Agriculture, 6, 111-128. doi: 10.1016/j.aiia.2022.08.002.
  23. Rudenko, M. (2019). The features of digitization influence on the functioning of agricultural enterprises. Bulletin of the Kharkiv National Agricultural University of V.V. Dokuchaeva, 1, 202-212. doi: 10.31359/2312-3427-2019-1-202.
  24. Sibo, X., Xinyuan, N., Xin, C., & Xumeng, L. (2022). Data fusion based wireless temperature monitoring system applied to intelligent greenhouse. Computers and Electronics in Agriculture, 192, article number 106576. doi: 10.1016/j.compag.2021.106576.
  25. Sonka, S. (2016). Big Data: Fueling the next evolution of agricultural innovation. Journal of Innovation Management, 4(1), 114-136. doi: 10.24840/2183-0606_004.001_0008.
  26. Sørensen, C.A.G., Kateris, D., & Bochtis, D. (2021). ICT innovation and smart agriculture. In M. Salampasis & T. Bournaris (Eds.), Information and communication technologies in modern agricultural development (vol. 953, pp. 1-19). Cham: Springer. doi: 10.1007/978-3-030-12998-9_1.
  27. Sun, A.Y., & Scanlon, B.R. (2019). How can Big Data and machine learning benefit environment and water management: A survey of methods, applications, and future directions. Environmental Research Letters, 14(7), article number 073001. doi: 10.1088/1748-9326/ab1b7d.
  28. The Guardian. (2017). Tsunami of data could consume one fifth of global electricity by 2025. Retrieved from https://www.theguardian.com/environment/2017/dec/11/tsunami-of-data-could-consume-fifth-global-electricity-by-2025.
  29. Trushlyakova, A.B. (2018). Development of digitalization in Ukraine: Factors of influence, advantages and challenges of today. Economies’ Horizons, 4(7), 186-191. doi: 10.31499/2616-5236.4(7).2018.212762.
  30. Upadhyaya, S.R., Danilevicz, M.F., Dolatabadian, A., Neik, T.X., Zhang, F., Al-Mamun, H.A., Bennamoun, M., Batley, J., & Edwards, D. (2024). Genomics-based plant disease resistance prediction using machine learning. Plant Pathology, 73(9), 2298-2309. doi: 10.1111/ppa.13988.
  31. Varga, V. (2020). Digitalization as one of the factors of enterprise competitiveness. Efektyvna Ekonomika, 8. doi: 10.32702/2307-2105-2020.8.154.
  32. Virnodkar, S., Pachghare, V., Patil, V., & Jha, S. (2020). Remote sensing and machine learning for crop water stress determination in various crops: A critical review. Precision Agriculture, 21, 1121-1155. doi: 10.1007/s11119-020-09711-9.
  33. Volokha, M., Bondar, S., Boldyrieva, L., & Kisilevskaya, M. (2022). Advantages of digitalization in field data processing and crop management. In Modern research in world science (pp. 414-420). Lviv: Scientific Publishing Center “Sci-conf.com.ua”.
  34. Voloshchuk, Y. (2019). Digital directions of agrarian enterprises. Efektyvna Ekonomika, 2. doi: 10.32702/2307-2105-2019.2.66.
  35. Yuan, Y., Chen, L., Wu, H., & Li, L. (2022). Advanced agricultural disease image recognition technologies: A review. Information Processing in Agriculture, 9(1), 48-59. doi: 10.1016/j.inpa.2021.01.003.
  36. Zecca, F. (2019). The use of Internet of Things for the sustainability of the agricultural sector: The case of climate smart agriculture. International Journal of Civil Engineering and Technology, 10, 494-501.
  37. Zhang, J., Rao, Y., Husband, K., Jiang, Z., & Lee, S. (2021). Identification of cucumber leaf diseases using deep learning and small sample size for agricultural Internet of Things. International Journal of Distributed Sensor Networks, 17(4). doi: 10.1177/15501477211007407.
  38. Zhao, W., Li, J., Yang, R., & Li, Y. (2018). Determining placement criteria of moisture sensors through temporal stability analysis of soil water contents for a variable rate irrigation system. Precision Agriculture, 19(4), 648-665. doi: 10.1007/s11119-017-9545-2.
  39. Zou, Y., Wu, J., Wang, X., Morales, K., Liu, G., & Manzardo, A. (2023). An improved artificial neural network using multi-source data to estimate food temperature during multi-temperature delivery. Journal of Food Engineering, 351, article number 111518. doi: 10.1016/j.jfoodeng.2023.111518.