Innovative smart agriculture toolkit in agribusiness cost management: modeling and practice of digital transformation
Keywords:
Smart Agriculture, Agriculture 4.0, digital transformation, cost management, Internet of Things (IoT), artificial intelligence, precision farming, digital readiness index, econometric modeling, agribusinessSynopsis
The article is devoted to a comprehensive study of the theoretical, methodological, and applied aspects of implementing Smart Agriculture technologies into the cost management systems of agricultural enterprises in the context of the Fourth Industrial Revolution.
The first section systematizes the conceptual foundations of Smart Agriculture and defines its place in the evolution from mechanization, through automation, to intelligent systems based on Big Data. The architecture of a multi-level digital cost management system is substantiated, integrating the Internet of Things (IoT), Artificial Intelligence (AI), digital twins, and Variable Rate Technology (VRT). The functional role of key components is revealed, including GPS navigation, NDVI satellite monitoring, unmanned aerial vehicles (UAVs), sensor networks, Farm Management Information Systems (FMIS), and predictive analytics algorithms.
The second section presents the results of econometric modeling of Smart technology efficiency. A methodology for assessing digital readiness based on the IDR index is developed, combining technical level, human resource potential, and management digitalization. An economic calculation for implementing a basic package for a 300-hectare farm is provided: capital expenditures of 108 thousand UAH pay back in 6.5 months with 5% savings. The critical implementation threshold is identified at 100–120 hectares. It is proven that digitalization is a necessity for large enterprises, while for small ones, it requires thorough justification.
An analysis of the dependence of efficiency on scale was conducted: farms of 200-300 hectares achieve payback within a year, over 500 hectares – within a season, and those under 50 hectares require state support. Sensitivity analysis confirmed the model's stability even under a pessimistic scenario (payback period of 1.4 years).
The results can be used by agricultural enterprises to justify investments, by researchers for studies on digital transformation, and by government agencies when formulating innovation support policies. The article is addressed to scientists, lecturers, students of economic and agricultural specialties, agribusiness managers, and public administration officials.
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Rozetka. Available at: https://rozetka.com.ua/
Schimmelpfennig, D., Lowenberg-DeBoer, J. (2020). Farm Types and Precision Agriculture Adoption: Crops, Regions, Soil Variability, and Farm Size. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3689311
Lowenberg-DeBoer, J., Huang, I. Y., Grigoriadis, V., Blackmore, S. (2019). Economics of robots and automation in field crop production. Precision Agriculture, 21 (2), 278–299. https://doi.org/10.1007/s11119-019-09667-5
Rajak, P., Ganguly, A., Adhikary, S., Bhattacharya, S. (2023). Internet of Things and smart sensors in agriculture: Scopes and challenges. Journal of Agriculture and Food Research, 14, 100776. https://doi.org/10.1016/j.jafr.2023.100776
Azlan, Z. H. Z., Junaini, S. N., Khan, A. S., Mustafa, W. A. (2025). Data‐Driven Agriculture: Unveiling the Power of Internet of Things and Data Analytics For Transformative Farming Practices. Journal of Sensors, 2025 (1). https://doi.org/10.1155/js/6205646
Wolfert, S., Ge, L., Verdouw, C., Bogaardt, M.-J. (2017). Big Data in Smart Farming – A review. Agricultural Systems, 153, 69–80. https://doi.org/10.1016/j.agsy.2017.01.023
Saiz-Rubio, V., Rovira-Más, F. (2020). From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management. Agronomy, 10 (2), 207. https://doi.org/10.3390/agronomy10020207
Ayoub Shaikh, T., Rasool, T., Rasheed Lone, F. (2022). Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Computers and Electronics in Agriculture, 198, 107119. https://doi.org/10.1016/j.compag.2022.107119
Bacco, M., Barsocchi, P., Ferro, E., Gotta, A., Ruggeri, M. (2019). The Digitisation of Agriculture: a Survey of Research Activities on Smart Farming. Array, 3-4, 100009. https://doi.org/10.1016/j.array.2019.100009
Talaviya, T., Shah, D., Patel, N., Yagnik, H., Shah, M. (2020). Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture, 4, 58–73. https://doi.org/10.1016/j.aiia.2020.04.002
Paudel, B., Riaz, S., Teng, S. W., Kolluri, R. R., Sandhu, H. (2025). The digital future of farming: A bibliometric analysis of big data in smart farming research. Cleaner and Circular Bioeconomy, 10, 100132. https://doi.org/10.1016/j.clcb.2024.100132
Hassoun, A., Marvin, H. J. P., Bouzembrak, Y., Barba, F. J., Castagnini, J. M., Pallarés, N. et al. (2023). Digital transformation in the agri-food industry: recent applications and the role of the COVID-19 pandemic. Frontiers in Sustainable Food Systems, 7. https://doi.org/10.3389/fsufs.2023.1217813
Pylianidis, C., Osinga, S., Athanasiadis, I. N. (2021). Introducing digital twins to agriculture. Computers and Electronics in Agriculture, 184, 105942. https://doi.org/10.1016/j.compag.2020.105942
Pivoto, D., Waquil, P. D., Talamini, E., Finocchio, C. P. S., Dalla Corte, V. F., de Vargas Mores, G. (2018). Scientific development of smart farming technologies and their application in Brazil. Information Processing in Agriculture, 5 (1), 21–32. https://doi.org/10.1016/j.inpa.2017.12.002
Liakos, K., Busato, P., Moshou, D., Pearson, S., Bochtis, D. (2018). Machine Learning in Agriculture: A Review. Sensors, 18 (8), 2674. https://doi.org/10.3390/s18082674
Kamilaris, A., Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90. https://doi.org/10.1016/j.compag.2018.02.016
Lan, J., Ban, Q. (2025). The Farm-Level Economic and Environmental Benefits of Precision Agriculture Technology Adoption: A Meta-Analysis of Global Evidence. Sustainability, 17 (24), 11223. https://doi.org/10.3390/su172411223
Lowenberg‐DeBoer, J., Erickson, B. (2019). Setting the Record Straight on Precision Agriculture Adoption. Agronomy Journal, 111 (4), 1552–1569. https://doi.org/10.2134/agronj2018.12.0779
Schimmelpfennig, D., Ebel, R. (2016). Sequential adoption and cost savings from precision agriculture. Journal of Agricultural and Resource Economics, 41 (1), 97–115.
Schimmelpfennig, D. (2016). Farm profits and adoption of precision agriculture (Economic Research Report No. 217). U.S. Department of Agriculture, Economic Research Service. Available at: https://www.ers.usda.gov/publications/pub-details/?pubid=80325
Gabriel, A., Gandorfer, M. (2023). Adoption of digital technologies in agriculture – an inventory in a european small-scale farming region. Precision Agriculture, 24, 68–91. https://doi.org/10.1007/s11119-022-09931-1
Papadopoulos, G., Arduini, S., Uyar, H., Psiroukis, V., Kasimati, A., Fountas, S. (2024). Economic and environmental benefits of digital agricultural technologies in crop production: A review. Smart Agricultural Technology, 8, 100441. https://doi.org/10.1016/j.atech.2024.100441
Zhang, Q., Liu, Y., Gong, C., Chen, Y., Yu, H. (2020). Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review. Sensors, 20 (5), 1520. https://doi.org/10.3390/s20051520
Klerkx, L., Jakku, E., Labarthe, P. (2019). A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS: Wageningen Journal of Life Sciences, 90-91 (1), 1–16. https://doi.org/10.1016/j.njas.2019.100315
Finger, R. (2023). Digital innovations for sustainable and resilient agricultural systems. European Review of Agricultural Economics, 50 (4), 1277–1309. https://doi.org/10.1093/erae/jbad021
Ma, W., Nie, P., Zhang, P., Renwick, A. (2020). Impact of Internet use on economic well-being of rural households: Evidence from China. Review of Development Economics, 24 (2), 503–523. https://doi.org/10.1111/rode.12645
Lyu, Y., Wang, W., Wu, Y., Zhang, J. (2023). How does digital economy affect green total factor productivity? Evidence from China. Science of The Total Environment, 857, 159428. https://doi.org/10.1016/j.scitotenv.2022.159428
Bronson, K. (2018). Smart Farming: Including Rights Holders for Responsible Agricultural Innovation. Technology Innovation Management Review, 8 (2), 7–14. https://doi.org/10.22215/timreview/1135
Rozetka. Available at: https://rozetka.com.ua/
Schimmelpfennig, D., Lowenberg-DeBoer, J. (2020). Farm Types and Precision Agriculture Adoption: Crops, Regions, Soil Variability, and Farm Size. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3689311
Lowenberg-DeBoer, J., Huang, I. Y., Grigoriadis, V., Blackmore, S. (2019). Economics of robots and automation in field crop production. Precision Agriculture, 21 (2), 278–299. https://doi.org/10.1007/s11119-019-09667-5
Text Identifier of this Chapter
Chapter 3
Published
December 4, 2025
Copyright (c) 2026 Hryhorii Savelenko, Nataliia Sysolina, Iryna Sysolina
How to Cite
Savelenko, H., Sysolina, N., & Sysolina, I. (2025). Innovative smart agriculture toolkit in agribusiness cost management: modeling and practice of digital transformation. In P. Lykhovyd, V. Silchenko, L. Kononenko, V. Savchenko, A. Karnaushenko, Y. Hlavatska, H. Savelenko, N. Sysolina, I. Sysolina, K. Vasylkovska, O. Andriienko, V. Malakhovska, I. Androshchuk, O. Vasylkovskyi, S. Moroz, G. Karashchuk, & E. Kulidzhanov, In Press. Innovative technological solutions in agriculture. Scientific Route OÜ®. https://monograph.route.ee/rout/catalog/book/innovative_technological_solutions_in_agriculture/chapter/162
