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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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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