O.V. Lebid, S.S. Kiporenko, V.Yu. Vovk

Èlektron. model. 2023, 45(3):57-71


Artificial intelligence (AI) technologies are used in various sectors of the national economy, in particular in agriculture. The purpose of the research is to consider the essence and directions of application of AI technologies in agriculture. These technologies are used in various branches of agriculture: detection of plant diseases, classification and identification of weeds, determination and counting of fruits, management of water resources and soil, forecasting of weather (climate), determination of animal behavior. AI technologies used in agriculture have a number of significant features. First of all, these are software and technical means. AI technologies perform an intellectual function when performing work in agriculture, which consists in making abstract conclusions, recognizing patterns, taking actions in conditions of incomplete information, showing creativity, and the ability to self-learn. The strengths of the use of AI technologies include increasing labor productivity in the agricultural sector, increasing the efficiency of management decisions, as well as increasing access to information, expanding human opportunities in the workplace and the emergence of new professions. The main opportunities are related to various technical breakthroughs, including machine learning, the use of neural networks, big data, etc. This will create additional jobs in high-tech sectors, in particular in programming. AI technologies will allow to optimize the production of food all over the world and reduce the severity of the problem of global hunger. One of the threats to Ukraine lies in the apparent lag behind advanced countries in the development of these technologies for agriculture. The results of the research can be used by the executive authorities when develo­ping programs for the innovative development of agriculture and technical modernization of the industry.


information technologies, artificial intelligence, agriculture, innovative development, digitization.


  1. Detection of cherry tree branches with full foliage in planar architecture for automated sweet-cherry harvesting / S. Amatya et al. (2016). Biosystems Engineering, 146, 3–15.
  2. Denning P.J., Lewis T.G. (2016). Exponential laws of computing growth. Communications of the ACM, 60, 54–65.
  3. Dynamic cattle behavioural classification using supervised ensemble classifiers / R. Dutta et al. (2015). Computers and Electronics in Agriculture, 111, 18–28.
  4. Impacts of the digital economy on the food chain and the CAP / Research for AGRI Committee of EP. Policy Department for Structural and Cohesion Policies Directorate-General for Internal Policies. IPOL_STU(2019)629192_EN.pdf/.
  5. Information and Communication Technology (ICT) in Agriculture: A Report to the G20 Agricultural Deputies. Rome: FAO. Information%20and%20Communication%20Technology%20-ICT-%20in%20Agriculture. pdf/.
  6. Mehdizadeh S., Behmanesh J., Khalili K. (2017). Using MARS, SVM, GEP and empirical equations for estimation of monthly mean reference evapotranspiration. Computers and Electronics in Agriculture, 139, 103–114. URL:
  7. Input selection and data-driven model performance optimization to predict the Standardized Precipitation and Evaporation Index in a drought-prone region / S. Mouatadid et al. (2018). Atmospheric Research, 212, 130–149.
  8. Detection of biotic and abiotic stresses in crops by using hierarchical self organizing classifiers / X.E. Pantazi et al. (2017). Precision Agriculture, 18, 3, 383–393.
  9. Soil moisture forecasting by a hybrid machine learning technique: ELM integrated with ensemble empirical mode decomposition / R. Prasad et al. (2018). Geoderma, 330, 136–161.
  10. Sengupta S., Lee W.S. (2014). Identification and determination of the number of immature green citrus fruit in a canopy under different ambient light conditions. Biosystems Enginee­ring, 117, 51–61.
  11. World Population Prospects 2022: Summary of Results – World. ReliefWeb. URL: https:// int/report/world/world-population-prospects-2022-summary-results?gclid=CjwK CAjw0ZiiBhBKEiwA4PT9z0KEXLCRm0bW0WsLa8Ci2AkuuLVYFLNGKjVvsBE_SaVRcCrMWn0R6RoCQZwQAvD_BwE (date of access: 11.05.2023).
  12. Boltianska, N. (2020). Prospects and problems of development of information technologies in agriculture. Proceedings of the Tavria State agrotechnological university, 20, 4, 175–185.
  13. Pasichnyk, Yu.V. (2021), The use of artificial intelligence technologies in the agro-industrial sector of the economy. Modern trends in the development of financial and innovative investment processes in Ukraine : materials of the 4-th International Scientific and Practical Conference, Vinnytsia: VNTU, 880–882.
  14. State Statistics Service of Ukraine. Capital Investments.
  15. Land population.
  16. Pizhuk, O.I. (2019). Artificial intelligence as one of the key drivers of the digital transformation of the economy. Economy, management and administration, 3 (89), 41–46.
  17. Rudenko, M.V. (2019). The influence of digital technologies on agricultural production: methodical aspect. Academic notes of TNU named after V.I. Vernadskyi. Series: Economics and management, 30 (69) (6), 30-36.

Full text: PDF