The use of artificial intelligence in agriculture: Global practice

This article is devoted to the analysis of current trends and practices of the application of artificial intelligence (AI) in agriculture based on peer-reviewed scientific sources of 2022–2026. The purpose of the work is a comprehensive study of the problems and key trends of the development of AI in the agricultural sector, as well as the systematization of quantitative data confirming the effectiveness of the implemented technologies. The article discusses the main areas of AI use: crop yield forecasting based on neural network algorithms (LSTM, CNN), plant health monitoring and precise resource application using computer vision (ResNet, YOLO), robotization of production processes, and the introduction of intelligent systems into the economy and management of the agro-industrial complex. Based on the analysis of empirical studies, the article systematizes performance indicators: the accuracy of plant disease diagnosis reaches 99.2%, the reduction of pesticide use is up to 90%, water resources are saved by up to 46%, and farm income increases by 15–20%. Special attention is paid to a comparative analysis of the barriers to the implementation of AI in different groups of countries. In developed countries, the main problems are technological fragmentation and a shortage of qualified personnel. In BRICS countries, there are infrastructure limitations and a gap between science and production. In developing countries, the key obstacles are the lack of basic infrastructure, the low solvency of small farms, and a lack of digital literacy. The final part identifies understudied aspects that require further research, such as socio-psychological barriers to technology adoption, the environmental impact of AI solutions, issues related to data sovereignty and monetization, economic efficiency for small-scale farmers, cybersecurity, and ethical dilemmas in breeding.

1. Bayrak T., Olu M. Applications of artificial intelligence in smart agriculture: Plant health, drone technology, and digital communication. Journal of Agricultural Production. 2025;6(3):157-166. URL: https://doi. org/10.56430/japro.1767174

2. Ashe G., Mengistu D. Review on future farming using AI. Asian Science Bulletin. 2025;3(1):82-91. URL: https://doi.org/10.3923/asb.2025.82.91

3. Wildan J. F. A review: Artificial intelligence related to agricultural equipment integrated with the internet of things. Journal of Advanced Technology and Multidisciplinary. 2023;02(02):47-60. URL: https://doi.org/10.20473/jatm.v2i2.51440

4. Rozhkova A. V., et al. Prospects for the use of artificial intelligence in agriculture. IOP Conference Series: Earth and Environmental Science. 2022;1076(1):012051. URL: https://doi.org/10.1088/1755-1315/1076/1/012051

5. Kali S. Artificial intelligence in plant modelling and simulation. In: Plant Digital Twins: The Fusion of Biology and Artificial Intelligence; 2025. URL: https://doi.org/10.55938/wlp.v2i7.310

6. Shaitura S. V., Semichevskaya N. P., Shaitura N. S. Predicting crop yields in the southern regions of Russia with artificial intelligence tools. Digital Solutions and Artificial Intelligence Technologies. 2025;1(4):76-85. (In Russ.). URL: https://doi.org/10.26794/3033-7097-2025-1-4-76-85

7. Shebanina O., Tyshchenko S., Parkhomenko O., Khylko I., Krainii V. Application of artificial intelligence to improve the economic efficiency of land use management in the agricultural sector. Ekonomika APK. 2025;32(1):82-90. URL: https://doi.org/10.32317/ekon.apk/1.2025.82

8. Monsalve-Castro C., Ramirez Molina R. I., Fajardo Ortiz E. J., Soto Polo G. P. Artificial intelligence as a driver in adopting green innovation in the agricultural sector: evidence from an emerging economy. Cogent Business & Management. 2025;12(1):2575261. URL: https://doi.org/10.1080/23311975.2025.2575261

9. Zhang Q., Wang Q., Liang Y., Zhang K., Hu H. Artificial intelligence and the sustainable development of agricultural enterprises: a total factor productivity perspective. Frontiers in Sustainable Food Systems. 2026;10:1768115. URL: https://doi.org/10.3389/fsufs.2026.1768115

10. Saravanan P., Suguna Dr.M. A study on problems and prospects of agricultural marketing in the era of artificial intelligence and smart farming with special reference to Tamil Nadu. International Journal of Accounting and Economics Studies. 2025;12(4):554-561. URL: https://doi.org/10.14419/jf9cjx58

11. Caglar E. The impact of sectors on agriculture based on artificial intelligence data: a case study on G7 countries and Turkiye. International Journal of Agriculture, Environment and Food Sciences. 2024;8(3):486-494. URL: https://doi.org/10.31015/jaefs.2024.3.1

12. Slavova G., Todorova M. Opportunities for the application of artificial intelligence in the agricultural sector of Bulgaria. In: The interdisciplinary approach in economic and social sciences. 2024:323-333. URL: https://doi.org/10.3897/ap.10.e0323

13. Vedapathak D. M.D. Artificial intelligence (AI) and agricultural biotechnology. The International Journal of Commerce Management and Business Law in International Research. 2025;2(2):13-16. URL: https://doi.org/10.5281/zenodo.15709415

14. Harfouche Z., Benattallah A. Evaluating the efforts of utilizing artificial intelligence to enhance agricultural entrepreneurship: A case study of Tunisia. Current Perspectives in Social Sciences. 2025;29(4):732-753. URL: https://doi.org/10.53487/atasobed.1734836

15. Aladejebi O., Amao-Taiwo B., Oshinowo B. How artificial intelligence is revolutionizing agriculture in Nigeria? Archives of Business Research. 2026;14(02):139-157. URL: https://doi.org/10.14738/abr.1402.20078

16. Raihan A. A review of artificial intelligence for sustainable agriculture. Proceedings of the International Conference on Climate-Aware Agriculture. 2024. URL: https://www.researchgate.net/publication/386989366

17. Wahyuni R., Utomo S. W.A., Ghifari Z. Artificial neural network prediction model for agricultural commodity production using backpropagation algorithm. Knowbase: International Journal of Knowledge in Databases. 2025;5(01):52-68. URL: https://doi.org/10.30983/knowbase.v5i1.9530

18. Singh V., Choudhary S., Kumar H. Utilizing artificial intelligence technologies in the Indian agriculture sector. 2024. URL: https://doi.org/10.13140/RG.2.2.24666.56001

19. Yadav S. Introduction to plant digital twins: Concept and evolution. In: Plant Digital Twins: The Fusion of Biology and Artificial Intelligence. Wisdom Leaf Press; 2025:1-25. URL: https://doi.org/10.55938/wlp.v2i7.308

20. Kumar A., et al. Role of artificial intelligence in vegetable production: A review. Journal of Scientific Research and Reports. 2024;30(9):950-963. URL: https://doi.org/10.9734/jsrr/2024/v30i92423

21. González-Rodríguez V. E., Izquierdo-Bueno I., Cantoral J. M. et al. Artificial intelligence: A promising tool for application in phytopathology. Horticulturae. 2024;10:197. URL: https://doi.org/10.3390/horticulturae10030197

22. Istudor N., Ignat R., Deaconu M., Constantin M. Risk-management in the era of artificial intelligence in agriculture. Proceedings of the International Conference on Agriculture and Food Engineering. 2023. URL: https://doi.org/10.24818/CAFEE/2023/12/08