Application of Artificial Intelligence Technologies in Agriculture
DOI:
https://doi.org/10.52171/herald.334Keywords:
artificial intelligence, machine learning, aerospace industry, digital revolution, drug development, healthcare systemsAbstract
The article provides information on the division of artificial intelligence based on its functionality and degree of autonomy, i.e. weak (narrow), general and strong. In addition, it is noted that it is divided by functionality into: reactive machines, limited memory, theory of mind and self-awareness. In the field of artificial intelligence, Azerbaijan launched the country's first scientific information search system "Scopus AI" and implemented the "Smart Village" project in the village of Agaly in the Zangilan region. The study also presents the use of artificial intelligence for soil analysis and monitoring, advancement in the sowing phase of agricultural crops, pest/weed control methods, with a justified speed for harvesting and delivering products to the right place. Thanks to research and innovation, the agricultural sector is undergoing a qualitative transformation, becoming more technologically advanced, environmentally friendly and adapted to the challenges of our time. Examples include a drone with artificial intelligence collecting data on plant health, the use of geographic information system (GIS) in irrigation methods, a pilot project with 175 farmers, a robot for picking apples, and the “See and Spray” project. Finally, the goal of the work of the Republic of Azerbaijan in the field of using artificial intelligence (AI) technology in the agricultural sector was presented, which will greatly benefit farmers, especially in unforeseen situations caused by climate change.
References
1. Spector L. Evolution of artificial intelligence. Artif. Intell. 170(18), 1251–1253 (2006)
2. Kamble R., Shah D. Applications of artificial intelligence in human life. Int. J. Res. 6(6), 178–188 (2018)
3. Singh H. Artificial intelligence revolution and India’s AI development: challenges and scope. Int. J. Sci. Res. 3(3), 417–421 (2017)
4. Simeone O. A brief introduction to machine learning for engineers. Found. Trends Signal Pro-cess. 12(3–4), 200–431 (2018)
5. Wang X., Lin X., Dang X. Supervised learning in spiking neural networks: a review of algo-rithms and evaluations. Neural Netw. 125, 258–280 (2020)
6. Jiang F., Jiang Y., Zhi H., Dong Y., Li Hao Ma, Sufeng Wang Yongjun, Dong Q., Shen H., Wang Y. Artificial intelligence in healthcare: Past, present and future. Stroke Vasc. Neurol. 2 (4), 230 - 243 (2017).
7. Simeone O. A very brief introduction to machine learning with applications to communication systems. IEEE Trans. Cogn. Commun. Netw. 4(4), 648–664 (2018)
8. Herzog S., Tetzlaff C., Wörgötter F. Evolving artificial neural networks with feedback. Neural Netw. 123, 153–162 (2020)
9. Lobo J.L., Del Ser J., Bifet A., Kasa bov N.: Spiking Neural Networks and online learning: an overview and perspectives. Neural Netw. 121, 88–100 (2020)
10. Alakbarova T.Sh., Haciyev R.M. Analysis of Robocall Attacks: Methods of Protection Against This Threat // Herald of the Azerbaijan Engineering Academy, 2025, vol. 17 (3), pp. 98-103. DOI: https://doi.org/10.52171/herald.301
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
