Generative artificial intelligence as a strategic planning tool in agricultural companies
Abstract
Purpose. To investigate the impact of artificial intelligence on the development and decision-making of agricultural companies Materials and research methods. In the process of carrying out the work, general scientific research methods were applied: dispersion – to determine the reliability of the obtained research results; comparative and computational – to conduct economic and energy efficiency of the studied technologies. Research results. Artificial intelligence (AI) has revolutionized all industries around the world, including the management of agricultural companies. AI is changing supply chains, helping organizations navigate a rapidly changing world. For example, intelligent algorithms allow for strategic planning. This article discusses the application of new innovative technologies in agriculture. In particular, the application of artificial intelligence neural networks, as well as generative artificial intelligence in this field. It is shown that artificial intelligence neural networks are best suited for use in precision agriculture, especially when it is necessary to find a relationship between a large number of factors. Generative artificial intelligence can be used as a tool to assist in data analysis. The article emphasizes the importance of systematic and rational use of resources. It is noted that data preprocessing is the “weakest link” in any production process, and therefore in the agricultural sector as well. The main attention is paid to issues of practical work in the field of data preprocessing. Conclusion: While the results achieved are significant, there are several areas for further research and improvement. Through the application of generative AI, agricultural companies can significantly increase the efficiency and accuracy of their strategic planning, using huge amounts of data to assess various agricultural factors at scale. AI capabilities in the field of predictive analytics, machine learning, natural language processing and other advanced technologies have opened up new opportunities for understanding customers and natural processes in agriculture, providing agronomists with the tools to anticipate consumer needs and adapt their methods accordingly.
References
2. Lekkala S. Next-Gen Firewalls: Enhancing Cloud Security with Generative AI. Journal of Artificial Intelligence & Cloud Computing. 2024;3(4):1–9. Scientific Research and Community Ltd. https://doi.org/10.47363/jaicc/2024(3)404
3. Sarisa M., Patra G. K., Kuraku C., Konkimalla S., Boddapati V. N. Stock Market Prediction Through AI: Analyzing Market Trends With Big Data Integration. Migration Letters. 2024;21(4):1846–1859. URL: https://migrationletters.com/index.php/ml/article/view/11245
4. Polineni T. N. S., Maguluri K. K., Yasmeen Z., Edward A. AI-Driven Insights Into End-Of-Life Decision- Making: Ethical, Legal, And Clinical Perspectives On Leveraging Machine Learning To Improve Patient AutonomyA nd Palliative Care Outcomes. Migration Letters. 2022;19(6):1159–1172. URL: https://migrationletters.com/index.php/ml/article/view/11497
5. Lekkala S., Avula R., Gurijala P. Big Data and AI/ML in Threat Detection: A New Era of Cybersecurity. Journal of Artificial Intelligence and Big Data. 2022;2(1):32–48. URL: https://www.scipublications.com/journal/index.php/jaibd/article/view/1125
6. Kuraku C., Rajaram S. K., Gollangi H. K., Boddapati V. N., Patra G. K. Advanced Encryption Techniques in Biometric Payment Systems: A Big Data and AI Perspective. Library Progress International. 2024;44(3):2447–2458.
7. Vankayalapati R. K., Sondinti L. R., Kalisetty S., Valiki S. Unifying Edge and Cloud Computing: A Framework for Distributed AI and Real-Time Processing. Journal for ReAttach Therapy and Developmental Diversities. 2023. Green Publication. https://doi.org/10.53555/jrtdd.v6i9s(2).3348
8. Lekkala S. Ensuring Data Compliance: The role of AI and ML in securing Enterprise Networks. Educational Administration: Theory and Practice. 2021;27(4):1272–1279. https://doi.org/10.53555/kuey.v27i4.8102
9. Ibrahim A. L., Fekete I. What machine learning can tell us about the role of language dominance in the diagnostic accuracy of German litmus non-word and sentence repetition tasks. Frontiers in Psychology. 2019;9:2757. doi:10.3389/fpsyg.2018.02757
10. Aldowah H., Al-Samarraie H., Fauzy W. M. Educational data mining and learning analytics for 21st century higher education: a review and synthesis. Telematics and Informatics. 2019;37:13–49. doi:10.1016/ j.tele.2019.01.007
11. Lyon J. Y., Bogodistov Y., Moormann J. AI-driven Optimization in Healthcare: The Diagnostic Process. European Journal of Management Issues. 2021;29(4):218–231. https://doi.org/10.15421/192121
12. Lekkala S., Gurijala P. Leveraging AI and Machine Learning for Cyber Defense. In: Security and Privacy for Modern Networks. Berkeley, CA: Apress; 2024. https://doi.org/10.1007/979-8-8688-0823-4_16
13. Aravind R. Integrating Controller Area Network (CAN) with Cloud-Based Data Storage Solutions for Improved Vehicle Diagnostics using AI. Educational Administration: Theory and Practice. 2024;30(1):992–1005.
14. Pandugula C., Kalisetty S., Polineni T. N. S. Omni- channel Retail: Leveraging Machine Learning for Personalized Customer Experiences and Transaction Optimization. Utilitas Mathematica. 2024;121:389–401.
15. Lekkala S., Gurijala P. Cloud and Virtualization Security Considerations. In: Security and Privacy for Modern Networks. Berkeley, CA: Apress; 2024.
This work is licensed under a Creative Commons Attribution 4.0 International License.
