Model Building and Efficiency Improvement of Generative AI in Agricultural Planning
DOI:
https://doi.org/10.71222/gkaz3320Keywords:
generated AI, agricultural planning, model building, efficiency improvementAbstract
In recent years, artificial intelligence has been widely used in the field of agriculture, and information content such as data collection has also gradually emerged. This paper outlines the basic concepts of large models and explores the application of AI in agriculture. The adaptability in industry planning is analyzed and the application status is evaluated according to three categories of language model, visual model and multimodal large model. The subsequent development direction is discussed, emphasizing the need for AI-generated intelligent models to enhance agricultural decision-making to improve the management effect and realize the sustainability of agricultural production.
References
1. S. Zürner, L. P. Deutschländer, M. Schieck, and B. Franczyk, "Sustainable development of AI applications in agriculture: A review," Procedia Comput. Sci., vol. 225, pp. 3546-3553, 2023, doi: 10.1016/j.procs.2023.10.350.
2. M. Javaid, A. Haleem, I. H. Khan, and R. Suman, "Understanding the potential applications of Artificial Intelligence in Ag-riculture Sector," Adv. Agrochem., vol. 2, no. 1, pp. 15-30, 2023, doi: 10.1016/j.aac.2022.10.001.
3. K. Jha, A. Doshi, P. Patel, and M. Shah, "A comprehensive review on automation in agriculture using artificial intelligence," Artif. Intell. Agric., vol. 2, pp. 1-12, 2019, doi: 10.1016/j.aiia.2019.05.004.
4. K. Sharma and S. K. Shivandu, "Integrating artificial intelligence and internet of things (IoT) for enhanced crop monitoring and management in precision agriculture," Sensors Int., vol. 2024, p. 100292., doi: 10.1016/j.sintl.2024.100292.
5. D. H. Maulud, S. R. Zeebaree, K. Jacksi, M. A. M. Sadeeq, and K. H. Sharif, "State of art for semantic analysis of natural language processing," Qubahan Acad. J., vol. 1, no. 2, pp. 21-28, 2021, doi: 10.48161/qaj.v1n2a44.
6. G. Yenduri et al., "Generative pre-trained transformer: A comprehensive review on enabling technologies, potential appli-cations, emerging challenges, and future directions," 2023, arXiv preprint arXiv:2305.10435, doi 10.48550/arXiv.2305.10435.
7. Y. Xiong et al., "Temporal scaling law for large language models," 2024, arXiv preprint arXiv:2404.17785, doi: 10.48550/arXiv.2404.17785.
8. N. Sardana, J. Portes, S. Doubov, and J. Frankle, “Beyond Chinchilla-optimal: Accounting for inference in language model scaling laws,” 2023, arXiv preprint arXiv:2401.00448, doi: 10.48550/arXiv.2401.00448.
9. S. S. Sengar, A. B. Hasan, S. Kumar, and F. Carroll, "Generative artificial intelligence: A systematic review and applications," Multimed. Tools Appl., pp. 1-40, 2024, doi: 10.1007/s11042-024-20016-1.
10. Y. Cao et al., "A comprehensive survey of AI-generated content (AIGC): A history of generative AI from GAN to ChatGPT," 2023, arXiv preprint arXiv:230304226, doi: 10.48550/arXiv.2303.04226.
11. Y. Wang, Y. Xi, X. Liu, and Y. Gan, "Exploring the dual potential of artificial intelligence-generated content in the esthetic reproduction and sustainable innovative design of Ming-style furniture," Sustainability, vol. 16, no. 12, p. 5173, 2024, doi: 10.3390/su16125173.
12. L. Shao, B. Chen, Z. Zhang, Z. Zhang, and X. Chen, "Artificial intelligence generated content (AIGC) in medicine: A narrative review," Math. Biosci. Eng., vol. 21, no. 1, pp. 1672-1711, 2024, doi: 10.3934/mbe.2024073.
13. J. Liu et al., "Exploring the integration of digital twin and generative AI in agriculture," in Proc. 2023 15th Int. Conf. Intell. Hum.-Mach. Syst. Cybernetics (IHMSC), pp. 223-228, Aug. 2023., doi: 10.1109/IHMSC58761.2023.00059.
14. C. Zhu, L. Cui, Y. Tang, and J. Wang, "The evolution and future perspectives of artificial intelligence generated content," 2024, arXiv preprint arXiv:241201948, doi: 10.48550/arXiv.2412.01948.
15. F. Assimakopoulos, C. Vassilakis, D. Margaris, K. Kotis, and D. Spiliotopoulos, "Artificial intelligence tools for the agriculture value chain: Status and prospects," Electronics, vol. 13, no. 22, pp. 4362, 2024, doi: 10.3390/electronics13224362.
16. S. Rezayi et al., "Exploring new frontiers in agricultural NLP: Investigating the potential of large language models for food applications," IEEE Trans. Big Data, 2024, doi: 10.1109/TBDATA.2024.3442542.
17. T. Yang, Y. Mei, L. Xu, H. Yu, and Y. Chen, "Application of question answering systems for intelligent agriculture production and sustainable management: A review," Res. Conserv. Recycl., vol. 204, p. 107497, 2024., doi: 10.1016/j.resconrec.2024.107497.
18. G. Kostka, "China—A rising tech power?" in The Emergence of China’s Smart State, p. 199, 2024. ISBN: 9781538184424.
19. S. Yang et al., “ShizishanGPT: An agricultural large language model integrating tools and resources,” in Proc. Int. Conf. Web Inf. Syst. Eng., Singapore: Springer Nature Singapore, Nov. 2024, pp. 284-298. ISBN: 9789819605736.
20. A. Carraro, M. Sozzi, and F. Marinello, "The Segment Anything Model (SAM) for accelerating the smart farming revolution," Smart Agric. Technol., vol. 6, p. 100367, 2023, doi: 10.1016/j.atech.2023.100367.
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Copyright (c) 2025 Mengqiu Shao, Hongkun Liu, Guoqing Cai, Wei Xu (Author)
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