Research Article
Open Access
Research on Innovative Applications of Generative Artificial Intelligence in Agricultural Informatization
1
College of Big Data Science, Jiangxi Institute of Fashion Technology, Nanchang 330201, China
2
Key Laboratory of Big Data for Apparel in Nanchang, Jiangxi Institute of Fashion Technology, Nanchang 330201, China
3
College of Computer Science, Sichuan University, Chengdu 610065, China
Corresponding Author:
Yiyang Li,
[email protected]
Volume 2, Issue 1
2026
Received: 01 February 2026
Accepted: 05 March 2026
Published: 16 March 2026
Article Information
Abstract
The integration of information technology into agriculture is fundamental to modern agricultural development. However, traditional agricultural information models, which rely on analytical AI for prediction and monitoring, face significant limitations in handling unstructured data, generating actionable knowledge, and supporting complex decision-making in dynamic farm environments. This study's core innovation lies in constructing a “Generative AI-Driven Agricultural Informatization Framework” (GAAIF), which quantifies the synergistic mechanisms between generative models and specific agricultural scenarios. By introducing a multi-modal data fusion engine and a task-specific fine-tuning protocol, a suite of generative AI tools, including a textile crop (cotton) pest advisory chatbot and a dynamic supply chain optimizer, was developed. Field tests and simulations in the Xinjiang cotton basin and Jiangxi sericulture regions showed that the integrated solution substantially improved pest management efficiency (decision time reduced by approximately 87%), reduced supply chain losses by 32.5%, and increased farmer advisory service satisfaction from 55% to 94% compared with traditional decision-support systems. This research provides a systematic technical paradigm and implementation strategy for the next generation of agricultural intelligence, with particular relevance to the textile raw material sector.
Graphical Abstract
Keywords
generative artificial intelligence
agricultural information
smart agriculture
large language models
digital twin
textile crop agriculture
Data Availability Statement
Data will be made available on request.
Funding
This work was supported by the 2025 Ministry of Education Humanities and Social Sciences Research Planning Fund Project: "A New Paradigm in Software Engineering Education Driven by Generative Artificial Intelligence: Research on Theoretical Framework and Practical Pathways" under Grant 25YJAZH204. The authors would like to thank the agricultural cooperatives in Xinjiang and Jiangxi for their invaluable collaboration and participation in the field trials.
Conflicts of Interest
The authors declare no conflicts of interest.
AI Use Statement
The authors declare that no generative AI was used in the preparation of this manuscript.
Ethical Approval and Consent to Participate
Not applicable.
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APA Style
Wei, Y., Li, Y., Xu, Z., Wang, B., & Zhang, G. (2026). Research on Innovative Applications of Generative Artificial Intelligence in Agricultural Informatization. Digital Intelligence in Agriculture, 2(1), 19–31. https://doi.org/10.62762/DIA.2026.926094
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TY - JOUR AU - Wei, Yongqiang AU - Li, Yiyang AU - Xu, Zhaoxing AU - Wang, Bin AU - Zhang, Gesen PY - 2026 DA - 2026/03/16 TI - Research on Innovative Applications of Generative Artificial Intelligence in Agricultural Informatization JO - Digital Intelligence in Agriculture T2 - Digital Intelligence in Agriculture JF - Digital Intelligence in Agriculture VL - 2 IS - 1 SP - 19 EP - 31 DO - 10.62762/DIA.2026.926094 UR - https://www.icck.org/article/abs/DIA.2026.926094 KW - generative artificial intelligence KW - agricultural information KW - smart agriculture KW - large language models KW - digital twin KW - textile crop agriculture AB - The integration of information technology into agriculture is fundamental to modern agricultural development. However, traditional agricultural information models, which rely on analytical AI for prediction and monitoring, face significant limitations in handling unstructured data, generating actionable knowledge, and supporting complex decision-making in dynamic farm environments. This study's core innovation lies in constructing a “Generative AI-Driven Agricultural Informatization Framework” (GAAIF), which quantifies the synergistic mechanisms between generative models and specific agricultural scenarios. By introducing a multi-modal data fusion engine and a task-specific fine-tuning protocol, a suite of generative AI tools, including a textile crop (cotton) pest advisory chatbot and a dynamic supply chain optimizer, was developed. Field tests and simulations in the Xinjiang cotton basin and Jiangxi sericulture regions showed that the integrated solution substantially improved pest management efficiency (decision time reduced by approximately 87%), reduced supply chain losses by 32.5%, and increased farmer advisory service satisfaction from 55% to 94% compared with traditional decision-support systems. This research provides a systematic technical paradigm and implementation strategy for the next generation of agricultural intelligence, with particular relevance to the textile raw material sector. SN - 3069-3187 PB - Institute of Central Computation and Knowledge LA - English ER -
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@article{Wei2026Research,
author = {Yongqiang Wei and Yiyang Li and Zhaoxing Xu and Bin Wang and Gesen Zhang},
title = {Research on Innovative Applications of Generative Artificial Intelligence in Agricultural Informatization},
journal = {Digital Intelligence in Agriculture},
year = {2026},
volume = {2},
number = {1},
pages = {19-31},
doi = {10.62762/DIA.2026.926094},
url = {https://www.icck.org/article/abs/DIA.2026.926094},
abstract = {The integration of information technology into agriculture is fundamental to modern agricultural development. However, traditional agricultural information models, which rely on analytical AI for prediction and monitoring, face significant limitations in handling unstructured data, generating actionable knowledge, and supporting complex decision-making in dynamic farm environments. This study's core innovation lies in constructing a “Generative AI-Driven Agricultural Informatization Framework” (GAAIF), which quantifies the synergistic mechanisms between generative models and specific agricultural scenarios. By introducing a multi-modal data fusion engine and a task-specific fine-tuning protocol, a suite of generative AI tools, including a textile crop (cotton) pest advisory chatbot and a dynamic supply chain optimizer, was developed. Field tests and simulations in the Xinjiang cotton basin and Jiangxi sericulture regions showed that the integrated solution substantially improved pest management efficiency (decision time reduced by approximately 87\%), reduced supply chain losses by 32.5\%, and increased farmer advisory service satisfaction from 55\% to 94\% compared with traditional decision-support systems. This research provides a systematic technical paradigm and implementation strategy for the next generation of agricultural intelligence, with particular relevance to the textile raw material sector.},
keywords = {generative artificial intelligence, agricultural information, smart agriculture, large language models, digital twin, textile crop agriculture},
issn = {3069-3187},
publisher = {Institute of Central Computation and Knowledge}
}
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