Research on Innovative Applications of Generative Artificial Intelligence in Agricultural Informatization

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.