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在农业中利用大视野和语言模型:一篇回顾

Hongyan Zhu^1,2, Shuai Qin^1,2, Min Su^1,2

¹Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin, China.

Frontiers in plant science

|September 18, 2025

概括

大型模型为农业提供了变革的潜力,解决了诸如害虫和效率等挑战. 在问答,自动化和图像分析方面的应用虽然代表性不足,但在提高作物产量和可持续性方面表现有前途.

科学领域:

农业科学农业科学
人工智能的人工智能
计算机科学计算机科学

背景情况:

农业面临着关键的挑战,包括害虫控制,疾病管理,以及提高生产效率的需求.
大型模型,包括大型语言模型 (LLMs),大型视觉模型 (LVMs) 和多式大型语言模型 (MLLMs),在各种科学和工业领域展示了显著的潜力.

研究的目的:

系统地审查大型模型的开发和能力.
探索大型模型在农业中的潜在应用,以应对现有挑战并提高生产效率.
确定农业部门中大型模型的潜在和当前应用之间的差距.

主要方法:

对大型模型开发和能力的系统文献审查.
从科学网络和arXiv. 来进行研究的图书统计分析.
量化当前的研究重点,并确定农业的应用差距.

主要成果:

农业是一个新兴但代表性不足的大型模型研究领域.
确定有前途的应用包括农业问题答案,机器人自动化和使用遥感和光谱数据进行先进的图像分析.
这些应用程序显示出解决复杂农业任务的潜力.

结论:

量身定制的大型模型可以显著提高农业效率和产量.
充分发挥潜力需要克服数据采集,基础设施和道德方面的挑战.
提出了一个务实的框架,以指导基于数据可用性和部署约束的大型和传统模型之间的选择.

关键词:

农业农业农业农业计算机视觉计算机视觉大型模型大型模型多式联络模式的模型自然语言处理自然语言处理.

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