一个循环的人类方法来应用大型语言模型来获得农场管理洞察力
Spyridon Mourtzinis1, Tatiane Severo Silva2, Jason Chor Ming Lo3
1Department of Plant and Agroecosystem Sciences, University of Wisconsin- Madison, 1575 Linden Dr, Madison, WI, 53706, USA. agstat001@gmail.com.
Scientific reports
|December 2, 2025
概括
大型语言模型 (LLM) 显示了将农业研究转化为作物管理建议的潜力. 然而,目前的LLM需要进一步发展,以便为农民提供可靠的,特定领域的建议.
科学领域:
- 农业科学 农业科学
- 人工智能的人工智能
- 信息科学 信息科学 信息科学
背景情况:
- 可持续农业需要将研究转化为实际的农场管理. 知识与实践之间的差距阻碍了采用基于证据的实践.
- 在现有土地上提高农业生产率对于粮食安全至关重要.
- 大型语言模型 (LLM) 为将复杂的研究合成可行的建议提供了一个潜在的途径.
研究的目的:
- 评估LLM在科学文献中产生作物管理建议方面的有效性.
- 评估LLMs在弥合农业知识与实践差距方面的潜力.
- 为了比较一个半自动化的人在循环系统与商业的LLM来生成大豆管理计划.
主要方法:
- 开发了一种半自动化的人在循环管道,用于文学选和推生成,遵循系统审查协议.
- 作为一个案例研究,利用了美国大豆生产.
- 对生成的管理计划进行专家评估.
主要成果:
- 开发的系统在文献选中表现出高准确性,超过独立模型.
- 专家们认为,商业LLM用于一般大豆管理计划的输出比该系统的输出更为有利.
- 该研究确定了对LLM产生的建议的需求,这些建议是值得信赖和特定于领域的.
结论:
- 在综合农业研究方面,LLM是有前途的,但在实际应用中需要改进.
- 发展用户信任和提供量身定制的,可操作的建议对于LLM在农业中的采用至关重要.
- 未来的研究应该集中在提高LLM能力,以产生特定领域的可靠作物管理建议.
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