The increasing application of artificial intelligence in agriculture production and management has generated a large amount of data, leading to a demand for processing this data. This review focuses on the knowledge storage approaches in agricultural question answering systems, namely corpora, knowledge graphs, and large language models. These systems are built on massive amounts of data and aim to process and retrieve information effectively in the context of sustainable agriculture. Corpora refer to large collections of diverse documents that serve as foundational resources for training and fine-tuning question answering systems. Knowledge graphs capture structured and interconnected knowledge by representing entities, relationships, and attributes, enabling efficient organization and querying of information. Large language models, such as GPT-4, enhance the capacity of question answering systems to provide accurate and relevant responses. By exploring these three prominent knowledge storage approaches, this review analyses the methodology and impact of agricultural question answering systems, highlighting their applications in the production process. The findings provide important implications for future research in agriculture, and potential directions for further exploration.

中文翻译：

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问答系统在智能农业生产和可持续管理中的应用：综述

人工智能在农业生产和管理中的应用日益广泛，产生了大量的数据，产生了对这些数据进行处理的需求。本综述重点关注农业问答系统中的知识存储方法，即语料库、知识图和大语言模型。这些系统建立在大量数据的基础上，旨在在可持续农业的背景下有效地处理和检索信息。语料库是指作为培训和微调问答系统的基础资源的大量不同文档的集合。知识图通过表示实体、关系和属性来捕获结构化和互连的知识，从而实现信息的高效组织和查询。大型语言模型（例如 GPT-4）增强了问答系统提供准确且相关响应的能力。通过探索这三种著名的知识存储方法，本文分析了农业问答系统的方法和影响，重点介绍了它们在生产过程中的应用。这些发现为未来农业研究提供了重要意义，并为进一步探索提供了潜在方向。