Application of question answering systems for intelligent agriculture production and sustainable management: A review

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.