Retrieval Augmented Generation Data Query Technique for Pineapple Cultivation

Generative artificial intelligence is advancing at a blistering pace. Large Language Models, in particular, have sped up the development of machine learning applications. This work presents a large language model-based technique to query data collected during MD2 pineapple crop production. Retrieval Augmented Generation was used to feed structured and unstructured data to two large language models (GPT-4 and LLAMA2) to train and fine-tune the models. The performance of the models was then measured using actual and predicted question-answer pairs. Results showed that the models had a 78% - 87% correct answer rate for structured and 75% - 79% correct answer rate for unstructured data. However, results showed that the models had a 61%-68 % correct answer rate when an answer to a question needed to refer to structured and unstructured data. These results showed that large language models can be further investigated to give farmers useful insights when making crop management decisions.