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...

Full description

Saved in:
Bibliographic Details
Published inJournal of engineering and sustainable development (Online) Vol. 29; no. 4; pp. 416 - 421
Main Authors Abu Bakar, Badril, Baharom, Siti Noor Aliah, Ahmad, Mohd Taufik, Zubir, Mohd Nizam, Ahmad Sayuti, Adli Fikri, Mhd Bookeri, Mohd Aufa
Format Journal Article
LanguageEnglish
Published Mustansiriyah University/College of Engineering 01.07.2025
Subjects
Agriculture
Generative artificial intelligence
Large language model
Pineapple cultivation
Retrieval augmented generation
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:2520-0917
2520-0925
DOI:10.31272/jeasd.2732