Year: 2025
Type: article
Abstract: 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.
DOI: 10.31272/jeasd.2732
Authors: Badril Abu Bakar, Siti Noor Aliah Baharom, Mohd Taufik Ahmad, Mohd Nizam Zubir, Adli Fikri Ahmad Sayuti, Mohd Aufa Mhd Bookeri
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $50
OpenAlex ID: W4411913029