<![CDATA[Over the past decade, artificial intelligence has experienced phenomenally rapid and extensive expansion across a variety of industries. Along with developments over time, the agricultural sector stands to benefit significantly from the integration of technology. A significant challenge encountered by farmers is selecting the appropriate crop to plant. The selection of crops is influenced by various factors. Despite advancements in agricultural technology, a considerable gap remains in the integration of IoT with large language models (LLM) for delivering context-specific and data-driven plant recommendation. This study evaluates the reliability of plant recommendations produced by Internet of Things (IoT) devices utilizing the Llama 3.2 model. The model will utilize real-time environmental data, including soil pH, altitude, and temperature, to recommend appropriate plant. The recommendations will be compared between base model and fine tune model using precision, recall and f1 metrics and be assessed in relation to established agricultural literature concerning plant compatibility and growth requirements with human evaluation. This research achieved an AUC value that exceeded that of the base model by 10%, Precision exhibited a 25% increase relative to the base model, while recall demonstrated a significant rise of 52% from the base model. The F1 score also improved by 39% compared to the base model.]]>
