<![CDATA[Over the past decade, artificial intelligence has experienced phenomenally rapid and extensive expansion across a wide range of industries. Alongside these developments, 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 leverages real-time environmental data, including soil pH, altitude, and temperature, to recommend appropriate plant. The recommendations from the base model and a fine-tuned model were compared using precision, recall and F1-score metrics, and were further assessed against established agricultural literature on plant compatibility and growth requirements through human evaluation. The results show substantial performance improvements. The proposed approach achieved an AUC value 59% higher than that of the base model. Precision increased by 40%, recall improved by 105%, and the F1 score rose by 80% compared to the base model.]]>
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