Large Language Models (LLMs) are among the most significant advancements in artificial intelligence (AI), offering powerful capabilities in natural language understanding and generation. Their applications have expanded into various domains, including agriculture, where they are accelerating the transition to smart agriculture. This book presents a comprehensive review of the concepts, applications, techniques, and challenges related to the use of LLMs in the agricultural sector. General-purpose large-scale models—such as GPT, Claude, Gemini, and LLaMA—can process text, images, and sensor data, and can be applied to tasks such as expert advisory, intelligent crop management (including plant growth monitoring, disease and pest detection, irrigation and fertilization optimization, and yield prediction), agricultural big data analysis, documentation automation, and question-answering systems. However, the need for domain-specific expertise often necessitates the adaptation of these models. In response, specialized models—such as AgriVLM, AgriBERT, KALLM, and PLLaMa—have been developed or fine-tuned for agricultural use. Key techniques to improve LLM performance in this domain include fine-tuning or pre-training with agricultural datasets, using retrieval-augmented generation (RAG) to incorporate specialized knowledge, integrating knowledge graphs, employing vision-language models for multimodal data analysis, and leveraging Internet of Things (IoT) systems. Beyond technical approaches, robust evaluation of LLM performance in agriculture is critical for establishing trust and ensuring practical viability. This includes metrics such as accuracy, precision, F1 score, text generation quality (e.g., BLEU, ROUGE, and BERTScore), resource efficiency, benchmarking, and comparisons with expert evaluations. Despite their promise, LLMs face challenges such as limited high-quality agricultural data, the risk of misinformation, lack of deep domain expertise in general models, high computational costs, concerns about interpretability and reliability, potential biases, data privacy issues, and labor implications. Nonetheless, LLMs offer transformative potential—enhancing productivity, improving crop yields, promoting sustainability, and enabling data-driven decision-making in agriculture. Continued research into domain-specific model development, integration with complementary technologies, addressing existing limitations, and exploring ethical and social impacts remains essential.

Javad Khanifar, Ph.D., is an expert in soil science and engineering, geomorphometry, remote sensing, artificial intelligence, and spatial data analysis. His research is centered on the application of innovative technologies in earth sciences and agriculture. For more information about his scientific and research background, as well as to access his other works, please visit his personal website at www.drjk.ir.