A Review on Prompt Engineering in Agriculture
Abstract
The integration of Artificial Intelligence (AI) in agriculture is transforming farming practices by enhancing productivity, sustainability, and decision-making. A critical aspect of this transformation is prompt engineering—the art of crafting effective input prompts to guide large language models (LLMs) such as ChatGPT, Bard, and Claude in generating tailored and actionable outputs. This review paper presents an extensive analysis of prompt engineering techniques in agriculture, detailing advanced methods (few-shot, chain-of-thought, role based, and dynamic prompting), practical applications across crop management, soil analysis, pest control, and supply chain optimization, as well as integration with emerging technologies like IoT, blockchain, and robotics. We also discuss global case studies, challenges (data quality, economic constraints, ethical issues), and future prospects. The review integrates foundational articles from Intellias, Medium, and MDPI alongside additional scholarly sources to provide a holistic view of the field.
Keywords:
Blockchain, Prompt Engineering, Artificial Intelligence, Agriculture, Large Language Models, Precision Farming, Iot
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