• Husain H. Dawoodi

The rapid advancements in artificial intelligence, particularly in the domain of large language models (LLMs), have opened new avenues for transforming various sectors, including agriculture. LLMs, such as GPT-4, have demonstrated remarkable capabilities in natural language understanding, text generation, and data analysis, making them valuable tools for enhancing agricultural practices. This review explores the diverse applications of LLMs in agriculture, ranging from precision farming, crop disease detection, and weather forecasting to supply chain management, policy recommendations, and farmer advisory systems. By leveraging vast datasets and complex models, LLMs can assist in optimizing resource allocation, improving crop yields, and providing real-time decision support. The paper also highlights the challenges and limitations of implementing these models in rural settings, particularly in terms of data availability, accessibility, and computational infrastructure. This review aims to provide insights into the current state of LLM applications in agriculture and suggests future research directions to harness their full potential for sustainable agricultural development.

Large Language Models, Applications in Agriculture

"HARNESSING LARGE LANGUAGE MODELS FOR AGRICULTURAL INNOVATION: OPPORTUNITIES, CHALLENGES, AND FUTURE DIRECTIONS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 9, page no.e670-e677, September-2024, Available :http://www.jetir.org/papers/JETIR2409484.pdf

"HARNESSING LARGE LANGUAGE MODELS FOR AGRICULTURAL INNOVATION: OPPORTUNITIES, CHALLENGES, AND FUTURE DIRECTIONS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 9, page no. ppe670-e677, September-2024, Available at : http://www.jetir.org/papers/JETIR2409484.pdf