Enhancing Named Entity Recognition for Agricultural Commodity Monitoring with Large Language Models

Agriculture, as one of humanity's most essential industries, faces the challenge of adapting to an increasingly data-driven world. Strategic decisions in this sector hinge on access to precise and actionable data. Governments, major agriculture companies, and farmers have expressed a need for worldwide monitoring of crop commodity quantities and prices. However, the complex and diverse nature of agricultural data and crop commodities, often presented in unstructured formats, pose significant challenges in extracting meaningful insights. This study delves into the effectiveness of Large Language Models, particularly in Named Entity Recognition, focusing on their ability to efficiently tag and categorize crucial information related to agriculture, vessel tracking, imports, and exports, thereby enhancing data accessibility. Our results indicate that while fine-tuning a base model achieves high precision, Large Language Models, particularly GPT-4 and Claude v2, demonstrate comparable performance with the added benefit of requiring no additional training for new entity recognition. This research highlights the promising role of Large Language Models in agricultural AI, suggesting their use as a scalable solution for real-time data analysis and decision support in agriculture.