A Study of the Application Domain of a Large Language Models in the Agricultural Sector

Abstract

Given the expanding global population and the increasing need for food, employing effective agricultural techniques to enhance productivity on finite land resources is imperative. Artificial Intelligence is increasingly widespread in agriculture, and Artificial Intelligence driven solutions enhance the existing farming system. Agricultural productivity relies on soil nutrient composition, moisture levels, crop rotation, precipitation, temperature, etc. Artificial intelligence-based products can utilize these characteristics to monitor agrarian productivity. Industries are increasingly adopting Artificial Intelligence technologies to enhance and streamline agricultural activities across the whole food supply chain. Agricultural applications and solutions utilizing artificial intelligence have been developed to support farmers in precise and controlled farming practices. These applications provide accurate guidance on water management, changing crops, timely harvesting, crop selection, optimal planting, pest control, and nutrition management. Artificial Intelligence enabled systems utilize data such as precipitation, wind speed, temperature, and sun radiation, together with images captured by satellites and drones, to compute weather forecasts, monitor the sustainability of agriculture, and evaluate farms for the existence of infectious illnesses, pests, or undernourished plants. A large language model is a form of artificial intelligence that employs deep learning techniques to analyse and comprehend natural language. It is trained on extensive text datasets to discern statistical correlations between words and phrases. Subsequently, it may produce text, translate material, and execute other natural language processing operations. This research demonstrates how large language models emphasize the agricultural industry.