Enhancing Agriculture Prediction through AI and Parallel Distributed Computing: A Comprehensive Study on the Impact of Weather

Abstract

This revolutionary study focuses on incorporating Artificial Intelligence (AI) and Parallel Distributed Computing (PDC) in agriculture prediction, with a specific interest in weather patterns. Modern technologies are instrumental in providing solutions to enhance agricultural efficiency and sustainability. Results from this research show that PDCs and AI have the potential to make crop yield predictions more accurate, detect and prevent diseases, and allocate resources efficiently. This paper emphasizes accurate weather forecasts for sustainable farming practices while considering multiple factors involved in decision-making processes according to expert opinions. These hybrid computing approaches are highly effective in generating detailed spatiotemporal information about agricultural systems, improving agricultural predictions' accuracy. Therefore, the study has far-reaching implications for industry players seeking to improve productivity using data-based techniques for sustainability purposes.