Improving Farm Management and Yield Prediction With Digital Twins in Precision Agriculture

Abstract

Using digital twin technology into precision farming is a significant first toward the development of agricultural techniques more intelligent and less harmful to the environment. Basically, virtual copies of real-time data from sensors, satellites, and artificial intelligence models, digital twins aim to copy, monitor, and enhance agricultural activity by means of synchronizing real-time data. This paper investigates utilizing a comprehensive literature review, simulated trials, and statistical validation using analysis of variance (ANOVA) the possibilities of digital twins to enhance farm management and production prediction. The results reveal in terms of agricultural productivity, resource efficiency, and prediction accuracy that digital twin systems outperform both traditional techniques and solutions based on the Internet of Things. Notable gains also came in important performance measures like water use, decision-making time required, and input cost savings. The results underscore the importance of digital twins driven by data in the agricultural field.