Digital Twins of Farms for Optimized Resource Management

Abstract

Sustainable agriculture is increasingly challenged by climate variability, water scarcity, and declining soil fertility, which together call for intelligent digital tools that support efficient farm management. This paper presents a Digital Twin framework designed to mirror the physical state of a farm using continuously updated data streams from Internet of Things (IoT) sensors, weather stations, and soil-monitoring equipment. The framework combines real-time synchronization between the physical field and its virtual counterpart with machine learning models that forecast irrigation needs, nutrient requirements, and environmental changes, allowing farmers to plan resource use before conditions become critical. Beyond monitoring, the virtual model supports scenario testing, letting decision-makers evaluate alternative management strategies without disrupting ongoing operations. Simulation-based evaluation of the proposed system shows marked improvements over conventional and IoT-only approaches: water use falls by 45%, fertilizer efficiency rises by 52%, energy consumption drops by 41%, and crop yield improves by 38%, while the predictive module reaches 95.4% accuracy in forecasting resource needs. These results indicate that coupling digital twin modeling with machine learning offers a practical route toward reduced operating costs and more sustainable resource use in precision agriculture.