Digital twins represent a transformative advancement in modern agriculture, enabling virtual replication of physical farming systems for real-time monitoring, simulation, and predictive decision-making. This chapter explores the fundamentals of agricultural digital twins, illustrating how they are built using integrated data from IoT devices, drones, weather stations, and sensors. These digital counterparts offer dynamic insights into crop health, soil conditions, and environmental factors, significantly reducing the need for physical inspections and allowing proactive responses to emerging issues. Real-time simulation capabilities enhance predictive analysis, enabling farmers to test different agricultural strategies, forecast potential outcomes, and optimize resource utilization. The chapter emphasizes the role of predictive maintenance and risk assessment, where digital twins can anticipate machinery breakdowns, forecast pest infestations, and assess climatic risks, thus safeguarding productivity. Integration with IoT and biotechnology further strengthens digital twins by feeding continuous data streams and incorporating genetic and microbial information into simulations. Practical applications in farm management are also discussed, including improved planning, precision resource allocation, and sustainable practices. By bridging physical systems with digital intelligence, agricultural digital twins empower data-driven, resilient, and sustainable farming. This chapter underscores their growing importance in addressing global challenges like food security and climate change, positioning them as key enablers of the next generation of smart agriculture.