Digital Twin-Based Crop Yield Prediction in Agriculture

Abstract

This chapter explores the transformative integration of digital twin technology and drone-based solutions in agriculture, focusing on the innovative Digital Twin Empowered Drones (DTEDs) system for predicting crop yields. The chapter delineates the workflow involving continuous data collection from IoT-based sensors and drones, feeding into a digital twin model, and utilizing advanced AI algorithms like YOLO V7 for real-time analysis. The system aims to enhance predictive capabilities, optimize resource utilization, monitor crop health, and provide data-driven decision support. Results indicate a remarkable prediction accuracy of 91.69%, showcasing the system's potential to revolutionize agriculture, empower farming communities, and contribute to global food security. The chapter concludes by outlining potential future enhancements and advancements, positioning the digital twin-based crop yield prediction system as a significant stride towards efficient and sustainable agricultural practices.