Digital Twin-Based Crop Yield Prediction in Agriculture-Reference-Cited by-同舟云学术

Digital Twin-Based Crop Yield Prediction in Agriculture

Published:2024-02-02 Issue: Volume: Page:99-110
ISSN:2327-3275
Container-title:Advances in Business Information Systems and Analytics
language:
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Author:

Rajeswari D.¹^ORCID,Venkatachalam Parthiban Athish²^ORCID,Ponnusamy Sivaram³^ORCID

Affiliation:

1. Department of Data Science and Business Systems, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India

2. Clemson University, USA

3. Sandip University, Nashik, India

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.

Publisher

IGI Global

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