AI-Driven Digital Twins: Redefining Agricultural Science in the Era of Intelligent Systems
DOI:
https://doi.org/10.32350/sir.94.06Keywords:
agriculture, artificial intelligence, AI modeling, data driven digital technologies, digital twins, intelligent systemsAbstract
One of the most promising and modern technologies that has the potential to enhance agricultural productivity, sustainability, and management is the digital twin technology. A digital twin is the digital representation of a physical system that holds real-time data from sensors and monitoring devices to constantly update itself. A digital twin can be utilized by the farmers to evaluate agricultural procedure, crop conditions, and determine potential outcome Prior to making decisions about field-based management. This research determines the application of the digital twin technology in agriculture, emphasizing how it integrates data analytics, remote sensing technologies, artificial intelligence (AI), and the Internet of Things (IoT). IoT sensors collect environmental and crop data in real time and send it over communication networks to digital processors that create virtual models. Machine learning (ML) system make it possible to analyze data for crop monitoring, disease detection, irrigation control, and predictive modeling. Drones and remote sensing technologies provide geographic data that enhances the precision of digital twin models and assist how digital twins might improve sustainable farming methods, precision farming and optimize resources. It also reduces the difficulties of adopting digital twin systems in agriculture, including data integration, infrastructure requirements and system complexity. Overall, digital twin technology provides the basis for data-driven decision-making, with the potential to change traditional farming into an intelligent and sustainable agricultural system. The current research aims to critically evaluate the revolutize potential of AI-driven digital twin technology in agriculture, exploring how intelligent systems revolutionize crop and animal management and analyze the socio-technical factors influencing equitable adoption
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Copyright (c) 2025 Madieha Ambreen, Muhammad Talha Hassan, Maryam Khalid
This work is licensed under a Creative Commons Attribution 4.0 International License.
