Feasibility Study of an IoT-Based Digital Twin Platform for Smart Plant Cultivation
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The rising global demand for sustainable agriculture and innovative smart farming technologies has accelerated the development of IoT-based digital twin platforms. As urbanization continues to expand, traditional farming methods face numerous challenges, including limited arable land, unpredictable climate conditions, and inefficient resource utilization. To address these issues, digital twin technology has emerged as a transformative solution, enabling real-time monitoring, predictive analytics, and data-driven decision-making for optimized plant cultivation. This paper presents an in-depth analysis of the system's technical framework, including sensor integration, AI-based decision-making processes, and cloud-based infrastructure. Additionally, it explores the potential applications of digital twin technology in smart farming, assesses the economic feasibility of the platform, and evaluates its market potential. The findings highlight the role of IoT-based digital twin platforms in revolutionizing modern agriculture by bridging the gap between technology and sustainable farming practices. By demonstrating the effectiveness of digital twin applications in plant cultivation, this study contributes to the broader discourse on intelligent agricultural solutions, sustainable food production, and the future of precision farming. The proposed platform offers a scalable, adaptable, and energy-efficient approach to modern agriculture, paving the way for the widespread adoption of AI-driven smart farming technologies.
Abstract
The rising global demand for sustainable agriculture and innovative smart farming technologies has accelerated the development of IoT-based digital twin platforms. As urbanization continues to expand, traditional farming methods face numerous challenges, including limited arable land, unpredictable climate conditions, and inefficient resource utilization. To address these issues, digital twin technology has emerged as a transformative solution, enabling real-time monitoring, predictive analytics, and data-driven decision-making for optimized plant cultivation. This paper presents an in-depth analysis of the system's technical framework, including sensor integration, AI-based decision-making processes, and cloud-based infrastructure. Additionally, it explores the potential applications of digital twin technology in smart farming, assesses the economic feasibility of the platform, and evaluates its market potential. The findings highlight the role of IoT-based digital twin platforms in revolutionizing modern agriculture by bridging the gap between technology and sustainable farming practices. By demonstrating the effectiveness of digital twin applications in plant cultivation, this study contributes to the broader discourse on intelligent agricultural solutions, sustainable food production, and the future of precision farming. The proposed platform offers a scalable, adaptable, and energy-efficient approach to modern agriculture, paving the way for the widespread adoption of AI-driven smart farming technologies.