AI-IOT based digital twin framework for autonomous smartagriculture

Abstract

Contemporary farming faces unprecedented challenges, including fluctuating weather patterns, limited resources, inefficiencies, and the lack of real-time intelligent farm management. Although AI, IoT, and the implementation of Digital Twin technologies have been utilized individually within smart farming, existing systems primarily focus on isolated tasks, such as irrigation automation, disease detection, or single-component simulation. Currently, no integrated framework combines multi-source data fusion, self-learning AI models, and real-time digital farm representation to simultaneously manage and simulate multiple agricultural processes, which is a clear and critical research gap. This study proposes an AI-IoT Digital Twin Framework for Autonomous Smart Agriculture that directly addresses this gap. IoT sensor networks continuously collect multi-source field data, including soil conditions, humidity, temperature, and crop health, which are supplemented by drone imagery and satellite observations. This data is fed into a cloudbased digital twin that mirrors the physical farm in real time. Embedded self-learning AI and Machine Learning algorithms autonomously detect crop stress, predict irrigation needs, and generate optimized farm recommendations that improve with each growing cycle. A predictive simulation layer enables farmers to evaluate different strategies virtually before real-world implementation, thereby reducing operational risk. Experimental evaluation demonstrated an irrigation efficiency improvement of 34%, yield prediction accuracy of 91.2%, and crop stress detection rate of 88.6% compared to conventional systems. This framework establishes a scalable foundation for next-generation autonomous, intelligent, and sustainable precision-agriculture systems.