An Agricultural Electrical Automation Control System Based on Decision Modeling

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Weifeng Peng, Zhouya Zhang


The integration of electrical automation control systems with decision modeling techniques has revolutionized modern agriculture, ushering in an era of unprecedented efficiency, precision, and sustainability. This abstract encapsulates the essence of such systems, elucidating their fundamental principles, applications, and transformative impact on farming methodologies. At its core, decision modeling harnesses sophisticated algorithms and predictive analytics to optimize farming operations. By leveraging data-driven insights, farmers can make informed decisions regarding irrigation, fertilization, pest control, and other critical aspects of crop management. Furthermore, decision models enable adaptive responses to dynamic environmental conditions, ensuring resilience in the face of uncertainties such as climate change and market fluctuations. Complementing the prowess of decision modeling is the integration of electrical automation control systems, encompassing technologies such as sensors, actuators, programmable logic controllers (PLCs), and supervisory control and data acquisition (SCADA) systems. These systems facilitate precise control over agricultural processes, optimizing resource utilization and minimizing wastage through real-time monitoring and seamless connectivity. The advent of the Internet of Things (IoT) and cloud computing has further propelled agricultural automation, enabling remote access, data analytics, and machine learning capabilities. This convergence of technologies not only enhances productivity and efficiency but also fosters sustainability by reducing the ecological footprint of farming practices. This abstract offers a glimpse into the transformative potential of agricultural electrical automation control systems based on decision modeling, paving the way for a more resilient, productive, and sustainable future in agriculture.

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