A review of modern intelligent and energyefficient tobacco curing system

Abstract

The article reviews the core technological components that enable intelligent and energy-efficient control of modern tobacco curing systems. Traditional flue-curing is a highly energy-intensive process relying heavily on subjective manual control, leading to high fuel consumption and variable leaf quality. This review focuses on the integration of Machine Vision (MV), specifically Convolution Neural Networks (CNN), automated leaf recognition and Fuzzy Logic Control for precise barn parameters, that is temperature and humidity. The Specific Energy Consumption (SEC) is analysed. The paper concludes that intelligent systems offer a significant pathway to minimize operational cost ensuring uniform, high-quality cured tobacco.