Intelligent tobacco flue-curing method based on leaf texture feature analysis

Most traditional curing systems are manually or half-artificial operated that requiring the curers to observe the state of tobacco leaves frequently. A novel intelligent real-time curing control system is developed in this paper by acquiring the optical image of tobacco leaves and extracting the color features and texture features to predict and control the temperature and humidity of the curing barn. The tobacco leaves changes from green to yellow and shrinks gradually, and this changing regulation would enhance the intelligence of tobacco curing system. The proposed neural network is designed to predict the set-point values of the adjustment of dry-bulb temperature, wet-bulb temperature and the changing time, which has eleven inputs include three color features, three texture features, ideal dry-wet temperature, ideal wet-bulb temperature, current stage, stage passing time, tobacco leaves varieties and flag. Some experiments are induced and the experimental results show this proposed approach based on color features and texture features could improve significantly the accuracy than that of the similar method only using color features especially in post-curing process.