Recognition and Application of Tobacco Stalk Features in Dynamic Images

For accurate extraction of tobacco stalk features,a method for dynamic recognition of tobacco stalks was proposed based on computer vision technology.A function of image clarity evaluation was constructed at image preprocessing stage,an iterative method was used to extract distinct images and the images were grayed by weighted average method.For diminishing the influences from light source intensity,ghost,terrain and other factors,the grayed images were denoised,and the gray degree of images was intensified to make the features of tobacco stalks more prominent.Finally,self-adaptive algorithm was used to segment image threshold intelligently,the features of tobacco stalks were extracted.The simulation tests of tobacco stalk recognition were conducted with 100 tobacco plants separately under adequate light irradiation,shade,and bumping vehicle conditions.The results showed that:with average recognition rate of 72.67% under the three conditions,the recognition rate under adequate light irradiation was the highest(76%),while that in a bumping vehicle was the lowest(69%).The experiment verified the feasibility of obtaining the features of tobacco stalks in dynamic images,automatic tobacco leaf harvesting could be implemented by combining with a bionic actuator,it is helpful to lowering leaf damage and improving the efficiency of leaf harvesting.