Tobacco Leaf Maturity Classification Based on Extreme Learning Machine

For improving the efficiency of grading and the quality of tobacco leaf and reducing the labor intensity of operators,a leaf maturity classifying method based on extreme learning machine was proposed. Firstly,the image of tobacco leaf was equally divided into 4 blocks after being normalized;secondly,principal component analysis (PCA) was conducted to reduce the dimension of the extracted characteristics;finally, extreme learning machine was adopted to identify leaf maturity. The results of simulation experiment showed that the precision of test reached 96.43% when extreme learning machine was applied. Extreme learning machine could identify the maturity of tobacco leaf rapidly and accurately,it was of potential practical uses and was better than support vector machine and BP neural network in terms of training speed and generalization.