Tobacco Leaf Detection System Research And Application Based On Image Processing And Artificial Neural Network

The tobacco industry plays an important role in China's national economy. Classification of tobacco leaf is a basic work in tobacco industry. It is the key to improve the quality of tobacco products that accurately classifying appearance quality of flue-cured tobacco leaves according to national standards, it also has the important effect in reducing the harm of smoking on the body and ensuring the rational distribution of economic benefits in agricultural, industrial and commercial production links.The inspection and classification of tobacco leaves in tobacco industry at home and abroad are based on grading standards, determined empirically by relying on people's senses. In recent years, our school's research team used computer image processing and pattern recognition technology for feature extraction and automatic leaf grading and made a series of achievements. On the basis of that team's preliminary studies this paper made the following findings:1. Study on tobacco leaf's maturity, oil detection model based on neural network Tobacco leaf's maturity and oil which are two important indicators of tobacco leaf classification are unable to be directly quantified and extracted. In this paper, according to the relationship between maturity and oil of flue-cured tobacco leaves with appearance factors already extracted, through the experiment we selected appropriate appearance factors to compose feature vector, established two single - target classification model of maturity and oil by using of back-propagation network and probabilistic neural network, tested the identifying accuracy of classifying models, compared the performance of the model set up by two networks, confirmed the applying superiority of the probabilistic neural network in maturity and oil classifying model.2. Study on algorithm of tobacco leaf shape based on invariant moments In this paper we extracted two invariant moment features of flue-cured tobacco image, applied invariant moments as new characters in the maturity and oil network classifying model. Through the experimental comparison, the complement of invariant moment features did not serve to improve the maturity model but improve the recognizing accuracy of oil modal.3. Study on identifying the growing area of tobacco leaves based on support vector machine the base of classifying tobacco leaves is determining the growing area in tobacco plants, then classify the tobacco leaf according to different groups. In this paper, support vector machine had been used to establish the grouping modal of tobacco leaves. The accurate rate of grouping increased.The results show that using image processing, artificial neural network technology in feature extraction of tobacco leaves and establishing grouping and classifying modal has the feasibility, it is going to play a good supporting role in areas of formulating and improving quality standards of tobacco leaves, verifying and arbitrating quality of tobacco leaves and training classifying staff of tobacco leaves.