Study On Model Of Fresh Tobacco Leaf's Water Contents Based On Machine Vision

To judge the fresh tobacco leaf's water contents quickly, simply, conveniently and accurately, this paper does some meaning exploration, using modern computer vision technology and combining with local economic construction. In the process of flue-cured tobacoo, fresh tobacco leaf's water contents have near relationships with tobacco leaf's flue-cured characteristic. It is the important foundation to choose the optimization flue-cured mode and establish the proper technics and flue-cured procedure. Compared with hang discretionarily, if the wet leaf which have different water contents is placed in hot liquid which have different temperature in order to hang orderly ,it does not only the time will be shorter but also the problem that is hard to flue-cured when the leaf contain much water and the character of flue-cured is bad when the leaf contain less water will be solved. In addition, the efficiency and flue-cured quality and the proportion of high quality tobacco will be improved, increasing the mean value of tobacco, decreasing the wasting of coal and other energy sources, which have dramatically economical value.If detected by manpower, the intensity of work is dense, the subjectivity is big and can be easily disturbed.The work efficiency is low, the classification standard is hard to master and also the classification precision is hard to be stabilized. The technician and tobacco farmer was easily influenced by subjective (personal experience) factor even if they have abundance experience and were well trained, and many index take on illegible state, which can not be used to judge the leaf water contents exactly. This paper extract many characters non-destructive and quickly involved with water contents from 160 wet tobacco leaf sample gathered in land, set up a non-linear judge model about leaf water contents using Elman neural network . By which, the leaf 's real water contents can be detected.To improve the precision of the system, engageing in theory study about key technology with system construction, image preprocessing,image feature extraction, algorithm choice and so on,engaging in image segmentation between the image and the background by MATLAB.The leaf have been filtered by Butterworth filter which does not have "flap bell" phenomenon and have uniform characteristic, design the algorithm about color,vein and outline to draw image characters by MATLAB, extract fresh tobacco leaf's color,vein and outline as the synthetical judgment indexes, in this way ,to reduce single index's limitation. Rank the indexes synthetically by the three-demarcation AHP method, The analytical conclusion indicate that the leaf's width,acreage and mean value take a biggish ratio in the whole judgment system, can be used as the swift judgment indexes of water contents,seting up three layers neural network, taking the three indexes as the input, the water contents as the output. Train the network by learngdm. Predict the water contents of testing sample using the trained network. The relative error between the test sample value and the real value has been controlled below 10%, the predicting precision exceed 90%, the predicting results achieve anticipating goals.Design a professional instrument which can be used repeatedly and operated simply with the image processing emulational platform of the matlab's GUI, provide function in image input\image enhancement\image analysis and outputting water contents of fresh tobacco leaf, can predict the inputting fresh tobacco leaf's water contents according to the above model.The research indicate that this nondestructive detecting technique is feasible, the detecting result is accurate, which is the nicer foundation to establish the tectonics and procedure of flue-cured tobacco leaf and to choose the best flue-cured model that fit with different flue-cured curve in later research. In additional, it can be used as the foundation of field irrigation and find out the method about the leaf's nutritional information simply and exactly, according to the interrelated characteristic data about the leaf and the leaf's nutritional indexes, such as the contents of nitrogen\ phosphor.