The purpose of a green leaf threshing (GLT) plant is to separate the tobacco leaf from the stem. In the GLT process, there are over a dozen parameters to adjust for optimal performance. The current laboratory measurement technique is destructive, laborious and not as well suited for optimizing processing parameters as an on-line technique. A machine vision technique was developed to non-destructively measure the amount of stem in the stream of processed tobacco leaf. The machine vision technique employs novel lighting to collect images and image processing to measure the amount of stem in the image. The image processing combines fuzzy logic and unique morphological image processing to extract the leaf from the background and then the stem present in the leaf. Performance of the optical technique compared favorably with the lab method. Correlation coefficient (R<SUP>2</SUP>) was greater than 0.95 and relative error (%RSD) was less than 6%.
Imaging technology has extended itself from performing gauging on machined parts, to verifying labeling on consumer products, to quality inspection of a variety of man-made and natural materials. Much of this has been made possible by faster computers and algorithms used to extract useful information from the image. In the application of agricultural material, specifically tobacco leaves, the tremendous amount of natural variability in color and texture creates new challenges to image feature extraction. As with many imaging applications, the problem can be expressed as `I see it in the image, how can I get the computer to recognize it?' In this application, the goal is to measure the amount of thick stem pieces in an image of tobacco leaves. By backlighting the leaf, the stems appear dark on a lighter background. The difference in lightness of leaf versus darkness of stem is dependent on the orientation of the leaf and the amount of folding. Because of this, any image thresholding approach must be adaptive. Another factor that allows us to identify the stem from the leaf is shape. The stem is long and narrow, while dark folded leaf is larger and more oblate. These criteria under the image collection limitations create a good application for fuzzy logic. Several generalized classification algorithms, such as fuzzy c-means and fuzzy learning vector quantization, are evaluated and compared. In addition, fuzzy thresholding based on image shape and compactness are applied to this application.