<title>Application of fuzzy logic to feature extraction from images of agricultural material</title>

Abstract

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 thresholdmg 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 thresholdmg based on image shape and compactness are applied to this application.

<title>Measurement of stem content for control purposes in a green leaf threshing plant</title>

“…Algorithms that extract features from anywhere in the image have greater utility than those looking in one specific place. The algorithms, whose origins include military and medical applications, use fuzzy logic, neural networks or other non-linear image processing techniques [6][7][8][9] . The disadvantages of these techniques are they require large data sets for training or the image processing time is too long to make them an effective tool for process monitoring.…”