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The micro-section image analysis is an effective method to measure cotton fiber maturity directly. However, the accuracies of the existing image segmentation and contour extraction algorithms were limited by the extraction of cotton fiber cross-section features. Therefore, the paper first binarized the cotton fiber cross-section micro image with the image processing software programmed by VC++, and then extracted the contours of its outer layer, inner layer and cavity, and finally calculated its geometric features. Based on statistics of these geometric features, a judgment model was established to remove the low quality cross sections such as pseudo-cross sections and separate adhesion cross sections, so as to improve the geometric feature extraction effect and the maturity estimation accuracy of cotton fiber cell. The results showed that, compared with the artificial subjective judgment, the cotton fiber cross-section feature extraction method proposed in this paper could not only solve the problem of unable to determine the geometric characteristics caused by cotton fiber adhesion and other interference factors, but also reduce the identification error rate from 0.073 to 0.038, so as to better judge the quality of cotton fiber at harvest time, and infer the possible influencing factors.

Extraction Method of Microscopic Image Feature of Cotton Fiber Cross Section