A novel method for seed cotton color measurement based on machine vision technology

Abstract

Color of seed cotton is one of the key indexes of seed cotton quality, which greatly affects the price, grading, storage, and processing of seed cotton. Currently, there are shortage of mature color measurement methods and equipment specifically for seed cotton. Therefore, a color measurement method for seed cotton based on machine vision technology was proposed in this research. To solve the problem of color difference in images, a color difference correction algorithm based on multiple linear regression was proposed, which significantly reduced the color difference by 54.19%. To segment large impurities and large hard particles (cotton seeds, cotton stalks, and boll shells) that are easy to produce shadows from seed cotton images, a quadratic dynamic thresholding segmentation algorithm based on multi-channel fusion was proposed, which significantly improved the segmentation accuracy. The verification results showed that the average value of the intersection over union was 0.9. In the calculation of the color indexes of seed cotton, a correction algorithm based on the BP neural network was used to correct the indexes by taking standard tiles as a reference to reduce the difference caused by system error. The results of the machine vision method were compared with those of the detection of corresponding lint by HVI 1000 and spectrophotometer HX-410. The coefficients of determination (R2) of the Reflectance degree (Rd) and Yellowness (+b) measured by HVI 1000 were 0.790 and 0.865, respectively. The R2 for Rd and +b measured by HX-410 were 0.809 and 0.879, respectively. In addition, the analysis results of the effect of impurities and shadows on seed cotton color showed that both impurities and shadows had a negative effect on Rd. However, the effect of shadows on +b was negative and the effect of impurities was positive. This study indicated that it was feasible to detect seed cotton color using machine vision method.