1a.Objectives (from AD-416):
To obtain the distribution of color of a cotton sample instead of one single color grade by using image analysis methods, and to investigate if higher variation in cotton color is associated with higher variations in other cotton properties such as maturity and fineness. This research will provide new tools for better evaluation of cotton quality and aid the development of the next generation rapid test instrument for cotton color measurement.
1b.Approach (from AD-416):
Image analysis techniques will be used to explore a series of sub-areas of the area of the measured cotton samples. The size of these sub-areas can be down to pixel level. Sophisticated image processing techniques will be used to analyze the sample images’ color distributions and identify the impacts of trash and other impurities. The digital images can have very high resolutions and color levels that are sufficient for differentiating the variations among the different parts of the sample.
This is the final progress report for this project. Experimental protocols have been established. Tests have been conducted to acquire color images of a large set of cotton samples covering a wide range of color grades. Samples also included standard color tiles and trash tiles as references. Experiments to investigate the effects of pressure on measurements were also conducted. Methods have been developed to process the acquired sample images and obtain the intra-sample cotton color distributions and variations. Intra-sample variation is defined as the color variation within one measurement of a repetition. Inter-sample variation is defined as the color variation of different repetitions of a cotton. MatLab programs have been developed to compute the intra-sample distribution and variation, in addition to the overall color grade based on the color grade look-up table and chart. Results also indicated that the color values computed by using the image analysis method were highly correlated to color values reported by High Volume Instrument (HVI). ARS Scientist at Southern Regional Research Center (SRRC) in New Orleans, LA are developing methods to quantitatively describe the intra-sample color distribution and variation. ARS Scientists at SRRC in New Orleans, LA have developed the mathematical function that can describe the intra-sample color distribution and variation. The bivariate normal distribution function has been investigated for this purpose. The distributions’ locations, shapes, orientations, and sizes can quantitatively describe the distribution and be used to compare color distributions. MatLab and SAS programs (Statistical Analysis System) were developed for the analyses. Difference between samples’ distributions and variations were compared by looking at their statistical parameters computed from the distribution function.