Proceedings


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Cotton Fiber Quality


Book Description

The market value of cotton is largely determined by a subjective evaluation of a limited number of quality parameters such as: Grade, Length, and Fineness. Cotton is classed to determine its grade, which indicates the spinning value and utility, and hence the market value or price. Farmers are interested in the classification of cotton by grade to evaluate production, harvesting, and ginning practices, in order to market their cotton profitably, on the other hand, merchants could efficiently buy and sell cotton in accordance with grade.The world wide spread of High Volume Instrument (HVI) testing systems, with their ability to provide rapid and economical measurements of the most important cotton fiber properties, has stimulated efforts to obtain the maximum benefits from these systems.There is a direct correlation between the quality of raw material and the end products. The lower quality of cotton fibers means the lower quality of yarn produced from such a raw material. High quality cotton blends are superior with respect to properties such as length, fineness, elongation, and brightness, sufficiently mature and without any trash particles and displaying a high capacity of spinning consistency.It's worth to say that we are in a need for the visual classification, as it is suitable for application in the local marketing system, as producer sells seed cotton. While using the fiber quality index (FQI) comes in a following step, as it is suitable for application in the international marketing system, as traders sells lint cotton.The present investigations are subjected to study the relationship between the HVI fiber properties and yarn quality to evaluate the actual value of the cotton fibers. Besides determining the relative importance or contribution percentage of each fiber property to the yarn quality and suggesting a statistical approach for calculating a Fiber Quality Index (FQI). In Addition to study the effect of variety, location and seed cotton level on the fiber quality index of the Egyptian cotton.Also to clarify the effect of both genotype and environment on the HVI cotton fiber properties of some Egyptian cotton varieties and its yarn quality. Besides, introducing some new variables fiber quality index (FQI) for determining the actual value of cotton using the HVI fiber properties. This FQI defined the desirable properties of cotton that allow the production of the best quality yarn in particular manufacturing system.New FQIs (Fiber Quality Indices) equations, Lea Product value predicting equation, and a New Leaf Grade equation was obtained, beside studying Card Waste (%) effect on color attributes.




Site-specific Prediction and Measurement of Cotton Fiber Quality


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Maintaining cotton fiber quality is crucial for the survival of the U.S. cotton industry. Previous studies have indicated that spatial variability of fiber quality parameters exists in cotton fields. If site-specific measurement and prediction of quality is possible, then fiber could be segregated during the harvesting process, thus increasing the overall price a producer would receive for his crop. Because of the importance of fiber micronaire to the textile industry, the fact that micronaire exhibits moderate variation in the field, and the fact that it has been shown to be related to optical properties of cotton fibers, micronaire measurement was considered for quality segregation. Two years' cotton and soil data from two fields in Brooksville, Mississippi were used to investigate how much spatial variation in cotton quality factors could be explained by soil parameters. It was found that spatial variability exists in soil and cotton quality parameters, and micronaire (maturity and fineness) was found to have relatively large variability compared to other quality parameters. About 22 to 35% of the variation in micronaire could be explained by soil parameter variability. Site-specific prediction of micronaire based on only soil seems to be not practical according to the results of this study. Another objective was to develop a methodology for measuring important parameters of cotton crop quality in the field. USDA Micronaire Standard Calibration samples were used in infrared spectral measurements in order to relate their micronaire values to near-infrared and mid-infrared wavelength spectra. Near-infrared reflectance measurements in certain wavelengths ranging from 800 to 2500 nm were found to be closely correlated to micronaire values. Mid-infrared transmittance measurements (ratios) in certain wavelengths ranging from 2.5 to 25 [microns] were also related to micronaire values. The R2 value of the optimal prediction model was 0.92. This model was validated with HVI measurement of cotton samples from Mississippi and Arizona. Optical sensors based on spectral reflectance and transmittance measurements seem to be a reasonable choice for site-specific harvesting. A practical sensor mounted on cotton picker for measuring cotton micronaire appears to be feasible and a draft design was proposed.




Development of a Seed Cotton Fiber Quality Sensing System for Cotton Fiber Quality Mapping


Book Description

For precision agriculture to work, an automated process to collect spatial-variability data within a field is necessary. Otherwise, data collection is prohibitively expensive and time consuming. Furthermore, to minimize measurement error due to harvesting method, data-collection processes involving normal cotton harvesting and ginning operations must be used. For the case of cotton, an automated prototype system using image processing to measure the micronaire value of cotton fiber during harvest was designed and built in the laboratory. This system was tested with two image-processing algorithms to identify and remove the effects of objects present in the images that were not cotton fiber, and then measure the reflectivity in three Near-Infrared (NIR) wavebands. Both algorithms yielded similar results when used on seed cotton samples. The reflectivity measurement after removing the effects of foreign matter had a strong relationship to standard micronaire measurements (R2= 0.73 and 0.74 for the ratio-image and single-image algorithms, respectively) with a root mean squared error (RMSE) of 0.28 and 0.27, respectively. The ratio-image pixel classification method classified an average of 58% of the pixels in an image as "cotton", while the single-image method classified an average of 81% of the pixels in each image as cotton. These results do not show as strong a relationship between micronaire and NIR reflectivity of cotton samples as previous research done with very uniform lint cotton calibration samples. This is attributed to the higher content of foreign matter in seed cotton samples. With higher trash cotton and fiber that has not yet been cleaned, results obviously are not as good as when using calibration cotton samples. These results indicate the system can be adapted to perform in-situ measurement of cotton fiber quality, specifically micronaire, and enable harvesters to create quality maps of a field automatically to allow better crop management.




Cotton Fibers


Book Description

Explore new and proven information about cotton fibers! Cotton Fibers is an important reference source for anyone who produces, markets, and researches cotton fibers. This unique book is written by internationally renowned researchers who have pooled their immense knowledge to create this outstanding volume that deals with development, quality improvement, and textile/technological aspects of cotton production. Cotton, a worldwide crop that is valued at $20 billion, is the premiere natural fiber for textiles. As cotton fiber consumption continues to increase, the crop is becoming a major importance to the economies of both developed and developing countries. Cotton Fibers covers the recent explosion of information on cotton fibers and points out research priorities for the future, consequently stimulating multidisciplinary cotton research. Cotton Fibers provides you with information on topics that will help you improve the quantity and quality of cotton crops, such as: developing cotton fibers in vitro developing cellulose biosynthesis in cotton fibers modifying cotton fibers with genetic engineering strategies managing postharvest fiber quality abating air pollution and disposal of gin waste fiber-to-fabric engineering for optimal cotton fiber quality structural development of cotton fibers and linkages to fiber quality cotton germplasam resources and their potential for improved fiber productivity and quality molecular genetics of developing cotton fibers Complete with charts and diagrams, Cotton Fibers is a thorough exploration of what is known about cotton fibers and what research is just beginning to reveal about the crop. You will explore some of the latest technological advances in cotton fiber production, such as understanding the genetics of fiber growth and development and introducing hormone genes into cotton. This comprehensive guide is a vital tool for anyone interested in increasing the yield and quality of cotton, the world's most popular fiber.







Cotton Quality - Fibre to Fabric


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The textile industry has a recurrent white speck nep problem in cotton. "White specks" are immature clusters of fibres that are not visible as defects until dyeing, and then they appear white on darkly dyed fabric. The white speck potential of cotton is difficult to predict except in extremely immature cottons. Industry requires a method to predict fabric quality from cotton bale fibre properties to minimize this risk and reduce financial losses. This research addresses the problem of predicting white specks in dyed cotton fabrics given controlled gin and mill processing. Gin and mill processing must be controlled so that field and varietal effects can be seen without the interaction of mechanical processing differences. This results in achieving other objectives, including the provision of baseline data for Australian varieties, ginning effects and comparison of ring and open-end spinning. This final analysis of these studies results in white speck prediction equations from high-speed fibre measurement systems. Cotton breeders will be able to use the equations in the development of new varieties with low white speck potential.