Submitted to: National Cotton Council Beltwide Cotton Conference
Publication Type: Proceedings
Publication Acceptance Date: June 18, 1998
Publication Date: N/A
Interpretive Summary: Gin process control systems adjust gin equipment while ginning to increase financial returns to the farmer. To do this, the systems evaluate the leaf grade of the cotton during processing at the gin, and predict among other factors the monetary value of the cotton. The leaf grade, which is related to the trash content, is an important factor in determining the value of the cotton. Leaf grade results from a visual assessment made by a human classer, thus, the sensor cannot measure it. Existing relationships between sensor measurements of trash and the human assessments are not sufficiently accurate to ensure optimum processing at the gin. Because numerous sensor assessments of trash are made at the gin on each bale, the measurements were used to predict the leaf grade. The resulting equation predicts the leaf grade better than the established relationship. The improved prediction of leaf grade allows better selection of gin drying and cleaning, and will improve the monetary returns to the farmers.
Technical Abstract: The economic value of cotton to the farmer greatly depends on the quality as determined by the United States Department of Agriculture (USDA), Agricultural Marketing Service (AMS), Cotton Division, Cotton Classing Office (CO). Optimization of gin processing depends on the accurate prediction of the AMS CO quality measurements in real time in the gin. Cotton samples with a wide variety of trash content were generated with a variety of gin cleaning machinery and sent to the Dumas CO for classing. Data were collected while ginning the samples with equipment similar to that used in gin process control in the Microgin facility at the U.S. Cotton Ginning Laboratory, Stoneville, MS. About 17 readings of samples of each of the 122 lots were made. Data from a commercial gin were analyzed with similar models. One model was chosen to predict the AMS CO leaf grade based on readings made while ginning. This model predicted leaf grade correctly 64.5% of the time and was within one grade 99% of the time. This was an improvement over the 40-60% correct predictions experienced in 1994-1996 based on AMS CO data from 1991-1992.