Conference on Applied Statistics in Agriculture

IDENTIFICATION OF ERRORS IN COTTON FIBER DATA SETS USING BAYESIAN NETWORKS

Cotton fiber is graded on a series of parameters based on physiological factors (strength, length, and thickness), lint color, and presence of non-lint matter such as leaves, stems or other foreign materials. Cotton lint is graded by the USDA-AMS after harvest and ginning, and the grade determines the price of the lint. Given the importance of cotton fiber quality to the value of the crop, the spatial variability of cotton fiber properties is of particular interest to researchers and producers in developing management scenarios for optimal profitability. Previous research studies have relied on hand-harvesting the cotton at intervals throughout the field to obtain a measure of the cotton fiber quality and the extent of spatial variability. However, hand-harvested cotton has different qualities than that harvested by machine and ginned in the large-scale production gins. Part of this arises from the difference in efficiency of harvest between machine and humans, and part results from the different gins used for the smaller sample sizes. While these studies have demonstrated the extent of spatial variability of fiber properties, handharvesting is not amenable to large-scale or production research efforts. Moreover, the differences in fiber properties limit the extension of the results to the production setting. We have developed a mechanism of sampling cotton from the cotton chute during mechanical harvest. The samples are then ginned on a research gin. This study was undertaken to develop a method of translating these small-scale researcher level results to full-scale production level results. The research reported here is the first step in that effort, and demonstrates the use of Bayesian networks to detect erroneous entries in cotton fiber data sets.