Cotton Warehousing Improvement for Bale Management System Based on Neutrosophic Classifier

One of the big factors affecting yarn quality is the cotton mix. There is always a considerable variation in the fiber characteristics from one bale to another, even within the same lot. This variation will result in the yarn quality difference, which leads to many fabric defects if the bales are mixed in an uncontrolled manner. The bale management system is based on the categorization of cotton bales according to their fiber quality characteristics. It includes the measurement of the fiber characteristics concerning each bale by using a High Volume Instrument (HVI). The separation of bales into categories for cotton lay-down to achieve balanced bale mixes must be based on a robust clustering algorithm. This paper discusses the utilization of the neutrosophic classifier, for the first time, to categorize the cotton in the warehouse. Although the traditional categorizing method using fuzzy logic came out with some satisfying results, it was missing the way of excluding the outlier’s data points (off-quality bales) which can affect the fabric quality. Neutrosophic classifier deals with cotton bale’s data type by excluding some bale data points that affect the fabric quality through falsity and indeterminacy membership functions to increase the accuracy of the bale management system. Our proposed method has been tested on mill cotton data. The results have been compared with the results of the traditional fuzzy logic algorithms and revealed higher accuracy.