Abstract
Abstract Selecting a desired level of quality in a product is crucial—for instance, in dried chili peppers (Capsicum annuum L.). However, classifying dried chili peppers is a time-consuming, manual task. One of the main problems with sorting this way is the lack of product homogeneity. This paper presents the development of a classification system, based on artificial neural networks, for size and color recognition applied to the aforementioned peppers. The classification system uses 8-bit grayscale-image histograms to characterize the peppers. Three quality levels identified in a Mexican Official Norm (NMX-FF-107/1-SCFI-2014) for a specific type of chili (Guajillo) were used to show the proposed method. The effectiveness of the proposed classification system is demonstrated through experiments and is measured using the receiver-operating characteristic curve, calculating the area under the curve and obtaining an accuracy of 82.13. The results show an innovative, reliable and economical alternative, capable of sorting dried chili peppers; a system that aims to contribute to the solution of the problem of identification and classification in dehydrators y/o final customers.
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