Genetic algorithms for feature selection of image analysis-based quality monitoring model: An application to an iron mine

Measuring the quality parameters ofmaterials atmines is difficult and a costly job. In this paper, an image analysis-basedmethod is proposed efficiently and cost effectively that determines the quality parameters of material. The image features are extracted from the samples collected from amine and modeled using neural networks against the actual grade values of the samples generated by chemical analysis. The dimensions of the image features are reduced by applying the genetic algorithm. The results showed that only 39 features out of 189 features are sufficient to model the quality parameter. The model was tested with the testing data set and the result revealed that the estimated grade values are in good agreement with the real grade values (R1⁄40.77). The developedmethodwas then applied to a case studymine of iron ore. The case study results show that proposed image-based algorithm can be a good alternative for estimating quality parameters of materials at a mine site. The effectiveness of the proposed method was verified by applying it on a limestone deposit and the results revealed that themethod performed equally well for the limestone deposit. & 2010 Elsevier Ltd. All rights reserved.