Department of Environmental Engineering, University of Vigo, Vigo 36310, Spain.
Sensors (Basel). 2010;10(9):8572-84. doi: 10.3390/s100908572. Epub 2010 Sep 14.
The granite processing sector of the northwest of Spain handles many varieties of granite with specific technical and aesthetic properties that command different prices in the natural stone market. Hence, correct granite identification and classification from the outset of processing to the end-product stage optimizes the management and control of stocks of granite slabs and tiles and facilitates the operation of traceability systems. We describe a methodology for automatically identifying granite varieties by processing spectral information captured by a spectrophotometer at various stages of processing using functional machine learning techniques.
西班牙西北部的花岗岩加工行业处理多种具有特定技术和美学特性的花岗岩品种，这些品种在天然石材市场上的价格也有所不同。因此，从加工初始阶段到最终产品阶段，正确识别和分类花岗岩，可以优化花岗岩板材和地砖库存的管理和控制，并有助于可追溯性系统的运作。我们描述了一种使用功能机器学习技术在加工过程中的各个阶段处理分光光度计捕获的光谱信息来自动识别花岗岩品种的方法。
