A least-squares support vector machine (LS-SVM) based on fractal analysis and CIELab parameters for the detection of browning degree on mango (Mangifera indica L.)

作者：Zheng Hong; Lu Hongfei^*

来源：Computers and Electronics in Agriculture, 2012, 83: 47-51.

DOI：10.1016/j.compag.2012.01.012

• Browning
• Color
• Fractal dimension
• LS-SVM
• Mangifera indica L.

This paper introduces a least-squares support vector machine (LS-SVM) classifier to detect the degree of browning on mango fruits as a function of fractal dimension (FD) and L*a*b* values. Our results showed that the best classification accuracy of browning degree was up to 100% using the LS-SVM classifier based on FD and L*a*b* (gamma = 6.13, sigma(2) = 9.36). However, the correct classification rates of 85.19% and 88.89% were achieved for the LS-SVM models based on FD (gamma = 1.13, sigma(2) = 5.52) and based on L*a*b* (gamma = 6.68, sigma(2) = 2.44), respectively. Therefore, this study indicated the possibility of developing a potentially useful classification tool using the LS-SVM combined with FD and L*a*b* values for classifying the degree of browning on mango fruits during processing, storage and distribution.

• 出版日期2012-4
• 单位浙江师范大学