Near-infrared hyperspectral imaging for classification of mung bean seeds. Issue 1 (1st January 2018) - Phuangsombut, Kaewkarn
ABSTRACT: Hard mung bean seeds pose a problem in the sprouting process as they develop mold and infect neighboring seeds. Near-infrared hyperspectral imaging combined with partial least squares discriminant analysis was applied to develop a classifying model to separate hard mung beans from normal ones. The orientation of the measured beans was found to affect the classification result. The optimal partial least squares discriminant analysis model based on all orientations resulted in a correlation coefficient ( R ) of 0.919 with a root mean squared error of prediction of 0.197. The non-germinative parts were mapped and were concentrated at one end of the bean. Finally, a germinability index was proposed according to the proportion of colored areas between the germinative and non-germinative parts from the hyperspectral imaging results.