Abstract
Hybrid pattern recognition was put forward to discriminate paddy seeds of four different storage periods based on visible/near infrared reflectance spectroscopy (Vis/NIRS). The hybrid pattern recognition included extracting feature and building classifier. A total of 210 samples of paddy seeds, which belonged to four classes, were used for collecting Vis/NIR spectra (325-1075 nm) using a field spectroradiometer. The hybrid pattern recognition was integrated with wavelet transform (WT), principal component analysis (PCA) and artificial neural networks (ANN) models. WT was used to eliminate noises and extract characteristic information from spectral data. The characteristic information could be visualized in principal components (PCs) space, in which the structures correlative with the storage periods could be discovered. The first eight PCs, which accounted for 99.94% of the raw spectral data variance, were used as input of the ANN mode, and the model yielded high discrimination accuracy rates of 100%, 100%, 100% and 90% for four classes’ samples respectively.
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Xiaoli, L., Fang, C., Yong, H. (2007). Application of Hybrid Pattern Recognition for Discriminating Paddy Seeds of Different Storage Periods Based on Vis/NIRS. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_111
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DOI: https://doi.org/10.1007/978-3-540-71701-0_111
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