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Classification of Vegetable Soybean Based on Hyperspectral Images

机译：基于高光谱图像的蔬菜大豆分类

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摘要

Thickness is one of the important appearance qualities of vegetable soybeans in the commodity classification. But conventional classification methods for vegetable soybeans are time- consuming and may not be optimal. As a nondestructive and rapid method, hyperspectral imaging technology has been investigated for classification of vegetable soybeans. The research evaluated the classification for vegetable soybeans which is determined by the thickness measurements using the hyperspectral imaing technology. Hyperspectral reflectance images of two hundred vegetable soybean samples between 400 and 1,000nm were acquired using a hyperspectral reflectance imaging system. A fuzzy-rough set model based on the entropy algorithm is proposed to select the optimal wavelengths for the hyperspectral imaging data of vegetable soybeans. Moreover, partial least squares discrimination analysis (PLSDA) model was developed for two-class ('thinness' and 'thickness'). The validation models yielded 85% and 97% classification accuracies for 'thinness' and 'thickness' with full wavelengths (400-1,000 nm), respectively. Better results, 94% and 97% for 'thinness' and 'thickness', were obtained by validation models with three optimal wavelengths using the fuzzy-rough set algorithm. The simulation results demonstrate that hyperspectral imaging technology is potentially useful for classifying of vegetable soybeans.
机译：厚度是商品分类中蔬菜大豆的重要外观品质之一。但植物大豆的常规分类方法是耗时的，可能不是最佳的。作为一种非破坏性和快速的方法，已经研究了高光谱成像技术以进行蔬菜大豆的分类。该研究评估了植物大豆的分类，该分类由使用超细谱技术的厚度测量决定。使用高光谱反射成像系统获取400至1,000nm之间的两百株植物大豆样品的高光谱反射图像。提出了一种基于熵算法的模糊粗糙集模型，为蔬菜大豆的高光谱成像数据选择最佳波长。此外，为两类（'薄荷'和'厚度'）开发了局部最小二乘辨别分析（PLSDA）模型。验证模型分别产生85％和97％的分类精度，分别具有全波长（400-1,000nm）的“薄薄”和“厚度”和“厚度”（400-1,000nm）的分类精度。通过使用模糊粗略集合算法的三个最佳波长的验证模型获得了更好的结果，94％和97％，获得了三种最佳波长的验证模型。仿真结果表明，高光谱成像技术可能对植物大豆进行分类。

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