Identification of Bruise and Fungi Contamination in Strawberries Using Hyperspectral Imaging Technology and Multivariate Analysis
Identification of Bruise and Fungi Contamination in Strawberries Using Hyperspectral Imaging Technology and Multivariate Analysis
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利用高光谱成像技术和多变量分析识别草莓中的瘀伤和真菌污染
DOI：
10.1007/s12161-017-1136-3
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发表时间：
2018-05-01
影响因子：
2.9
通讯作者：
Tu, Kang
中科院分区：
文献类型：
--
作者：
Liu, Qiang;Sun, Ke;Tu, Kang
机械损伤和真菌污染是草莓的两个典型缺陷特征，导致草莓在运输和储存过程中迅速品质劣变。本研究将图像处理与光谱分析相结合，成功地利用高光谱反射率成像系统对草莓的损伤和真菌感染进行了识别。利用最小噪声分数（MNF）变换对高光谱图像数据进行处理，结合阈值法和形态学方法对草莓缺陷进行识别，并对缺陷区域进行定位和分离，进行光谱提取。基于目标缺陷区域，建立了品质参数与光谱特征之间的关联。光谱归一化后，三个不同的光谱区域（400至600 nm，650至720 nm，和900至1010 nm）被确定为健康，擦伤，或感染的草莓，和8个最佳波长选择的连续投影算法（SPA）从整个波长范围。开发了线性和非线性算法来识别草莓中的缺陷类型。结果表明，基于全波长的SVM模型的整体识别精度最高，校正准确率为96.91%，预测准确率为92.59%。研究结果表明，高光谱反射成像技术在草莓缺陷识别中具有一定的应用潜力，为不同缺陷水果的在线分类提供了理论依据。
Mechanical bruise and fungi contamination are two typical defective features for strawberries, resulting in quick quality deterioration of the strawberries during transportation and storage. In this work, the approach of combined image processing with spectra analysis was successfully developed to identify defective strawberries (bruised and fungal infected) using hyperspectral reflectance imaging system. Hyperspectral image data was exploited by minimum noise fraction (MNF) transformation for strawberry defects distinguished by combining thresholding and morphology procedures, and defective regions were located and separated for spectra extracting. The linkages between quality parameters and spectra features were established based on the target defective regions of the fruit. After spectra normalization, three different spectral regions (400 to 600 nm, 650 to 720 nm, and 900 to 1010 nm) were identified for healthy, bruised, or infected strawberries, and eight optimal wavelengths were selected by the successive projection algorithms (SPA) from the whole range of wavelengths. Both linear and non-linear algorithms were developed to identify defective types in strawberries. The results indicated that based on full wavelengths, SVM model performed the highest overall identification accuracy, with the accuracy of 96.91% for calibration and 92.59% for prediction of the fruit. This work shows that hyperspectral reflectance imaging technology has the potential for identifying defective strawberries and provides theoretical basis for the development of online classification of different defected fruits.