为探究柚果视觉特征与其内部品质之间的相关特性,寻找一种快速低成本的检测方法,该文采集了柚果20种外部视觉特征(纵径、横径、纵横径比、面积、R、G、B灰度分别的一阶、二阶和三阶形态、H、V、S灰度、对比度、相关度、能量、粗糙度,分别用F1~F20表征)与3种主要内部品质[硬粒程度、可溶性固形物(total soluble solid,TSS)含量和含水率]用于分析。研究结果表明,机器视觉技术可以精确地还原柚果外部特征情况。柚果全部视觉特征与内部品质指标的线性相关性均不显著,但与内部品质指标之间具有较强的非线性相关特性。其中,柚果视觉特征F5、F8、F9、F10、F11、F12、F13、F14、F15、F17、F19和F20与硬粒程度之间的非线性相关相关系不显著,其余特征均与硬粒程度显著非线性相关。柚果视觉特征F5、F8、F9、F10、F11、F12、F13、F14、F15和F17与TSS含量和含水率均显著非线性相关,其余特征与TSS含量和含水率的非线性相关性均极其显著。采用柚果视觉全特征与仅用显著非线性相关特征对其硬粒程度识别结果均不佳。采用柚果视觉特征对其TSS和含水率进行粗略识别均是可行的,其中全特征识别效果要优于仅用极显著非线性相关特征的识别效果。因此,柚果视觉特征可为其内部品质无损检测提供有益的信息补充,也可直接形成粗略低成本的TSS含量和含水率无损检测方法。该研究也为其他水果内部品质无损检测技术的提升提供参考与新思路。
To find the relationship between external vision features and internal quality detection of pomelo, and provide a rough detection method, twenty vision features (vertical and transverse diameters, ratio of vertical and transverse diameters, area, first order of R, G and B gray values, second-order of R, G and B gray values, third order of R, G and B gray values, H, V, and S gray values, correlation, contrast, energy, and roughness, which were represented by F1 to F20) and 3 major internal quality parameters (granulation degree, total soluble solid (TSS) and water content) were detected and analyzed. The results showed that the external feature could be well described by machine vision technology. The linear correlation between whole vision features and internal quality parameters was insignificant. However, the nonlinear correlation between vision features and internal quality parameters was significant. Thereinto, the nonlinear correlation between vision features and granulation degree were insignificant except F5, F8, F9, F10, F11, F12, F13, F14, F15, F17, F19 and F20. The nonlinear correlation between vision features (F5, F8, F9, F10, F11, F12, F13, F14, F15 and F17) and TSS/water content were significant, the nonlinear correlation between other vision features and TSS/water were extremely significant. The granulation degree of pomelo cannot be detected by whole vision features or only significant correlated vision features. However, TSS and water content of pomelo could be roughly well detected by vision features, and the detection effects of whole vision features based were better than only extremely significant features based. Thus, machine vision features not only provide supply information for internal quality detection by other methods, but also can be applied to construct a low-cost and roughly nondestructive detection method for pomelo TSS and water content. This study also provide a reference and new ideas for the improvement of internal quality nondestructive detection of other fruits.
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