Yin Hai, Li Bin, Liu Yan-de, Zhang Feng, Su Cheng-Tao, Ou-Yang Ai-Guo
Institute of Optical-electro-mechatronics Technology and Application, East China Jiao Tong University, National and local joint engineering research center of fruit intelligent photoelectric detection technology and equipment, Nanchang 330013, China.
Institute of Optical-electro-mechatronics Technology and Application, East China Jiao Tong University, National and local joint engineering research center of fruit intelligent photoelectric detection technology and equipment, Nanchang 330013, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2022 Dec 15;283:121775. doi: 10.1016/j.saa.2022.121775. Epub 2022 Aug 22.
The bruising is one of the major factors affecting the quality of loquat and the bruised areas of loquat are also prone to harbor bacteria and molds. Therefore, it is critical to detect early bruises of loquat. In this study, a method based on hyperspectral imaging technology coupled with band ratio and improved Otsu method was proposed to detect early bruises of loquat. Firstly, the principal component cluster analysis was used to analyze the three regions of Vis-NIR (397.5-1014.0 nm), Vis (397.5-780.0 nm), and NIR (780.0-1014.0 nm), respectively. It was found that the Vis-NIR and NIR spectral regions along PC1 could be used to effectively distinguish bruised tissues. Then, the key wavelength images corresponding to the two regions were selected according to the load curve, respectively, and two sets of PC images and band ratio images of them were established. After comparison, it was found that the band ratio image Q was the most suitable for subsequent analysis of detecting early bruises of loquat. Finally, in order to evaluate the segmentation effect of the improved Otsu method, the segmentation results of the global threshold and the Otsu method were compared with it, respectively, and it was found that the performance of the improved Otsu method was best. However, since the stem-end area and the bruised area have similar intensity features causing mis-segmentation, the stem-end area was removed by curvature-assisted Hough transform circle detection (CACD) algorithm. And all test set samples were used to evaluate the performance of the proposed method, and the overall accuracy of it was 96.0 %. The results show that the detection method proposed in this study has the potential to detect early bruises of loquat in online practical applications, and it provides a theoretical basis for hyperspectral imaging in the bruise detection of fruit.
瘀伤是影响枇杷品质的主要因素之一，枇杷的瘀伤部位也容易滋生细菌和霉菌。因此，早期检测枇杷瘀伤至关重要。本研究提出了一种基于高光谱成像技术结合波段比值和改进的大津法来检测枇杷早期瘀伤的方法。首先，分别利用主成分聚类分析对可见-近红外（397.5-1014.0nm）、可见光（397.5-780.0nm）和近红外（780.0-1014.0nm）三个区域进行分析。发现沿主成分1的可见-近红外和近红外光谱区域可有效区分瘀伤组织。然后，根据载荷曲线分别选取这两个区域对应的关键波长图像，并建立两组它们的主成分图像和波段比值图像。经比较发现，波段比值图像Q最适合后续枇杷早期瘀伤检测分析。最后，为评估改进的大津法的分割效果，分别将全局阈值法和大津法的分割结果与之比较，发现改进的大津法性能最佳。然而，由于果梗端区域和瘀伤区域强度特征相似导致误分割，采用曲率辅助霍夫变换圆检测（CACD）算法去除果梗端区域。并使用所有测试集样本评估所提方法的性能，其总体准确率为96.0%。结果表明，本研究提出的检测方法在在线实际应用中具有检测枇杷早期瘀伤的潜力，为高光谱成像在水果瘀伤检测方面提供了理论依据。
