Gao Yingwang, Geng Jinfeng, Rao Xiuqin, Ying Yibin
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture, Ministry of Agriculture, Zhejiang University, Hangzhou 310058, China.
Sensors (Basel). 2016 Oct 18;16(10):1734. doi: 10.3390/s16101734.
Skinning injury on potato tubers is a kind of superficial wound that is generally inflicted by mechanical forces during harvest and postharvest handling operations. Though skinning injury is pervasive and obstructive, its detection is very limited. This study attempted to identify injured skin using two CCD (Charge Coupled Device) sensor-based machine vision technologies, i.e., visible imaging and biospeckle imaging. The identification of skinning injury was realized via exploiting features extracted from varied ROIs (Region of Interests). The features extracted from visible images were pixel-wise color and texture features, while region-wise BA (Biospeckle Activity) was calculated from biospeckle imaging. In addition, the calculation of BA using varied numbers of speckle patterns were compared. Finally, extracted features were implemented into classifiers of LS-SVM (Least Square Support Vector Machine) and BLR (Binary Logistic Regression), respectively. Results showed that color features performed better than texture features in classifying sound skin and injured skin, especially for injured skin stored no less than 1 day, with the average classification accuracy of 90%. Image capturing and processing efficiency can be speeded up in biospeckle imaging, with captured 512 frames reduced to 125 frames. Classification results obtained based on the feature of BA were acceptable for early skinning injury stored within 1 day, with the accuracy of 88.10%. It is concluded that skinning injury can be recognized by visible and biospeckle imaging during different stages. Visible imaging has the aptitude in recognizing stale skinning injury, while fresh injury can be discriminated by biospeckle imaging.
马铃薯块茎的去皮损伤是一种表面伤口，通常在收获和采后处理操作过程中由机械力造成。尽管去皮损伤普遍存在且具有阻碍性，但其检测方法却非常有限。本研究尝试使用两种基于电荷耦合器件（CCD）传感器的机器视觉技术，即可见光成像和生物散斑成像，来识别受损表皮。通过利用从不同感兴趣区域（ROI）提取的特征，实现了对去皮损伤的识别。从可见光图像中提取的特征是逐像素的颜色和纹理特征，而从生物散斑成像中计算出的是区域级的生物散斑活性（BA）。此外，还比较了使用不同数量散斑图案计算BA的情况。最后，将提取的特征分别应用于最小二乘支持向量机（LS-SVM）和二元逻辑回归（BLR）分类器中。结果表明，在区分完好表皮和受损表皮时，颜色特征的表现优于纹理特征，尤其是对于储存不少于1天的受损表皮，平均分类准确率为90%。生物散斑成像可以加快图像采集和处理效率，将采集的512帧减少到125帧。基于BA特征获得的分类结果对于储存1天以内的早期去皮损伤是可以接受的，准确率为88.10%。研究得出结论，在不同阶段，去皮损伤可以通过可见光和生物散斑成像来识别。可见光成像有能力识别陈旧的去皮损伤，而新鲜损伤则可以通过生物散斑成像来区分。
