title: CCD-Based Skinning Injury Recognition on Potato Tubers (Solanum tuberosum L.): A Comparison between Visible and Biospeckle Imaging.
abstract: 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.
authors.affiliation: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China. huneagler@163.com.
authors.forename: Yingwang
authors.initials: Y
authors.lastname: Gao
authors.affiliation: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China. kuailehongliu@126.com.
authors.forename: Jinfeng
authors.initials: J
authors.lastname: Geng
authors.affiliation: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China. xqrao@zju.edu.cn.|Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture, Ministry of Agriculture, Zhejiang University, Hangzhou 310058, China. xqrao@zju.edu.cn.
authors.forename: Xiuqin
authors.initials: X
authors.lastname: Rao
authors.affiliation: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China. ybying@zju.edu.cn.|Key Laboratory of Equipment and Informatization in Environment Controlled Agriculture, Ministry of Agriculture, Zhejiang University, Hangzhou 310058, China. ybying@zju.edu.cn.
authors.forename: Yibin
authors.initials: Y
authors.lastname: Ying
mesh_terms: D000465:Algorithms; D015374:Biosensing Techniques / Q000379:methods*; D016018:Least-Squares Analysis; D010363:Pattern Recognition, Automated; D035281:Plant Tubers; D011198:Solanum tuberosum; D060388:Support Vector Machine
keywords: biospeckle imaging; potato; recognition; skinning injury; visible imaging