PMID- 27763555
OWN - NLM
STAT- MEDLINE
DCOM- 20180205
LR  - 20181113
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 16
IP  - 10
DP  - 2016 Oct 18
TI  - CCD-Based Skinning Injury Recognition on Potato Tubers (Solanum tuberosum L.): A 
      Comparison between Visible and Biospeckle Imaging.
LID - 1734
AB  - 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.
FAU - Gao, Yingwang
AU  - Gao Y
AD  - College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 
      310058, China. huneagler@163.com.
FAU - Geng, Jinfeng
AU  - Geng J
AD  - College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 
      310058, China. kuailehongliu@126.com.
FAU - Rao, Xiuqin
AU  - Rao X
AD  - College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 
      310058, China. xqrao@zju.edu.cn.
AD  - Key Laboratory of Equipment and Informatization in Environment Controlled 
      Agriculture, Ministry of Agriculture, Zhejiang University, Hangzhou 310058, 
      China. xqrao@zju.edu.cn.
FAU - Ying, Yibin
AU  - Ying Y
AD  - College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 
      310058, China. ybying@zju.edu.cn.
AD  - Key Laboratory of Equipment and Informatization in Environment Controlled 
      Agriculture, Ministry of Agriculture, Zhejiang University, Hangzhou 310058, 
      China. ybying@zju.edu.cn.
LA  - eng
PT  - Journal Article
DEP - 20161018
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - Algorithms
MH  - Biosensing Techniques/*methods
MH  - Least-Squares Analysis
MH  - Pattern Recognition, Automated
MH  - *Plant Tubers
MH  - *Solanum tuberosum
MH  - Support Vector Machine
PMC - PMC5087519
OTO - NOTNLM
OT  - biospeckle imaging
OT  - potato
OT  - recognition
OT  - skinning injury
OT  - visible imaging
COIS- The authors declare no conflict of interest. The funding sponsors had no role in 
      the design of the study; in the collection, analyses, or interpretation of data; 
      in the writing of the manuscript, and in the decision to publish the results.
EDAT- 2016/10/21 06:00
MHDA- 2018/02/06 06:00
PMCR- 2016/10/18
CRDT- 2016/10/21 06:00
PHST- 2016/08/06 00:00 [received]
PHST- 2016/10/14 00:00 [revised]
PHST- 2016/10/15 00:00 [accepted]
PHST- 2016/10/21 06:00 [pubmed]
PHST- 2018/02/06 06:00 [medline]
PHST- 2016/10/21 06:00 [entrez]
PHST- 2016/10/18 00:00 [pmc-release]
AID - s16101734 [pii]
AID - sensors-16-01734 [pii]
AID - 10.3390/s16101734 [doi]
PST - epublish
SO  - Sensors (Basel). 2016 Oct 18;16(10):1734. doi: 10.3390/s16101734.