Detection of Thrips Defect on Green-Peel Citrus Using Hyperspectral Imaging Technology Combining PCA and B-Spline Lighting Correction Method

In order to find an effective method of detecting thrips defect on green-peel citrus, a defect segmentation method was developed using a single threshold value based on combination of characteristic wavelengths principal component analysis (PCA) and B-spline lighting correction method in this study. At first, four characteristic wavelengths (523, 587, 700 and 768 nm) were obtained using PCA of Vis-NIR (visible and near-infrared) bands and analysis of weighting coefficients; secondarily, PCA was performed using characteristic wavelengths and the second principal component (PC2) was selected to classify images; then, B-spline lighting correction method was proposed to overcome the influence of lighting non-uniform on citrus when thrips defect was segmented; finally, thrips defect on citrus was extracted by global threshold segmentation and morphological image processing. The experimental results show that thrips defect in citrus can be detected with an accuracy of 96.5% by characteristic wavelengths PCA and B-spline lighting correction method. This study shows that thrips defect on green-peel citrus can be effectively identified using hyperspectral imaging technology.

[1]  Moon S. Kim,et al.  Detection of Fecal Contamination on Cantaloupes Using Hyperspectral Fluorescence Imagery , 2005 .

[2]  D. Bulanon,et al.  Classification of grapefruit peel diseases using color texture feature analysis , 2009 .

[3]  José Blasco,et al.  Citrus sorting by identification of the most common defects using multispectral computer vision , 2007 .

[4]  José Blasco,et al.  Multispectral inspection of citrus in real-time using machine vision and digital signal processors , 2002 .

[5]  Jiangbo Li,et al.  Automatic detection of common surface defects on oranges using combined lighting transform and image ratio methods , 2013 .

[6]  Ning Wang,et al.  Studies on banana fruit quality and maturity stages using hyperspectral imaging , 2012 .

[7]  Ying Yibin,et al.  Background and external defects segmentation of navel orange based on mask and edge gray value compensation algorithm. , 2009 .

[8]  Josse De Baerdemaeker,et al.  Detecting Bruises on ‘Golden Delicious’ Apples using Hyperspectral Imaging with Multiple Wavebands , 2005 .

[9]  Jianwei Qin,et al.  Detecting citrus canker by hyperspectral reflectance imaging and PCA-based image classification method , 2008, SPIE Defense + Commercial Sensing.

[10]  Xiuqin Rao,et al.  Development of algorithms for detecting citrus canker based on hyperspectral reflectance imaging. , 2012, Journal of the science of food and agriculture.

[11]  José Blasco,et al.  Recognition and classification of external skin damage in citrus fruits using multispectral data and morphological features , 2009 .

[12]  José Blasco,et al.  Computer vision detection of peel defects in citrus by means of a region oriented segmentation algorithm , 2007 .

[13]  Ning Wang,et al.  Detecting chilling injury in Red Delicious apple using hyperspectral imaging and neural networks , 2009 .

[14]  Di Wu,et al.  Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review — Part II: Applications , 2013 .

[15]  薛龙 Xue Long,et al.  Detecting Pesticide Residue on Navel Orange Surface by Using Hyperspectral Imaging , 2008 .

[16]  Zhao Jiewen,et al.  Detection of rust in citrus by hyperspectral imaging technology and band ratio algorithm. , 2009 .

[17]  J. Blasco,et al.  Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment , 2012, Food and Bioprocess Technology.

[18]  Xiuqin Rao,et al.  Detection of common defects on oranges using hyperspectral reflectance imaging , 2011 .

[19]  Fernando López-García,et al.  Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach , 2010 .

[20]  Tao Chun-kan.  Cloud elimination method in remote sensing image based on spline curve , 2007 .

[21]  Zhang Chi,et al.  Effective wavelengths determination for detection of slight bruises on apples based on hyperspectral imaging , 2013 .

[22]  Moon S. Kim,et al.  Development of simple algorithms for the detection of fecal contaminants on apples from visible/near infrared hyperspectral reflectance imaging , 2007 .

[23]  Bostjan Likar,et al.  A Review of Methods for Correction of Intensity Inhomogeneity in MRI , 2007, IEEE Transactions on Medical Imaging.