Hyperspectral Imaging System with Rotation Platform for Investigation of Jujube Skin Defects

Quoc Thien Pham,Nai-Shang Liou

doi:10.3390/app10082851

Quoc Thien Pham, Nai-Shang Liou

Open Access

https://doi.org/10.3390/app10082851

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Abstract

A novel object rotation hyperspectral imaging system with the wavelength range of 468–950 nm for investigating round-shaped fruits was developed. This system was used to obtain the reflection spectra of jujubes for the application of surface defect detection. Compared to the traditional linear scan system, which can scan about 49% of jujube surface in one scan pass, this novel object rotation scan system can scan 95% of jujube surface in one scan pass. Six types of jujube skin condition, including rusty spots, decay, white fungus, black fungus, cracks, and glare, were classified by using hyperspectral data. Support vector machine (SVM) and artificial neural network (ANN) models were used to differentiate the six jujube skin conditions. Classification effectiveness of models was evaluated based on confusion matrices. The percentage of classification accuracy of SVM and ANN models were 97.3% and 97.4%, respectively. The object rotation scan method developed for this study could be used for other round-shaped fruits and integrated into online hyperspectral investigation systems.

Highlights

Appearance is one of the important attributes of fruits which affect their price and consumers’preferences
Line scan is a convenient way for imaging cylindrical object because an unwrapped image of the entire surface without distortion can be obtained by rotating the cylinder when scanning
If the image pixel aspect ratio was greater than one when imaging a cylindrical object by rotation scan, the distance between scan lines would be greater than the width of scan line, and the surface between could not be interrogated

Summary

Introduction

Appearance is one of the important attributes of fruits which affect their price and consumers’preferences. Should be sorted out during postharvest processing since the consumers normally prefer to buy fruits with no apparent defects. Sorting out fruits with surface defects is usually carried out by manual operation, which is time-consuming, laborious, and subjective [1,2,3]. Computer vision techniques based on red, green, and blue (RGB) images have been developed and applied in fruit sorting and grading systems over the past decades. A computer vision system with three color cameras was used to separate normal and defective apples with 11% classification error. This system could not distinguish different defect types [8]

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