# Hyperspectral Imaging System with Rotation Platform for Investigation of Jujube Skin Defects
## 1 Introduction  
Appearance is one of the important attributes of fruits which affect their price and consumers' preferences. Fruits which are unsuitable for sale or storage due to surface defects caused by mechanical injuries, insects, diseases, over-ripeness, under-ripeness, etc. should be sorted out during postharvest processing since the consumers normally prefer to buy fruits with no apparent defects. Furthermore, removing fruits with surface defects from the sound ones can reduce losses by preventing secondary contamination. Sorting out fruits with surface defects is usually carried out by manual operation, which is time-consuming, laborious, and subjective [1; 2; 3]. Scientific and technological advances have contributed to improve post-harvest processing. For example, 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. By using RGB values, Golden Delicious and Jonagold apples were graded into four categories according to European standards and the overall accuracy are 78% and 72% respectively [4]. The color index of citrus fruits was measured by using the average of the CCI value and the average RGB values. And the optimized algorithms for in-line color estimating of citrus were developed for mobile platform [5]. A computer vision model based on RGB image was used to estimate the size and weight of yellow melon [6]. Color moments and color histogram extracted from color images of tomato were used to classify ripeness stages of tomato [7]. A computer vision system with three color cameras was used to separate normal and defective apples with 11% classification error. However, this system could not distinguish different defect types [8]. Although the traditional computer vision systems based on RGB color video cameras are effectively used to measure or detect external quality characteristics such as color, texture, size, shape, and some obvious defects of fruits, it is impossible or difficult to detect or differentiate some other less obvious defects whose colors and texture are similar to the normal skin [9]. Unlike the conventional color camera capturing three monochromatic images centered at 700.0 nm (red, R), 546.1 nm (green, G), and 435.8 nm (blue, B) to imitate the vision of humans, the multispectral computer vision system can capture two or more different waveband monochromatic images in the spectrum as we want by utilizing optical filters. A common multispectral camera approach utilizes a filter-wheel consisting of optical bandpass filters. By shifting the filters sequentially into the optical path, the spectrum image is acquired through the channels [10]. A multi-spectral vision system including four wavelength bands in the visible/NIR range was developed to investigate the defects of Jonagold apples [11]. The used filters were centred at 450, 500, 750, and 800 nm and had, respectively, a bandwidth (full width at half maximum or FWHM) of 80, 40, 80, and 50 nm. No error was made on rejected fruits and high classification rates were reached for apples presenting serious defects and recent bruises. [PERSON] et al. [12] developed a multispectral imaging system with two dichroic beamsplitters, two band-pass filters and three charge-coupled device (CCD) cameras for online detection of bruises on apples. The online image acquisition speed is about three apples per second and the classification accuracy of bruised apples in online tests is 74.6%. Hyperspectral imaging, combining conventional two-dimensional imaging and spectroscopic techniques, produces a three-dimensional image data cube, and records the spatial and spectral information simultaneously [13]. In recent years, many researchers have demonstrated the feasibility of hyperspectral imaging systems in detecting skin defects of fruit [14; 15]: bruise on apple and pear [4; 16], chilling injury in peaches [17; 18], decay in citrus [5; 19], fungal infections in strawberry [20], and green mold pathogens on lemons [21] to name a few.