# Hyperspectral Imaging System with Rotation Platform for Investigation of Jujube Skin Defects
## 3 Results and Discussion
### Classification Results of Injubes  
Figure 7 shows the prediction results of four example jujubes using the proposed classification models. These examples included all surface features studied in this work. For each sample, RGB image was shown in the first column, and the classification results of SVM and ANN models were shown in the second and third columns respectively. By using linear scan method, only the image of surface facing camera could be obtained. The surface area, defined by the new mask, suitable for pixel based defect detection was about 43% of the whole jujube surface. By using the rotation scan method, about 91% of pixels of the whole jujube surface area could be used for defect classification.  
It can be seen from Figure 7 that most of the fruit pixels can be properly classified. However, in Figure 7, it can be noted that the pixels near the right boundary of the fourth specimen were misclassified as black fungus due to low intensity. Similarly, in the classification results of the third sample shown in Figure 7, rusty spots near the top of the image misclassified as black fungus could be due to the same reason. It can be noticed that a small region of rusty spots, shown in the circles of Figure 7, was incorrectly predicted as crack of the fourth jujube sample. By inspecting the color image of this specimen, it can be seen that the rusty area misclassified as crack was brighter than the typical rusty area. The brighter rusty surface could be due to the glare on this rusty area and could be the reason that results in the misclassification. One thing should be pointed out is that jujube surface could have many small rusty spots thus many clusters of pixels classified as rusty spots could be small (only several pixels). Therefore, if image post processing were used to improve accuracy, a particle  
Figure 7: Classification results of jujube samples by using (**a**) linear scan and (**b**) rotation scan methods.  
filter should be used with care to avoid removing small rusty spots by mistake. The Kaohsiung No.11 jujube has smooth calyx; however, the concave feature of stem-end of the Kaohsiung No.11 jujube is similar to that of apple. The color of calyx is similar to that of rusty spot. The color of normal stem-end is the same as that of normal jujube surface; however, the surface of stem-end, compared with other normal surface, could present a relative low intensity due to the curvature of stem-end. Furthermore, during harvesting, the stem of the jujube could remain on the tree and the skin and flesh surrounding the stem could be pulled out of the jujube (Figure 7b (4)). Therefore, it would be necessary to consider methods to recognize stem-end and calyx and to differentiate the normal stem-end and calyx from the real defects in the future work.  
The current hyperspectral imaging system had a stable scanning frame rate of 33 frames per second (FPS) and it took about 40 s for rotation scan of one jujube sample. The use of higher frame rate could not guarantee the image data can be taken at stable frame rate with the use of current computer hardware and software. To increase the stable scan rate of this system, a real time vision and motion control system with synchronization features for camera and motors could be used. In this study, we used whole spectral data to train the classification models. In order to improve the classification speed, dimension reduction techniques should be used to reduce data redundancy of the models while retaining important features of the image. Thus, the classification time can be improved and the classification accuracy can be retained.