# Peeling Damage Recognition Method for Corn Ear Harvest Using RGB Image
## 4 Discussion  
Based on the experimental results, several issues are discussed as follows.  
* In this study, we subjectively evaluated the recognition performance. For an objective image, especially for a multiple ear image, the recognition accuracy was determined by several aspects, such as the position of damage region, the area of damage region, and the contour accuracy of ears. Hence, one or more indicators may be required to objectively evaluate recognition performance. This is of great valuable for further study.
* As shown in Figure 12, wrapped leaves are recognized as kernels. This is because the RGB data of leaves are also added into the training samples in the dictionary learning process. The ears wrapped by leaves imply they are not peeled thoroughly. Such ears are usually complete without damage, and, therefore, in this study, we treated the wrapped leaves as kernels.
* We utilize the dictionary learning method to recognize kernels, while using thresholding method in terms of RGB reflections to recognize damage. This is because the characteristic of RGB reflections for ear damage is distinctive, and it can be easily distinguished through RGB reflections. However, as mentioned above, the RGB reflections of corn kernels are close to some impurities. Additionally, the RGB reflections of kernels for different corn varieties are also different. Hence, we trained dictionaries in advance to recognize kernels based on the correlation between dictionaries and objective images.
* In the experimental results on multiple ears, only two damaged regions are missed among all test scenes. As shown in Figure 14, one missed region (Figure 14, left) is located near a cracked ear. The cross-section faces right to the camera, and therefore the damaged circumferential surface is not imaged, leading to the failure of recognition. The other missed region (Figure 14, right) shows atypical RGB data on its damaged surface such that they do not satisfy the constraint of the recognition algorithm.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \begin{tabular}{c} **Number of All** \\ **Damaged Regions** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **Recognized Regions** \\ \end{tabular} &
\begin{tabular}{c} **Recognition Rate** \\ **(\%)** \\ \end{tabular} \\ \hline Scene 1 & 6 & 6 & 100.00 \\ \hline Scene 2 & 10 & 8 & 80.00 \\ \hline Scene 3 & 9 & 9 & 100.00 \\ \hline Scene 4 & 11 & 11 & 100.00 \\ \hline Scene 5 & 7 & 7 & 100.00 \\ \hline
**Total** & **43** & **41** & **95.35** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary on recognition rates for test scenes.  
Figure 14: Final results on multiple ear damage recognition.  
* In this study, we theoretically developed the method of peeling damage recognition. We believe the proposed method can be applied for practical harvesting in the field. The realization should rely on a RGB camera and an embedded control system that are fixed into the harvesters. The images acquired by the RGB camera are transmitted to the embedded control system where the recognized process is executed based on the proposed method.