# Segmentation of Rice Seedings Using the YCrCb Color Space and an Improved Otsu Method
## 3 Experimental Results and Discussion
### Comparison of Segmentation Performance  
To evaluate the performance of the proposed algorithm, we compared it with two other threshold segmentation methods: (1) the Otsu method, which is widely used to segment crops in agricultural visual navigation [24] (referred here as Otsu); (2) a modified valley-emphasis method [26] using the neighborhood information of the valley point to select the optimal threshold (referred to here as NVE). In the experiment, we selected 50 rice seedling images taken under different paddy field environments as per the test images shown in Figure 3, and 2 Cg-Cr-Cb was used to obtain gray images. Figure 11  
Figure 10: The rice seedling images and the processing results by applying the different graying processing methods: (**A**) the original image with shadow on a cloudy day; (**A1–A3**) the corresponding results of G component, ExG and 2 Cg-Cr-Cb graying methods; (**B**) the original image with highlights on a sunny day; (**B1–B3**) the corresponding results of G component, ExG and 2 Cg-Cr-Cb graying methods.  
shows segmentation results of five test images obtained via Otsu, NVE and the proposed methods. The original test images taken in different weather (sunny, cloudy) and field (water, mud) environments are shown in the first column; the second, third and fourth columns show the segmentation results by the Otsu, the NVE and the proposed method, respectively, where rice seedlings are marked with white.  
As seen in the second column, the Otsu method segments many background pixels as rice seedling pixels and fails to provide reliable information on rice seedling rows. Although the NVE method performs well compared with the Otsu method, it loses many rice pixels of seedling leaf edges and brings insufficient results. Clearly, the proposed method can provide reliable information on seedling rows and does not disturb the subsequent extraction of the navigation line.  
Additionally, the performance of the proposed method can be evaluated quantitatively, and one common metric is used in this study: the Misclassification Error (ME) [27]. The ME metric describes  
Figure 11: Segmentation results of the five test images by the proposed method and two other methods. (**A–E**) are the original rice seedling images; (**A1–E1**) show segmentation results by Otsu; (**A2–E2**) give segmentation results by NVE; and (**A3–E3**) present segmentation results by the proposed method.  
the percentage of object pixels incorrectly detected as the background, and vice versa. The ME metric is defined as:  
\[\mathrm{ME}=1-\frac{|B_{result}\cap B_{truth}|-|F_{truth}\cap F_{result}|}{|B_ {truth}|+|F_{truth}|}, \tag{20}\]  
where \(B_{result}\) and \(F_{result}\) are the background and object pixels in the segmentation result, \(B_{truth}\) and \(F_{truth}\) are the background and object pixels in the ground truth labeled manually and \(|\bullet|\) denotes the cardinality of a set. The lower value of ME leads to a higher accuracy of segmentation. The ME results of the proposed method and of the other two methods are computed for the 50 test images which are presented in Figure 3, and the ME curves are plotted in Figure 12. It can be seen from Figure 12 that the ME results of the Otsu and the NVE methods are greatly affected by the paddy field environment because the Otsu and the NVE methods usually bring over-segmentation and under-segmentation of results, respectively, under complex paddy field environments. However, the segmentation performance of the proposed method improves on that of the other two methods and the proposed approach offers the lowest ME in the same image, which can provide reliable seedling row information for visual navigation system of automated rice tranplanters.