## 3 Experimental Results and Discussion
### Defect Identification Threshold Calculation and Result Analysis  
Firstly, some images were selected from the dataset to calculate the defect identification threshold. Two-hundred normal seedling images were randomly selected from the dataset as the normal group, and 25 defective seedling images were selected as the defect group, including 5 empty conveyor belt  
Figure 11: Mean IOU of the substrate (**A**) and seedling region (**B**). Figure 12: Image segmentation results based on maxIOU. (**A–E**) The original images; (**A-1–E-1**) the segmented images. The red regions are the segmented substrate regions. The green regions are the segmented seedling regions. images, 5 seedling deficiency images, 5 skew images, 5 multiple seedlings images, and 5 substrate damage images. According to Equation (7), a scatter plot was used to determine the lower threshold of seedling area (see Figure 13). According to the defect feature analysis in Table 2, the empty belt, lack of seedlings, and skewed seedling images were selected for comparison with the normal group images. The area minimum of the normal group seedlings was 19,275 pixels, followed by 29,230 pixels. The area maximum of the empty belt, lack of seedlings, and skewed seedling was 23,688, followed by 18,806. The classification results of different segmentation thresholds are shown in Table 3. As shown in Table 3, when the classification threshold interval was (18,806, 19,275), there was one misclassification in the empty conveyor belt images, while the seedling deficiency, skew, and normal images were all classified correctly. When the classification threshold interval was (19,275, 23,688), there was one error respectively in the normal images and the defect images' (empty conveyor belt, seedling deficiency, and skew images) classification result. When the classification threshold interval was (23,688, 29,230), there was one misidentification in normal images' classification, and the defect images (empty conveyor belt, seedling deficiency, and skew images) were all classified correctly. As the effect of defect seedling misidentification was greater than normal seedling misidentification, the seedling defect identification threshold interval should be selected as (23,688, 29,230). Finally, the threshold was taken as the intermediate value of 26,459 in the experiment. According to Equation (7), a scatter plot was used to determine the upper and lower threshold of the seedling area (see Figure 14). According to the defect feature analysis in Table 2, the empty conveyor belt, multiple seedlings, and substrate damaged images were selected for comparison with the normal images. The area maximum of the normal group substrate was 46,912 pixels, and the area minimum of the normal group substrate was 21,888 pixels. The area maximum of the multiple seedlings and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification Threshold Interval** & **(18,806,** & **(19,275,** & **(23,688,** \\  & **19,275)** & **23,688)** & **(29,230)** \\ \hline The number of misclassified normal images & 0 & 1 & 1 \\ \hline The accuracy of normal images’ classification & 100\% & 99.5\% & 99.5\% \\ \hline The number of misclassified defect images & & & \\ (empty conveyor belt, seedling deficiency, and skew images) & 1 & 1 & 0 \\ \hline The accuracy of defect images’ (empty conveyor belt, seedlings deficiency, and skew images) classification & 93.3\% & 93.3\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Analysis of the seedling defect identification threshold. Figure 13: Feature scatter plot of the seedling area. substrate damaged images was 51,931 pixels. All the area of the empty conveyor belt images was zero. There was no overlapping interval between the normal group and the defect images. The substrate identification lower threshold interval was selected as (0, 21,688), and the upper threshold interval was selected as (46,912, 57,931). Finally, the lower and upper threshold were taken as the intermediate values of 10,844 and 52,422 in the experiment.