## 4 Results
### Accuracy Evaluation  
The validation data were created manually for each class, and a confusion matrix was calculated to evaluate the accuracy of the two classification methods, as shown in Tables 1 and 2. The overall accuracy of the SVM method is 96.65%, and the Kappa coefficient (a statistic that is used to measure inter-rater reliability) [28]  
The number of cotton plants identified by the method used in this study is presented in Table 3. The column labeled 'non split' means that the overlapping cotton plants were not identified and segmented, and cotton plants composed of an overlapping cotton plant were counted as one cotton plant. The column labeled 'non split' means the number of cotton plants which were split by Equations (1) through (5). The germination rates for manual interpretation, non split, and split are listed in the table.  
As shown in Figure 4b, samples 3, 7, and 11 had lower cotton plant density than the other samples, and samples 1 and 5 had higher cotton plant density. The germination rate by manual interpretation shown in Table 3 indicates similar results. The germination rates for samples 3, 7, and 11 were 0.485, 0.470, and 0.509, and the germination rates for samples 1 and 5 were 0.683 and 0.664, respectively. The minimum and maximum germination rates were 0.470 and 0.683 for samples 7 and 1, respectively.  
The budding rates of the validation samples obtained from manual interpretation, non split, and split methods are shown in Figure 6 in order to further compare their differences. As shown in Figure 6, in general, the manual interpretation has the highest accuracy as compared to the other methods for all the validation samples. The split method always has a higher germination rate than the non split method, with overlapping indicating that overlapping cotton plants were present. For different samples, due to the different growth status of the cotton plants, the overlapping degree of overlapping cotton plants varied. The germination rate of the split method for sample 5 was larger than the non split method, but for samples 7 and 11, the difference in germination rate was small. All in all, the split method presented an increase in detection accuracy of 6.3% over the non split method, and the mean germination rates  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Truth**} \\ \hline  & **Class** & **Cotton** & **Soil** & **Shadow** & **Plastic** & **Total** \\ \cline{2-7} Prediction (percent) & cotton & 96.70 & 0 & 0 & 0 & 6.69 \\ soil & 1.96 & 97.03 & 10.69 & 0.56 & 22.02 \\ shadow & 1.34 & 2.97 & 88.42 & 1.67 & 10.20 \\ Plastic & 0 & 0 & 0.90 & 97.77 & 61.09 \\ Total & 100 & 100 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion Matrix of the classification (percent).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{6}{c}{**Number of Cotton Plants**} & \multicolumn{6}{c}{**Germination Rate**} \\ \cline{2-9}  & **Manual** & **Non Split** & **Split** & **Sown** & **Manual** & **Non Split** & **Split** \\ \hline
1 & 820 & 682 & 788 & 1200 & 0.683 & 0.568 & 0.657 \\
2 & 920 & 736 & 850 & 1400 & 0.657 & 0.526 & 0.607 \\
3 & 582 & 479 & 532 & 1200 & 0.485 & 0.399 & 0.443 \\
4 & 768 & 625 & 691 & 1200 & 0.640 & 0.521 & 0.57 for manual interpretation, non split, and split were 57.7%, 46.3%, and 52.6%, respectively. Furthermore, since the data collection in the study was conducted by a camera loaded on a UAV, it is more reasonable to evaluate the accuracy of the proposal method using the visual manual interpretation result as the real data rather than the number of seeds sown. Hence, the method presented in the study achieved an accuracy of 91.13%.