## 3 Results
### Machine Learning Classification
It was also discovered that the number of bands and the type of algorithms used affected the model performance; for example, cubic SVM took the longest time to develop a classification model for all bands, with an average of 33.9382 s. Furthermore, when using the 9 bands, Gaussian Naive Bayes provided the shortest performance time of 0.4503 s; however, the F-score was slightly lower than coarse Gaussian SVM by 2.49%. Overall, coarse Gaussian SVM of 9 bands has the shortest performance time of 1.7124 s with an F-score of 95.21%, making it the best model for classifying the I and U seedlings. Furthermore, Table 8 displayed the \(p\)-values of [PERSON]'s test, which examined only misclassifications of U and I within each model. If the counts of misclassification in U and I are similar or nearly identical, this implies that the models make approximately the same number of errors for both treatments. In this scenario, the result would not be significant, and the null hypothesis would not be rejected. Based on Table 8, nearly all models had \(p\)-values greater than 0.05 at the 95% confidence level, implying that there were no significant differences in the misclassification of U and I and that the models were not biased in favor of treatments. In comparison, only three models (fine tree and fine kNN of 35 bands and cubic SVM of 9 bands) obtained the \(p\)-values less than 0.05, showing that substantial differences existed in misclassifying the U and I. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**F-Score (\%)**} \\ \cline{2-6}
**Classification Model** & \multicolumn{5}{c}{**Number of Significant Bands**} \\ \cline{2-6}  & **35** & **18** & **9** & **14** & **11** \\ \hline Fine tree & 92.93 & 93.38 & 92.67 & 92.81 & 93.11 \\ Medium tree & 92.98 & 94.08 & 93.73 & 93.51 & 93.81 \\ Coarse tree & 95.08 & 95.45 & 94.43 & 94.53 & 95.11 \\ Linear discriminant & 91.50 & 93.85 & 94.27 & 93.59 & 93.93 \\ Quadratic discriminant & 78.23 & 89.84 & 90.49 & 89.90 & 92.16 \\ Logistic regression & 91.33 & 94.08 & 92.46 & 93.73 & 93.46 \\ Gaussian naive Bayes & 95.08 & 95.08 & 92.72 & 94.08 & 93.07 \\ Kernel naïve Bayes & 95.45 & 95.45 & 93.11 & 95.11 & 94.16 \\ Linear SVM & 95.45 & 95.77 & 94.16 & 95.11 & 95.45 \\ Quadratic SVM & 94.50 & 94.19 & 93.89 & 94.19 & 95.48 \\ Cubic SVM & 94.74 & 92.46 & 73.60 & 90.32 & 94.53 \\ Fine Gaussian SVM & 95.45 & 95.77 & 94.81 & 95.45 & 94.81 \\ Medium Gaussian SVM & 95.45 & 95.45 & 94.50 & 95.45 & 95.18 \\ Coarse Gaussian SVM & 95.45 & 95.45 & 95.21 & 94.87 & 94.87 \\ Fine kNN & 91.58 & 92.36 & 92.72 & 93.46 & 92.72 \\ Medium kNN & 95.45 & 95.77 & 94.50 & 95.45 & 95.15 \\ Coarse kNN & 95.45 & 95.45 & 94.84 & 95.15 & 95.18 \\ Cosine kNN & 92.98 & 93.33 & 93.02 & 93.33 & 93.69 \\ Cubic kNN & 95.45 & 95.77 & 94.50 & 95.45 & 95.15 \\ Weighted kNN & 95.45 & 95.77 & 94.81 & 95.11 & 94.81 \\ Bagged trees & 95.11 & 94.74 & 94.81 & 94.77 & 94.77 \\ Subspace discriminant & 93.11 & 93.89 & 94.30 & 94.57 & 94.23 \\ Subspace kNN & 91.28 & 93.42 & 93.38 & 93.46 & 93.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: F-score of models developed using the significant bands.