## 3 Experimental Validation
### Result Analysis of SSODTL-CC Model
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Entire Dataset Samples**} \\ \hline
**Class Labels** & **Accuracy** & **Precision** & **Recall** & **Kappa Score** \\ \hline Corn & 99.24 & 97.55 & 95.60 & - \\ \hline Cotton & 99.22 & 95.59 & 97.50 & - \\ \hline Sesame & 98.97 & 93.82 & 97.10 & - \\ \hline Broad-leaf soybean & 99.16 & 95.38 & 97.10 & - \\ \hline Narrow-leaf soybean & 99.39 & 98.07 & 96.40 & - \\ \hline Rice & 99.26 & 98.34 & 94.90 & - \\ \hline Water & 99.13 & 95.73 & 96.50 & - \\ \hline Roads and Houses & 99.17 & 96.67 & 95.80 & - \\ \hline Mixed weed & 99.27 & 96.70 & 96.70 & - \\ \hline
**Average** & **99.20** & **96.43** & **96.40** & **95.95** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Result analysis of the SSODTL-CC technique with distinct classes under all of dataset-1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Training Samples (70\%)**} \\ \hline
**Class Labels** & **Accuracy** & **Precision** & **Recall** & **Kappa Score** \\ \hline Corn & 99.19 & 97.04 & 95.49 & - \\ \hline Cotton & 99.21 & 95.63 & 97.27 & - \\ \hline Sesame & 99.02 & 94.01 & 97.46 & - \\ \hline Broad-leaf soybean & 99.06 & 95.45 & 96.38 & - \\ \hline Narrow-leaf soybean & 99.33 & 97.96 & 96.01 & - \\ \hline Rice & 99.32 & 98.57 & 95.43 & - \\ \hline Water & 99.03 & 95.07 & 96.29 & - \\ \hline Roads and Houses & 99.27 & 97.00 & 96.14 & - \\ \hline Mixed weed & 99.27 & 96.67 & 96.67 & - \\ \hline
**Average** & **99.19** & **96.38** & **96.35** & **95.89** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Result analysis of the SSODTL-CC technique with distinct classes under 70% of training dataset-1. class 9, 779 samples under class 10, 733 samples under class 11, 806 samples under class 12, 771 samples under class 13, 829 samples under class 14, 733 samples under class 15, and 821 samples under class 16. Similarly, the SSODTL-CC approach categorized the class labels proficiently on 70% of the training samples and 30% of the testing samples on dataset-2. Table 4 demonstrates the detailed crop classification outcomes of the SSODTL-CC model on all of dataset-2. The experimental values exposed that the SSODTL-CC model gained effectual outcomes under every individual class. For instance, in class 1, the SSODTL-CC algorithm obtained \(accu_{y}\), \(prec_{n}\), and \(reca_{l}\) of 97.39%, 79.57%, and 78.30% correspondingly. In addition, in class 16, the SSODTL-CC model gained \(accu_{y}\), \(prec_{n}\), and \(reca_{l}\) of 97.17%, 74.98%, and 82.10%, correspondingly. Overall, the SSODTL-CC model outperformed higher average \(accu_{y}\), \(prec_{n}\), and \(reca_{l}\) of 97.13%, 74.98%, and 82.10%, respectively. Table 5 reports a brief crop classification outcome of the SSODTL-CC model on 70% of training dataset-2. The experimental values exposed that the SSODTL-CC model gained effectual outcomes under every individual class. For instance, in class 1, the SSODTL-CC model offered \(accu_{y}\), \(prec_{n}\), and \(reca_{l}\) of 97.33%, 79.74%, and 79.29%, respectively. In addition, in class 16, the SSODTL-CC model reached \(accu_{y}\), \(prec_{n}\), and \(reca_{l}\) of 97.12%, 74.08%, and 81.65%, respectively. Overall, the SSODTL-CC methodology exhibited maximal average \(accu_{y}\), \(prec_{n}\), and \(reca_{l}\) of 97.13%, 77.08%, and 77.05%, correspondingly. Table 6 defines the detailed crop classification outcome of the SSODTL-CC technique on 30% of testing dataset-2. The experimental values indicated that the SSODTL-CC algorithm gained effectual outcomes under every individual class. For sample, in class 1, the SSODTL-CC model offered \(accu_{y}\), \(prec_{n}\), and \(reca_{l}\) of 97.29%, 79.15%, and 75.93%, correspondingly. In the same way, in class 16, the SSODTL-CC system reached \(accu_{y}\), \(prec_{n}\), and \(reca_{l}\) of 97.06%, 76.80%, and 82.99%, respectively. Overall, the SSODTL-CC approach showed maximal average \(accu_{y}\), \(prec_{n}\), and \(reca_{l}\) of 97.15%, 77.25%, and 77.09%, correspondingly.