## 3 Experimental Validation
### Discussion  
To ensure the improved crop classification results of the SSODTL-CC model, a comparison study with recent models on two datasets is given in Table 7[22, 23]. Figure 6 investigates a comparative classification outcome of the SSODTL-CC model with existing models on dataset-1. The results indicated that the SVM model gained an ineffectual outcome with the least \(accu_{y}\) of 95.98%. In line with this, the FNEA-OO model certainly accomplished increased performance with an \(accu_{y}\) of 97.07%. In addition, the SVRFMC, CNN, and CNN-CRF models depicted closer \(accu_{y}\) values of 98.20%, 98.08%, and \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Testing Samples (30\%)**} \\ \hline
**Class Labels** & **Accuracy** & **Precision** & **Recall** & **Kappa Score** \\ \hline Corn & 99.37 & 98.68 & 95.85 & - \\ \hline Cotton & 99.26 & 95.5 & 98.02 & - \\ \hline Sesame & 98.85 & 93.33 & 96.22 & - \\ \hline Broad-leaf soybean & 99.37 & 95.21 & 98.93 & - \\ \hline Narrow-leaf soybean & 99.52 & 98.31 & 97.32 & - \\ \hline Rice & 99.11 & 97.74 & 93.53 & - \\ \hline Water & 99.37 & 97.32 & 96.99 & - \\ \hline Roads and Houses & 98.93 & 95.99 & 95.11 & - \\ \hline Mixed weed & 99.26 & 96.76 & 96.76 & - \\ \hline
**Average** & **99.23** & **96.54** & **96.53** & **96.08** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Result analysis of the SSODTL-CC technique with distinct classes under 30% of testing dataset-1. 98.80%, respectively. However, the SSODTL-CC model demonstrated superior performance with an \(accu_{y}\) of 99.23%. Figure 7 examines a comparative classification outcome of the SSODTL-CC model with existing approaches on dataset-2. The outcomes indicated that the SVM model gained an ineffectual outcome with the least \(accu_{y}\) of 77.34%. Likewise, the FNEA-OO model certainly accomplished an increased performance with an \(accu_{y}\) of 86.49%. Then, the SVRFMC, CNN, and CNN-CRF models depicted closer \(accu_{y}\) values of 86.95%, 87.72%, and 94.67%, correspondingly. At last, the SSODTL-CC methodology demonstrated superior performance with an \(accu_{y}\) of 97.15%. From these results and discussions, it is evident that the SSODTL-CC model has the capability of attaining improved crop classification outcomes on HSIs. Figure 5: Confusion matrix of the SSODTL-CC technique under dataset-2. (**a**) Entire dataset-2. (**b**) 70% of Training dataset-2, and (**c**) 30% of Testing dataset-2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Entire Dataset Samples**} \\ \hline
**Class Labels** & **Accuracy** & **Precision** & **Recall** & **Kappa Score** \\ \hline Class-1 & 97.39 & 79.57 & 78.30 & - \\ \hline Class-2 & 97.24 & 79.18 & 75.70 & - \\ \hline Class-3 & 97.14 & 77.37 & 76.60 & - \\ \hline Class-4 & 96.89 & 76.39 & 72.80 & - \\ \hline Class-5 & 96.63 & 73.42 & 72.10 & - \\ \hline Class-6 & 97.25 & 78.34 & 77.40 & - \\ \hline Class-7 & 97.07 & 76.63 & 76.40 & - \\ \hline Class-8 & 97.31 & 78.25 & 78.80 & - \\ \hline Class-9 & 97.41 & 80.06 & 77.90 & - \\ \hline Class-10 & 96.85 & 73.35 & 77.90 & - \\ \hline Class-11 & 96.87 & 75.80 & 73.30 & - \\ \hline Class-12 & 97.36 & 77.95 & 80.60 & - \\ \hline Class-13 & 97.39 & 80.40 & 77.10 & - \\ \hline Class-14 & 97.36 & 76.69 & 82.90 & - \\ \hline Class-15 & 96.84 & 75.41 & 73.30 & - \\ \hline Class-16 & 97.17 & 74.98 & 82.10 & - \\ \hline
**Average** & **97.13** & **77.11** & **77.08** & **75.55** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Result analysis of the SSODTL-CC technique with distinct classes under all of dataset-2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Training Samples (70\%)**} \\ \hline
**Class Labels** & **Accuracy** & **Precision** & **Recall** & **Kappa Score** \\ \hline Class-1 & 97.43 & 79.74 & 79.29 & - \\ \hline Class-2 & 97.31 & 78.98 & 78.42 & - \\ \hline Class-3 & 97.04 & 77.56 & 76.28 & - \\ \hline Class-4 & 96.77 & 75.11 & 72.77 & - \\ \hline Class-5 & 96.63 & 72.73 & 73.35 &