## 4 Framework
### HPA-C Framework
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Dataset** & **Feature Selection** & **Accuracy** & **Macro Precision** & **Macro Recall** & **Macro F1 Score** \\ \hline \multirow{4}{*}{Plant Leaf Diseases} & RelieFF & 0.826 & 0.805 & 0.790 & 0.790 \\  & PSO & 0.978 & 0.974 & 0.974 & 0.974 \\  & GA & 0.973 & 0.968 & 0.967 & 0.967 \\  & GWO & 0.983 & 0.979 & 0.979 & 0.979 \\  & HPA-C & 0.986 & 0.983 & 0.983 & 0.983 \\ \hline \multirow{4}{*}{Weed Detection} & RelieFF & 0.990 & 0.993 & 0.977 & 0.984 \\  & PSO & 0.994 & 0.990 & 0.992 & 0.991 \\  & GA & 0.996 & 0.994 & 0.995 & 0.995 \\  & GWO & 0.997 & 0.996 & 0.996 & 0.996 \\  & HPA-C & 0.998 & 0.998 & 0.997 & 0.997 \\ \hline \multirow{4}{*}{Fruits-360} & RelieFF & 0.987 & 0.987 & 0.987 & 0.987 \\  & PSO & 0.991 & 0.990 & 0.990 & 0.990 \\  & GA & 0.981 & 0.980 & 0.981 & 0.981 \\  & GWO & 0.982 & 0.981 & 0.981 & 0.981 \\  & HPA-C & 0.986 & 0.986 & 0.986 & 0.985 \\ \hline \multirow{4}{*}{Fresh n Rotten} & RelieFF & 0.885 & 0.813 & 0.793 & 0.794 \\  & PSO & 0.991 & 0.981 & 0.979 & 0.980 \\  & GA & 0.983 & 0.967 & 0.964 & 0.964 \\  & GWO & 0.993 & 0.984 & 0.984 & 0.983 \\  & HPA-C & 0.998 & 0.994 & 0.994 & 0.993 \\ \hline \multirow{4}{*}{UC-MERCED} & RelieFF & 0.640 & 0.642 & 0.640 & 0.640 \\  & PSO & 0.940 & 0.943 & 0.940 & 0.940 \\  & GA & 0.947 & 0.948 & 0.947 & 0.947 \\  & GWO & 0.956 & 0.956 & 0.954 & 0.955 \\  & HPA-C & 0.962 & 0.964 & 0.962 & 0.962 \\ \hline \multirow{4}{*}{AID} & RelieFF & 0.584 & 0.584 & 0.577 & 0.566 \\  & PSO & 0.914 & 0.915 & 0.912 & 0.913 \\  & GA & 0.889 & 0.886 & 0.884 & 0.885 \\  & GWO & 0.908 & 0.907 & 0.908 & 0.906 \\  & HPA-C & 0.927 & 0.926 & 0.926 & 0.925 \\ \hline \multirow{4}{*}{NWPU-RESISC45} & RelieFF & 0.547 & 0.547 & 0.547 & 0.541 \\  & PSO & 0.853 & 0.855 & 0.854 & 0.854 \\  & GA & 0.834 & 0.835 & 0.834 & 0.833 \\  & GWO & 0.854 & 0.857 & 0.854 & 0.854 \\  & HPA-C & 0.882 & 0.883 & 0.882 & 0.882 \\ \hline \multirow{4}{*}{RSSCN7} & RelieFF & 0.744 & 0.760 & 0.744 & 0.741 \\  & PSO & 0.896 & 0.898 & 0.896 & 0.896 \\  & GA & 0.876 & 0.880 & 0.876 & 0.877 \\  & GWO & 0.914 & 0.914 & 0.914 & 0.914 \\  & HPA-C & 0.926 & 0.926 & 0.925 & 0.925 \\ \hline \multirow{4}{*}{SIRI-WHU} & RelieFF & 0.666 & 0.664 & 0.666 & 0.661 \\  & PSO & 0.927 & 0.930 & 0.927 & 0.927 \\ \cline{1-1}  & GA & 0.920 & 0.920 & 0.920 & 0.920 \\ \cline{1-1}  & GWO & 0.927 & 0.927 & 0.927 & 0.927 \\ \cline{1-1}  & HPA-C & 0.929 & 0.929 & 0.929 & 0.928 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance metrics of feature selection methods across datasets using DenseNet-201(FE) and a custom CNN. ### Statistical Analysis

The effectiveness of the proposed HPA-C feature selection technique was evaluated by conducting a comparative analysis against four benchmark methods: RelieFF, PSO, GA, and GWO. The classification accuracy was measured across nine diverse datasets related to smart agriculture and remote sensing. To ensure a statistically rigorous comparison, we performed a one-way ANOVA, followed by [PERSON]'s HSD post hoc test and the Wilcoxon signed-rank test. The box plot obtained from the ANOVA is presented in Figure 5. The results of the one-way ANOVA presented in Table 8 indicate a statistically significant difference among the feature selection techniques (\(p=8.776\times 10^{-5}\), \(F=7.88\)). This suggests that at least one of the methods performs significantly better than the others. The statistical analysis revealed that PSO, GA, and GWO do not exhibit significant differences in performance. This observation suggests the convergence to similar optima in the given datasets. However, their inclusion is still meaningful because it demonstrates that HPA-C outperforms not just one but multiple representative optimization-based approaches.