## 4 Framework
### HPA-C Framework
The lack of significant differences among PSO, GA, and GWO highlights that conventional population-based methods may not always offer substantial distinctions in feature selection performance, emphasizing the need for more advanced hybrid approaches like HPA-C.

Figure 4: Accuracy plots for 9 datasets and 5 FS techniques. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Source** & **SS** & **df** & **MS** & **F** & \(p\)**-Value** \\ \hline Columns & 0.23797 & 4 & 0.05949 & 7.88 & \(8.776\times 10^{-5}\) \\ Error & 0.30189 & 40 & 0.00755 & – & – \\ Total & 0.53986 & 44 & – & – & – \\ \hline \hline \end{tabular}
\end{table} 
Table 8: ANOVA results for accuracy. To provide a comprehensive evaluation, we retained all three in the comparative analysis. The analysis showed that HPA-C is not only superior to a statistical method (RelieFF) but also to three widely used metaheuristic techniques. While redundancy in performance exists, excluding any of these methods would weaken the generalizability of the findings. In addition to the ANOVA, a Wilcoxon signed-rank test was also performed to validate the superiority of HPA-C. The test results, summarized in Table 9, prove that HPA-C outperforms all other used benchmark feature selection techniques, with \(p<0.05\) in all cases. This value provides strong statistical evidence for the robustness of HPA-C across diverse datasets. Figure 5: ANOVA box plots. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Comparison** & \(\mathbf{p}\)**-Value** & **Significance** \\ \hline HPA-C against RelieFF & 0.0078 & Significant \\ HPA-C against PSO & 0.0195 & Significant \\ HPA-C against GA & 0.0039 & Highly Significant \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Wilcoxon signed-rank test results. The results clearly prove that HPA-C significantly improves classification accuracy when compared to RelieFF, PSO, GA, and GWO. HPA-C consistently performs better than the benchmark approaches. Statistical analysis indicates that HPA-C is a robust and effective feature selection technique for smart agricultural applications and beyond. It demonstrated strong generalization across diverse datasets. ### Feature Selection Consistency Across Datasets

An analysis of feature selection patterns across datasets revealed that while selected features varied based on dataset characteristics, certain features were consistently prioritized, indicating that HPA-C effectively identifies highly discriminative features that enhance classification performance. The texture-based and color histogram features were frequently selected in plant disease and fruit classification tasks, while spectral and shape-based features were dominant in weed detection and remote sensing datasets. This consistency suggests that HPA-C captures domain-relevant attributes rather than selecting features arbitrarily. ### Convergence and Wall-Clock (Elapsed) Time

The convergence plot for HPA-C and GWO using the Plant Leaf Diseases dataset is shown in Figure 6.

In terms of computational time, HPA-C required 5631.75 s, while GWO completed the feature selection process in 5224.16 s. This marginal increase of approximately 10%

Figure 6: Convergence plot using Plant Leaf Diseases dataset. in elapsed time for HPA-C is justified by its enhanced search capabilities and improved feature selection. Moreover, the theoretical complexity of the two algorithms remains similar, as discussed in Section 4.3, ensuring that hybridization does not impose significant computational overhead. ### Challenges and Future Work

While this research demonstrates the effectiveness of the proposed hybrid feature selection technique and custom CNN for smart agricultural datasets, several challenges highlight areas for further exploration and improvement. One of the key challenges encountered during implementation was the limitation of computational resources, particularly GPU memory.