## 4 Results and Discussion
### Performance Analysis of BILSTM-LFSOA  
The performance analysis of the proposed BILSTM-LFSOA was analyzed using various classifiers and optimization algorithms such as statistical measures (MAE, MSE, RMSE, and (R\({}^{2}\))) and classification measures (recall, accuracy, F1-score, and precision) are explained in this section. To analyze the LFSOA, many optimization-based hyperparameter tuning techniques were taken into consideration, including Grey Wolf Optimization (GWO) [35], the While Optimization Algorithm (WOA) [36], and SOA [34]. In this analysis, the GWO [35], WOA [36], and SOA [34] were developed for hyperparameter optimization for coconut yield prediction in order to analyze the effectiveness of the LFSOA. Figure 6 displays the fitness graph for the LFSOA with GWO [35], WOA [36], and SOA [34]. Figure 7 shows the comparison of the LFSOA with GWO [35], WOA [36], and SOA [34] for different population sizes. From this analysis, it is found that an LFSOA with a population size of 40 provides a better performance. Figure 6: Fitness graph. BILSTM-LFSOA's performance with various classifiers and optimization strategies for hyperparameter optimization is examined. Random Forest Classifier (RFC), Recurrent Neural Network (RNN), and LSTM are the three classifiers that are compared against the BILSTM. The analysis of statistical and prediction measures for classifiers with default hyperparameters is shown in Tables 1 and 2. According to this investigation, the BILSTM outperforms the RNN, RFC, and LSTM in performance. For instance, the accuracy of BILSTM with default hyperparameters is 96.467%, whereas that of RNN, RFC, and LSTM is 90.390%, 92.756%, and 94.502%, respectively. In comparison to previous classifiers, the crop yield estimate is improved by using both historical and future data throughout the prediction process. Tables 3 and 4 show the statistical and prediction measures of classifiers with optimized hyperparameters. This investigation demonstrates that compared to RNN, RFC, and LSTM, the BILSTM with optimized hyperparameters performs better. For example, the accuracy of BILSTM with optimized hyperparameters is 98.963%, whereas the RNN has an accuracy of 93.431%, RFC has 96.390%, and LSTM has 97.137%. Furthermore, compared to the one with default hyperparameters, the BILSTM with optimized hyperparameters performs better. To minimize the loss function, the ideal collection of hyperparameters discovered by the LFSOA is being used. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifiers** & **MAE** & **MSE** & **RMSE** & **R2** \\ \hline RNN & 0.412 & 0.443 & 0.666 & 0.618 \\ RFC & 0.304 & 0.399 & 0.632 & 0.697 \\ LSTM & 0.278 & 0.281 & 0.530 & 0.770 \\ BILSTM & 0.133 & 0.108 & 0.329 & 0.857 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Analysis of statistical measures for classifiers with default hyperparameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifiers** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline RNN & 90.390 & 89.879 & 89.704 & 90.205 \\ RFC & 92.756 & 92.166 & 93.999 & 92.676 \\ LSTM & 94.502 & 93.908 & 94.516 & 94.327 \\ BILSTM & 96.467 & 97.627 & 97.172 & 96.135 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Analysis of prediction measures for classifiers with default hyperparameters. Figure 7: Analysis of optimization for different population sizes. The analysis of statistical and prediction measures for different optimizations is shown in Tables 5 and 6. This investigation demonstrates that the LFSOA outperforms the GWO [35], WOA [36], and SOA [34]. For example, the accuracy of the LFSOA with default hyperparameters is 98.963%, whereas the GWO [35] is 90.570%, WOA [36] is 93.623%, and SOA [34] is 96.046%. The incorporation of LF in SOA is used to handle the local search which helps to achieve a more optimum set of hyperparameters than the other methods. Moreover, the tuning of hyperparameters avoids the overfitting issue and additionally helps to enhance the estimation performances.