## 5 Conclusions  
In this research, the hyperparameters for BILSTM were optimized using the proposed LFSOA that helped to avoid the overfitting risk and effectively forecast the coconut yield. Also, the optimized features and optimized hyperparameter with BILSTM were used to enhance the crop yield prediction performances. The contributions of this study are as follows:  
* The LFSOA model was compared against GWO, WOA, and SOA. The results indicate that the LFSOA outperforms these benchmark models in terms of accuracy, precision, recall, and f1-score. The LFSOA scored 98.963%, 99.026%, 99.155%, and 98.837% in these areas, respectively, which is higher than the next-highest score attained by SOA. SOA attains 96.046%, 95.402%, 96.817%, and 95.384% in the aforementioned metrics, respectively. * The prediction results of BILSTM with and without LFSOA were analyzed, showing that BILSTM without LFSOA scores lower than BILSTM with LFSOA. BILSTM without LFSOA was shown to score 96.064%, 95.928%, 95.911%, and 95.758% in accuracy, precision, recall, and f1-score, respectively. Meanwhile, BILSTM-LFSOA scored 98.963%, 99.026%, 99.155%, and 98.837%, respectively, in each of these areas. * These results were compared against RNN, RFC, and LSTM models. It was found that while BILSTM-LFSOA outperformed all the benchmark models, with the next-highest score being that of the LSTM model (97.137%, 98.231%, 97.205%, and 97.061%, respectively), both LSTM and RFC outperformed the simple BILSTM model. This is likely attributable to the size of the dataset. As noted in [37], while the BILSTM model has the potential to outperform unilateral LSTM models in time series forecasting, the model requires a sufficiently large dataset so as to properly tune its parameters. The inclusion of the LFSOA to tune hyperparameters was likely one reason for the higher score of the BILSTM-LFSOA model as compared to the simple BILSTM model. * The superior predictive power of the BILSTM model when compared to benchmark models is likely to be attributable to the incorporation of forward and backward directions, which allows it to more accurately obtain long-term complex and non-linear dependencies in the dataset. In the future, the BILSTM can be integrated with the attention mechanism for enhancing the coconut yield estimation by handling the long-term dependencies. One possible limitation of the model presented can be the reliance on sensors to gather accurate data readings, which can be costly to implement at a larger scale. Additionally, the model requires large amounts of data to create accurate estimations of yield outcomes. The limited geographical area in which the study was conducted also puts constraints on the generalization potential of the study. Finally, the variables considered within the scope of this study are not necessarily the only factors influencing crop yields. Further, future research will focus on factors such as soil composition, pesticide use, and agricultural site characteristics to create a more complete production prediction model. However, future research could focus on alternate methods to gather data without the loss of accuracy in the dataset. Additionally, the BILSTM could be integrated with the attention mechanism for enhancing the coconut yield estimation by handling the long-term dependencies. Another avenue for future research could be to examine alternative methods of addressing the overfitting risk. While this study uses hyperparameter tuning to reduce overfitting risk, other potential techniques could be deployed to reduce overfitting, such as early stopping mechanisms. There is also potential to examine whether the inclusion of the LFSOA in the other benchmark models raises their performance to the point that it can challenge BILSTM. Finally, future research could incorporate other factors influencing crop yields in their study. Conceptualization, S.H.S. and A.R. ; methodology, S.Z. and S.P. ; software, V.V. and G.F.; validation; formal analysis, R.N.A. and G.F.; investigation, S.H.S. and S.P. ; resources, V.V.