## 4 Results and Discussion
### Discussion  
This research developed an enhanced deep learning classifier name called BILSTM with the LFSOA to effectively predict coconut production. From the overall analysis, the proposed BILSTM with the LFSOA is evaluated against the different optimization approaches such as GWO [35], WOA [36], and SOA [34] to evaluate its efficiency. Furthermore, the classification by BILSTM-LFSOA is analyzed against some other classifiers such as RNN, RFC, and LSTM. In the results, the LFSOA achieved an enhanced prediction accuracy of 98.963% which was better than the existing SOA [34], GWO [35], and WOA [36] which obtained 96.046%, 90.570%, and 93.623%, respectively. Similarly, the BILSTM with LFSOA achieved an enhanced prediction accuracy of 98.963% which is superior to existing RNN, RFC, and LSTM which obtained 93.431%, 96.390%, and 97.137%, respectively. Overall, the proposed BILSTM with LFSOA achieved higher results in all the performance metrics when compared with the conventional methods. The incorporation of both the backward and forward directions in BILSTM helps obtain complex and non-linear dependencies that lead to enhanced prediction performances. Even though BILSTM is a much slower model since it needs additional time for the training process, the incorporation of the LFSOA reduces the training time. The BILSTM model has some other limitations. Firstly, the model requires an adequately large training dataset to fetch input data from. In the case that the training dataset is not sufficiently large, a traditional LSTM model tends to outperform the BILSTM model in terms of loss [37]. Secondly, LSTM is faster in reaching its equilibrium loss values in comparison to BILSTM, which is likely attributable to the latter model requiring to be trained twice (once in the forward direction, and once in the backward direction) as compared to the LSTM which is only trained once. Finally, while both LSTM and BILSTM store prior sequences of data to enhance the accuracy of predictions, the size of the sequences that can be stored is greater in LSTM than in BILSTM due to the model training two LSTM models simultaneously, reducing the size of the sequences to half the size of sequences LSTM can store [37]. It is noted that, while the proposed model exceeds the performance of all other benchmark models, BILSTM on its own has lower accuracy (96.064%), precision (95.928%), recall (95.911%), and f1-score (95.758%) in comparison to both the RFC and LSTM models. This is likely to be attributable to the aforementioned limitations of the BILSTM model wherein it has both lower memory for stored sequences as compared to the traditional LSTM model, and requires a larger training dataset in order to tune its parameters. Given that LFSOA-based hyperparameter tuning results in BILSTM outperforming the tested models, it is likely that the reason for the difference in scores is attributable to insufficient training data to tune parameters. This indicates that the inclusion of the LFSOA may shorten the training time of the BILSTM model. Another factor to consider is that the BILSTM model is a \"black box\"--it is not completely understood how it arrives at the specific values that it does, which can make it difficult to properly explain to small stakeholders the crucial factors that result in the particular decision that is reached [38]. BILSTM especially suffers from this problem, as it incorporates hidden factors that may not be evident in the datasets but become essential to accurate forecasting. This can impact the perceived trustworthiness of the predictions. Rural adoption of smart technologies is also a critical factor that can hinder adoption of the model. Rural areas present a challenge to the successful implementation of the proposed model for a variety of reasons. Simply put, there is a lack of sufficient research to accurately predict rural responses [39].