## 2 Related Work
### Different Types of Crop Yield Prediction  
[PERSON] et al. (2021) described many machine learning techniques including backpropagation neural networks, radial basis functional neural networks, support vector regression, and general regression neural networks (GRNN) to estimate the paddy yield. The eastern part of Tamil Nadu, South India, which is home to the Cauvery Delta Zone (also known as the CDZ), was the target of this work's rice yield calculation. Utilizing GRNN's forward data propagation, the results of the prediction were improved. Pre-processing and redundant data removal should be included to further improve the prediction.  
In order to estimate agricultural productivity, [PERSON] et al. (2020) developed a hybrid feature extraction technique that paired a Random Forest Recursive Feature Elimination (RFRFE) wrapper with a correlation-based filter (CFS). A variety of machine learning techniques, including random forest, gradient boosting, and decision trees, were used to analyze the generated CFS-RFRFE. The non-essential and superfluous features were removed by designing the filter approach using the correlation. The developed CFS-RFRFE only considered the correlation measure during the feature extraction.  
To calculate the productivity of agriculture, [PERSON] and [PERSON] (2021) introduced a hybrid regression-based system based on reinforcement learning and random forests. This hybrid regression was used in order to apply reinforcement learning to each choice of splitting characteristics between tree buildings. Variable significance measure was examined in order to choose significant variables for the node splitting process during the model's development and to facilitate efficient use of training data. By using internal cross-validation, the problem of overfitting was avoided and minimal parameter adjustment was achieved. However, the data was not divided using the proposed hybrid technique in a way that would have had an impact on prediction results.  
To forecast agricultural output, [PERSON] and [PERSON] (2021) created an improved ensemble regression based on mutual information. The main statistical method for identifyingfeature association between the datasets was the mutual information-based feature selection. Reducing the amount of data input was necessary to assist the classification or regression problem. The data that varied over time was required to be considered for an effective prediction of the crop.  
From the overall literature review, the following issues are highlighted: ineffective data incorporated into the prediction, a failure to analyze the complex and non-linear dependencies, and overfitting risk. It is difficult to calculate how several climatic elements, such as temperature, humidity, wind, and precipitation, interact with one another and affect crop yields. These requirements can be met by a deep learning model, which enables data-driven agricultural decision-making by producing precise and reliable crop predictions. In order to provide effective coconut prediction, this study implemented a deep learning method, BILSTM, with integrated LFSOA. To overcome the aforementioned issues, the following solutions are provided by the proposed method. For processing effective data to perform reliable prediction, the IoT sensors are used to obtain the data from the field. The information utilization from both the backward and forward directions is used to capture complex and non-linear dependencies for enhancing prediction. Finally, the tuning of hyperparameters using the LFSOA helps to avoid overfitting risk. Along with the hyperparameters, a penalty term to the loss function is added to restrain the large weights which supports to prevention of overfitting.