Estimating Coconut Yield Production using Hyperparameter Tuning of Long Short-Term Memory

Coconut production is one of the significant and main sources of revenue in India. In this research, an Auto-Regressive Integrated Moving Average (ARIMA)-Improved Sine Cosine Algorithm (ISCA) with Long Short-Term Memory (LSTM) is proposed for coconut yield production using time series data. It is used for converting non-stationary data to stationary time series data by applying differences. The Holt-Winter Seasonal Method is the Exponential Smoothing variations utilized for seasonal data. The time-series data are given as the input to the LSTM classifier to classify the yield production and the LSTM model is tuned by hyperparameter using Improved Sine Cosine Algorithm (ISCA). In basic SCA, parameter setting and search precision are crucial and the modified SCA improves the coverage speed and search precision of the algorithm. The model’s performance is estimated by utilizing R2, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE) with date on yield from 2011-2021 by categorizing yearly production into 120 records and eight million nuts. The outcomes display that the LSTM-ISCA offers values of 0.38, 0.126, 0.049 and 0.221 for R2, MAE, MSE and RMSE metrics, which offer a precise yield production when related to other models.

@article{Jayanna2024,
  title     = {Estimating Coconut Yield Production using Hyperparameter Tuning of Long Short-Term Memory},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {4},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Niranjan Shadaksharappa Jayanna and Raviprakash Madenur Lingaraju},
  doi       = {10.14569/IJACSA.2024.0150478},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150478}
}