Forecasting Volume of Corn Production through Neural Network Model: A Post-harvest Monitoring Tool
Paper Details
Forecasting Volume of Corn Production through Neural Network Model: A Post-harvest Monitoring Tool
Abstract
The paper deals with the forecasting of volume (in tonnes) of corn production relative to the harvested farmed area during the second semester of agricultural cropping. Time series data used were obtained from the open stat database published by the Philippine Statistics Authority from the second semester of 1987 to second semester of 2022. Artificial Neural Network (ANN) models were developed, trained and validated to forecast the volume of corn production. Statistical errors such as Root Mean Square Error (RMSE) were computed and compared to identify the most suitable model to forecast the corresponding volume. ANN () model was identified and used to forecast the volume of corn production. The two sets of data, namely actual and forecast volumes of production, were found to have statistically no significant difference, which implies that the model gives forecast values that are relatively close to the actual ones.
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