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Reliable and accurate predictions in oil palm production can provide the basis for management decisions of budgeting, storage, distribution, and marketing. Artificial Neural Network (ANN) and Non-linear Autoregressive Exogenous Neural Network (NARX) models were developed based on 19 440 data set of 15 inputs variables, namely, percentage of mature area and percentage of immature area, rainfall, rainy days, humidity, radiation, temperature, surface wind speed, evaporation and cloud cover, ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), and particulate matter of less than 10 microns in size (PM10) for predicting oil palm fresh fruit bunch (FFB). The results were validated with an independent validation dataset. Results showed that NARX models performed more accurately with multiple coefficients of determination (R2) reached 97% and mean square errors (MSE) between 0.0104%-0.0665%, besides being an easy-to-use tool. Generally, NARX models proved to give more accurate predictions than the predictions of common ANN and Multi Linear Regression (MLR) models. Finally, 15-10-4-1 is chosen as the architecture of NARX for the states of Kedah, Kelantan, Perak, Pahang, Selangor, and Terengganu. The 15-7-4-1 is the best architecture of NARX for the state of Melaka and Pulau Pinang, while 15-13-4-1 architecture is for the state of Johor. This study showed that all of these architectures gave high accuracy with acceptable MSE values.

NEURAL NETWORKS METHOD IN PREDICTING OIL PALM FFB YIELDS FOR THE PENINSULAR STATES OF MALAYSIA