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Summary
Predicting the price of crude palm (CPO) oil is vital for resources management, especially in agricultural farms. However, the price of CPO is very volatile in uncertain economic conditions and the agricultural environment. In addition to this volatility, the objectives of the studywas achieved. This study will benefit the producers, traders and consumers in the production of palm oil. It will enable them to forecast the palm oil price by analyzing the location, current price, climate change and season of palm oil. From the result and data analysis, it could be considered a huge success to research on the Price of Palm Oil. Results indicated that rainfall frequency, root-zone soil moisture, and temperature could make a significant impact on oil palm yield. Most influential features that contributed to the prediction process are rainfall, cloud amount, number of rain days, wind speed, and root zone soil wetness. It is concluded that Machine Learning have great potential for the application to predict oil palm yield using weather and soil moisture data.
