PREDICTION OF OIL PALM YIELD USING MACHINE LEARNING: COMPARISON OF LINEAR AND NON-LINEAR ALGORITHMS WITH MULTIVARIATE TIME SERIES DATA

farming operations (An-Vo et al., 2021).These technologies enable the collection and analysis of data on weather patterns, soil conditions, crop yields, and pest populations to facilitate smarter decision-making and more efficient resource management (Khan et al., 2022b).Among these advancements, AI-powered predictive analytics play a crucial role in forecasting crop yields for future planning.They also help farmers to improve food security by adapting to climate variability (Javaid et al., 2023).Crop yield prediction is a complex task influenced by various factors, including genetics, soil conditions, weather and management practices (Khaki et al., 2020).Traditional methods, such as field surveys and crop growth models, have limitations