A novel ensemble machine learning and time series approach for oil palm yield prediction using Landsat time series imagery based on NDVI

Abstract

Accurate oil palm yield prediction is necessary to sustain oil palm production for food security and economic return. However, there are limited studies on comprehensive mapping and accurate oil palm yield prediction using advanced machine learning algorithms. Using multi-temporal remote sensing data, this paper proposed a new approach to predict oil palm yield based on the normalized difference vegetation index (NDVI) and ensemble machine learning algorithm. ReliefF algorithm with linear projection was employed to select the best combination of spectral indices in oil palm discrimination. Oil palm land cover was classified using random forest (RF) and modified AdaBoost algorithms. A time-series approach known as walk-forward validation was firstly introduced to train the model using the 2016-2019 data and the one-step prediction was performed for 2020 using RF and AdaBoost. Result of the study revealed that the RF model (RMSE = 0.384; MSE = 0.148; MAE = 0.147) outperformed the AdaBoost model (RMSE = 0.410; MSE = 0.168; MAE = 0.176). Our research has demonstrated the value of detailed mapping and subsequent yield prediction by developing a novel approach utilising time-series satellite imagery, ensemble machine learning, and NDVI, which will assist decision-makers in managing their practices related to oil palm.

Keywords:

• NDVI
• oil palm yield forecasting
• AdaBoost
• random forest (RF)
• walk-forward validation

Acknowledgments

The authors would also like to extend a special acknowledgment to the team from FGV R&D Sdn. Bhd. for their expertise in supporting this research.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The researchers would like to acknowledge the Ministry of Higher Education (MOHE), Malaysia for the resources and financial support through Long Term Research Grant Scheme (LRGS)-Malaysia Research University Network (MRUN). This project is conducted under the research programme: ‘A Big Data Analytics Platform for Optimizing Oil Palm Yield Via Breeding by Design (Grant No: 203.PKOMP.6770007)’ with specific project: ‘Geoinformatics Data for Palm Oil Yield Prediction Using Machine Learning (Vote No: 6300268-10801)’.