Oil palm yield depends on factors like plant physiology, meteorology, and soil quality. The interaction of these factors remains unclear. Machine learning can reveal these interactions, creating a data-driven model for accurate yield prediction. Hence, this cluster will develop an operational model to predict oil palm yield accurately.
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RM 459 k
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Universiti Sains Malaysia
Universiti Sains Malaysia
Universiti Sains Malaysia
Universiti Sains Malaysia
Universiti Sains Malaysia
FGV R&D
FGV R&D
Universiti Putra Malaysia
Universiti Sains Malaysia
Universiti Sains Malaysia
Universiti Sains Malaysia
Universiti Sains Malaysia
FGV R&D
FGV R&D
Universiti Sains Malaysia
Universiti Sains Malaysia
Universiti Sains Malaysia
Universiti Sains Malaysia
Universiti Sains Malaysia
FGV R&D
FGV R&D
A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction
https://doi.org/10.1109/ACCESS.2021.3075159Oil palm and machine learning: Reviewing one decade of ideas, innovations, applications, and gaps
https://doi.org/10.3390/agriculture11090832Detecting Outliers in a Univariate Time Series Dataset using Unsupervised Combined Statistical Methods: A Case Study on Surface Water Temperature
https://doi.org/10.1016/j.ecoinf.2022.101672Prediction of oil palm yield using machine learning in the perspective of fluctuating weather and soil moisture conditions: evaluation of a generic workflow
https://doi.org/10.3390/plants11131697Environment-Based Oil Palm Yield Prediction Using K-Nearest Neighbour Regression
https://doi.org/10.1109/IICAIET55139.2022.9936752Oil Palm Yield Gap Prediction Using Machine Learning: A Proof of Concepts
https://doi.org/10.1109/MACS56771.2022.10022394A novel ensemble machine learning and time series approach for oil palm yield prediction using Landsat time series imagery based on NDVI
https://doi.org/10.1080/10106049.2022.2025920Oil palm yield prediction across blocks from multi-source data using machine learning and deep learning
https://doi.org/10.1007/s12145-022-00882-9Oil Palm Plantation Land Cover and Age Mapping Using Sentinel-2 Satellite Imagery and Machine Learning Algorithms
https://doi.org/10.1088/1755-1315/1051/1/012024Differences in CO2 Emissions on a Bare-Drained Peat Area in Sarawak, Malaysia, Based on Different Measurement Techniques
https://doi.org/10.3390/agriculture13030622Seasonal and Yearly Controls of CO2 Fluxes in A Tropical Coastal Ocean
https://doi.org/10.1175/EI-D-22-0023.1Mapping Oil Palm Plantations Using WorldView-2 Satellite Imagery and Machine Learning Algorithms
https://doi.org/10.1088/1755-1315/1240/1/012013Predicting oil palm yield using a comprehensive agronomy dataset and 17 machine learning and deep learning models
http://dx.doi.org/10.1016/j.ecoinf.2024.102595
