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Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow

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dc.citedby3
dc.contributor.authorKhan N.en_US
dc.contributor.authorKamaruddin M.A.en_US
dc.contributor.authorUllah Sheikh U.en_US
dc.contributor.authorZawawi M.H.en_US
dc.contributor.authorYusup Y.en_US
dc.contributor.authorBakht M.P.en_US
dc.contributor.authorMohamed Noor N.en_US
dc.contributor.authorid57215962833en_US
dc.contributor.authorid44361188400en_US
dc.contributor.authorid57191516181en_US
dc.contributor.authorid39162217600en_US
dc.contributor.authorid24340055400en_US
dc.contributor.authorid57208424121en_US
dc.contributor.authorid25221616600en_US
dc.date.accessioned2023-05-29T09:37:07Z
dc.date.available2023-05-29T09:37:07Z
dc.date.issued2022
dc.description.abstractCurrent development in precision agriculture has underscored the role of machine learning in crop yield prediction. Machine learning algorithms are capable of learning linear and nonlinear patterns in complex agro-meteorological data. However, the application of machine learning methods for predictive analysis is lacking in the oil palm industry. This work evaluated a supervised machine learning approach to develop an explainable and reusable oil palm yield prediction workflow. The input data included 12 weather and three soil moisture parameters along with 420 months of actual yield records of the study site. Multisource data and conventional machine learning techniques were coupled with an automated model selection process. The performance of two top regression models, namely Extra Tree and AdaBoost was evaluated using six statistical evaluation metrics. The prediction was followed by data preprocessing and feature selection. Selected regression models were compared with Random Forest, Gradient Boosting, Decision Tree, and other non-tree algorithms to prove the R2 driven performance superiority of tree-based ensemble models. In addition, the learning process of the models was examined using model-based feature importance, learning curve, validation curve, residual analysis, and prediction error. 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 the means of machine learning have great potential for the application to predict oil palm yield using weather and soil moisture data. � 2022 by the authors. Licensee MDPI, Basel, Switzerland.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo1697
dc.identifier.doi10.3390/plants11131697
dc.identifier.issue13
dc.identifier.scopus2-s2.0-85132834355
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85132834355&doi=10.3390%2fplants11131697&partnerID=40&md5=f7ac59ff956f29f298ee80cd2541e440
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26839
dc.identifier.volume11
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitlePlants
dc.titlePrediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflowen_US
dc.typeArticleen_US
dspace.entity.typePublication
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