Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow
Current 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 predicti...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Plants |
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| Online Access: | https://www.mdpi.com/2223-7747/11/13/1697 |
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| author | Nuzhat Khan Mohamad Anuar Kamaruddin Usman Ullah Sheikh Mohd Hafiz Zawawi Yusri Yusup Muhammed Paend Bakht Norazian Mohamed Noor |
| author_facet | Nuzhat Khan Mohamad Anuar Kamaruddin Usman Ullah Sheikh Mohd Hafiz Zawawi Yusri Yusup Muhammed Paend Bakht Norazian Mohamed Noor |
| author_sort | Nuzhat Khan |
| collection | DOAJ |
| description | Current 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 R<sup>2</sup> 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. |
| first_indexed | 2024-03-09T03:57:11Z |
| format | Article |
| id | doaj.art-1420e1892fbe4f7d9ebe1af92e9571f9 |
| institution | Directory Open Access Journal |
| issn | 2223-7747 |
| language | English |
| last_indexed | 2024-03-09T03:57:11Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Plants |
| spelling | doaj.art-1420e1892fbe4f7d9ebe1af92e9571f92023-12-03T14:17:42ZengMDPI AGPlants2223-77472022-06-011113169710.3390/plants11131697Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic WorkflowNuzhat Khan0Mohamad Anuar Kamaruddin1Usman Ullah Sheikh2Mohd Hafiz Zawawi3Yusri Yusup4Muhammed Paend Bakht5Norazian Mohamed Noor6School of Industrial Technology, Universiti Sains Malaysia, Gelugor 11800, MalaysiaSchool of Industrial Technology, Universiti Sains Malaysia, Gelugor 11800, MalaysiaSchool of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, MalaysiaDepartment of Civil Engineering, Universiti Tenaga Nasional, Kajang 43000, MalaysiaSchool of Industrial Technology, Universiti Sains Malaysia, Gelugor 11800, MalaysiaSchool of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, MalaysiaSustainable Environment Research Group (SERG), Centre of Excellence Geopolymer and Green Technology (CEGeoGTech), Faculty of Civil Engineering Technology, Universiti Malaysia Perlis, Arau 01000, MalaysiaCurrent 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 R<sup>2</sup> 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.https://www.mdpi.com/2223-7747/11/13/1697oil palmcrop yieldpredictionmachine learningprecision agriculturesustainability |
| spellingShingle | Nuzhat Khan Mohamad Anuar Kamaruddin Usman Ullah Sheikh Mohd Hafiz Zawawi Yusri Yusup Muhammed Paend Bakht Norazian Mohamed Noor Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow Plants oil palm crop yield prediction machine learning precision agriculture sustainability |
| title | Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow |
| title_full | Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow |
| title_fullStr | Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow |
| title_full_unstemmed | Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow |
| title_short | Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow |
| title_sort | prediction of oil palm yield using machine learning in the perspective of fluctuating weather and soil moisture conditions evaluation of a generic workflow |
| topic | oil palm crop yield prediction machine learning precision agriculture sustainability |
| url | https://www.mdpi.com/2223-7747/11/13/1697 |
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