Oil Palm Yield Prediction Across Blocks Using Multi-Source Data and Machine Learning Article Swipe
Yuhao Ang
,
Helmi Zulhaidi Mohd Shafri
,
Yang Ping Lee
,
Shahrul Azman Bakar
,
Haryati Abidin
,
Mohd Umar Ubaydah Mohd Junaidi
,
Shaiful Jahari Hashim
,
Nik Norasma Che’Ya
,
Mohd Roshdi Hassan
,
H. S. Lim
,
Rosni Abdullah
,
Yusri Yusup
,
Syahidah Akmal Muhammad
,
Sin Yin Teh
,
Mohd Na’aim Samad
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-1938493/v1
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-1938493/v1
Predicting yields on a bigger scale in a timely and accurate manner is essential for preventing climate risk and ensuring food security, particularly in the light of climate change and the escalation of extreme climatic events. Furthermore, crop yield estimates are affected by various factors including weather, nutrients and management practices. In this study, integrating multi-source data (i.e. satellite-derived vegetation indices (VIs), satellite-derived climatic variables (i.e. land surface temperature (LST) and rainfall precipitation, weather station and field-surveys), we built one multiple linear regression (MLR), three machine learnings (XGBoost, support vector regression, and random forest) and one deep learning (deep neural network) model to predict oil palm yield at block-level within the oil palm plantation. Moreover, time-series moving average and backward elimination feature selection techniques were implemented at the pre-processing stage. The yield prediction models were developed and tested using MLR, XGBoost, support vector regression (SVR), random forest (RF) and deep neural network (DNN) algorithms. Their model performances were then compared using evaluation metrics and generated the final spatial prediction map based on the best performance. DNN achieved the best model performances for both selected (R 2 =0.91; RMSE= 2.92 tonnes per ha; MAE= 2.56 tonnes per ha and MAPE= 0.09) and full predictors (R 2 =0.76; RMSE of 3.03 tonnes per ha; MAE of 2.88 tonnes per ha; MAPE of 0.10 tonnes per ha). In addition, advanced ensemble ML techniques such as XGBoost may be utilised as a supplementary for oil palm yield prediction at the block level. Among them, MLR recorded the lowest performance. By using backward elimination to identify the most significant predictors, the performance of all models was improved by 5% - 26% for R 2 , and that decreased by 3% - 31% for RMSE, 7% - 34% for MAE, and 1% - 15% for MAPE, respectively. DNN generates the most accurate statistical metrics, with an increase of around 15% for R 2 , 11% for RMSE, 32% for MAE and 1% for MAPE. Our study successfully developed efficient, effective and accurate yield prediction models for timely predicting oil palm yield over a large area by integrating data from multiple sources. These can be potentially handled by plantation management to estimate oil palm yields to speed up the decision-making process for sustainable production.
Related Topics
Concepts
Support vector machine
Random forest
Multicollinearity
Artificial neural network
Variance inflation factor
Linear regression
Mean squared error
Mean absolute percentage error
Environmental science
Feature selection
Metric (unit)
Machine learning
Mathematics
Computer science
Statistics
Engineering
Operations management
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-1938493/v1
- https://www.researchsquare.com/article/rs-1938493/latest.pdf
- OA Status
- green
- Cited By
- 2
- References
- 70
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4293825551
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4293825551Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-1938493/v1Digital Object Identifier
- Title
-
Oil Palm Yield Prediction Across Blocks Using Multi-Source Data and Machine LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-08-18Full publication date if available
- Authors
-
Yuhao Ang, Helmi Zulhaidi Mohd Shafri, Yang Ping Lee, Shahrul Azman Bakar, Haryati Abidin, Mohd Umar Ubaydah Mohd Junaidi, Shaiful Jahari Hashim, Nik Norasma Che’Ya, Mohd Roshdi Hassan, H. S. Lim, Rosni Abdullah, Yusri Yusup, Syahidah Akmal Muhammad, Sin Yin Teh, Mohd Na’aim SamadList of authors in order
- Landing page
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https://doi.org/10.21203/rs.3.rs-1938493/v1Publisher landing page
- PDF URL
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https://www.researchsquare.com/article/rs-1938493/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-1938493/latest.pdfDirect OA link when available
- Concepts
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Support vector machine, Random forest, Multicollinearity, Artificial neural network, Variance inflation factor, Linear regression, Mean squared error, Mean absolute percentage error, Environmental science, Feature selection, Metric (unit), Machine learning, Mathematics, Computer science, Statistics, Engineering, Operations managementTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2024: 1, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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70Number of works referenced by this work
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-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.forest) | 94 |
| abstract_inverted_index.handled | 360 |
| abstract_inverted_index.indices | 61 |
| abstract_inverted_index.machine | 86 |
| abstract_inverted_index.metrics | 163 |
| abstract_inverted_index.network | 152 |
| abstract_inverted_index.predict | 104 |
| abstract_inverted_index.process | 374 |
| abstract_inverted_index.spatial | 168 |
| abstract_inverted_index.station | 75 |
| abstract_inverted_index.support | 89, 142 |
| abstract_inverted_index.surface | 68 |
| abstract_inverted_index.various | 44 |
| abstract_inverted_index.weather | 74 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.XGBoost, | 141 |
| abstract_inverted_index.accurate | 11, 306, 336 |
| abstract_inverted_index.achieved | 177 |
| abstract_inverted_index.advanced | 227 |
| abstract_inverted_index.affected | 42 |
| abstract_inverted_index.backward | 120, 258 |
| abstract_inverted_index.climatic | 35, 64 |
| abstract_inverted_index.compared | 160 |
| abstract_inverted_index.ensemble | 228 |
| abstract_inverted_index.ensuring | 20 |
| abstract_inverted_index.estimate | 365 |
| abstract_inverted_index.identify | 261 |
| abstract_inverted_index.improved | 272 |
| abstract_inverted_index.increase | 311 |
| abstract_inverted_index.learning | 98 |
| abstract_inverted_index.metrics, | 308 |
| abstract_inverted_index.multiple | 81, 354 |
| abstract_inverted_index.network) | 101 |
| abstract_inverted_index.rainfall | 72 |
| abstract_inverted_index.recorded | 252 |
| abstract_inverted_index.selected | 184 |
| abstract_inverted_index.sources. | 355 |
| abstract_inverted_index.utilised | 236 |
| abstract_inverted_index.weather, | 47 |
| abstract_inverted_index.(XGBoost, | 88 |
| abstract_inverted_index.Moreover, | 115 |
| abstract_inverted_index.addition, | 226 |
| abstract_inverted_index.decreased | 283 |
| abstract_inverted_index.developed | 136, 332 |
| abstract_inverted_index.effective | 334 |
| abstract_inverted_index.essential | 14 |
| abstract_inverted_index.estimates | 40 |
| abstract_inverted_index.generated | 165 |
| abstract_inverted_index.generates | 303 |
| abstract_inverted_index.including | 46 |
| abstract_inverted_index.learnings | 87 |
| abstract_inverted_index.nutrients | 48 |
| abstract_inverted_index.security, | 22 |
| abstract_inverted_index.selection | 123 |
| abstract_inverted_index.variables | 65 |
| abstract_inverted_index.Predicting | 1 |
| abstract_inverted_index.efficient, | 333 |
| abstract_inverted_index.escalation | 32 |
| abstract_inverted_index.evaluation | 162 |
| abstract_inverted_index.management | 50, 363 |
| abstract_inverted_index.plantation | 362 |
| abstract_inverted_index.practices. | 51 |
| abstract_inverted_index.predicting | 342 |
| abstract_inverted_index.prediction | 133, 169, 244, 338 |
| abstract_inverted_index.predictors | 203 |
| abstract_inverted_index.preventing | 16 |
| abstract_inverted_index.regression | 83, 144 |
| abstract_inverted_index.techniques | 124, 230 |
| abstract_inverted_index.vegetation | 60 |
| abstract_inverted_index.algorithms. | 154 |
| abstract_inverted_index.block-level | 109 |
| abstract_inverted_index.elimination | 121, 259 |
| abstract_inverted_index.implemented | 126 |
| abstract_inverted_index.integrating | 55, 351 |
| abstract_inverted_index.performance | 267 |
| abstract_inverted_index.plantation. | 114 |
| abstract_inverted_index.potentially | 359 |
| abstract_inverted_index.predictors, | 265 |
| abstract_inverted_index.production. | 377 |
| abstract_inverted_index.regression, | 91 |
| abstract_inverted_index.significant | 264 |
| abstract_inverted_index.statistical | 307 |
| abstract_inverted_index.sustainable | 376 |
| abstract_inverted_index.temperature | 69 |
| abstract_inverted_index.time-series | 116 |
| abstract_inverted_index.Furthermore, | 37 |
| abstract_inverted_index.multi-source | 56 |
| abstract_inverted_index.particularly | 23 |
| abstract_inverted_index.performance. | 175, 255 |
| abstract_inverted_index.performances | 157, 181 |
| abstract_inverted_index.successfully | 331 |
| abstract_inverted_index.respectively. | 301 |
| abstract_inverted_index.supplementary | 239 |
| abstract_inverted_index.pre-processing | 129 |
| abstract_inverted_index.precipitation, | 73 |
| abstract_inverted_index.decision-making | 373 |
| abstract_inverted_index.field-surveys), | 77 |
| abstract_inverted_index.satellite-derived | 59, 63 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 15 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.5 |
| sustainable_development_goals[0].display_name | Climate action |
| sustainable_development_goals[1].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[1].score | 0.4099999964237213 |
| sustainable_development_goals[1].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.64314548 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |