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Synthetic Minority Over-sampling TEchnique (SMOTE) and Logistic Model Tree (LMT)-Adaptive Boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll sufficiency levels of oil palm (Elaeis guineensis) using spectroradiometers and unmanned aerial vehicles

Research article (Computers and Electronics in Agriculture, 2022) · cited 64× · AI/ML
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Synthetic Minority Over-sampling TEchnique (SMOTE) and Logistic Model Tree (LMT)-Adaptive Boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll sufficiency levels of oil palm (Elaeis guineensis) using spectroradiometers and unmanned aerial vehicles

Summary

Synthetic Minority Over-sampling TEchnique (SMOTE) and Logistic Model Tree (LMT)-Adaptive Boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll sufficiency levels of oil palm (Elaeis guineensis) using spectroradiometers and unmanned aerial vehicles is a scholarly article[1].

Key Facts

  • Synthetic Minority Over-sampling TEchnique (SMOTE) and Logistic Model Tree (LMT)-Adaptive Boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll sufficiency levels of oil palm (Elaeis guineensis) using spectroradiometers and unmanned aerial vehicles's instance of is recorded as scholarly article[2].

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Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

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  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). Synthetic Minority Over-sampling TEchnique (SMOTE) and Logistic Model Tree (LMT)-Adaptive Boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll sufficiency levels of oil palm (Elaeis guineensis) using spectroradiometers and unmanned aerial vehicles. Retrieved May 24, 2026, from https://4ort.xyz/entity/synthetic-minority-over-sampling-technique-smote-and-logistic-model-tree-lmt-adaptive-boosting-algorithms-for-classifyin
MLA “Synthetic Minority Over-sampling TEchnique (SMOTE) and Logistic Model Tree (LMT)-Adaptive Boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll sufficiency levels of oil palm (Elaeis guineensis) using spectroradiometers and unmanned aerial vehicles.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/synthetic-minority-over-sampling-technique-smote-and-logistic-model-tree-lmt-adaptive-boosting-algorithms-for-classifyin.
BibTeX @misc{4ortxyz_synthetic-minority-over-sampling-technique-smote-and-logistic-model-tree-lmt-adaptive-boosting-algorithms-for-classifyin_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Synthetic Minority Over-sampling TEchnique (SMOTE) and Logistic Model Tree (LMT)-Adaptive Boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll sufficiency levels of oil palm (Elaeis guineensis) using spectroradiometers and unmanned aerial vehicles}}, year = {2026}, url = {https://4ort.xyz/entity/synthetic-minority-over-sampling-technique-smote-and-logistic-model-tree-lmt-adaptive-boosting-algorithms-for-classifyin}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Synthetic Minority Over-sampling TEchnique (SMOTE) and Logistic Model Tree (LMT)-Adaptive Boosting algorithms for classifying imbalanced datasets of nutrient and chlorophyll sufficiency levels of oil palm (Elaeis guineensis) using spectroradiometers and unmanned aerial vehicles — https://4ort.xyz/entity/synthetic-minority-over-sampling-technique-smote-and-logistic-model-tree-lmt-adaptive-boosting-algorithms-for-classifyin (retrieved 2026-05-24)

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