コメント: 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

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

The conventional method to quantify leaf biochemical properties (nutrients and chlorophylls) is tedious, labour-intensive, and impractical for vast oil palm plantation areas. Spectral analysis retrieved from a spectroradiometer and an unmanned aerial vehicle (UAV) and imbalanced approaches such as t...

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主要な著者:	Amirruddin, Amiratul Diyana, Muharam, Farrah Melissa, Ismail, Mohd Hasmadi, Tan, Ngai Paing, Ismail, Mohd Firdaus
フォーマット:	論文
出版事項:	Elsevier 2022
オンライン･アクセス:	http://psasir.upm.edu.my/id/eprint/103414/ https://www.sciencedirect.com/science/article/pii/S0168169921006633
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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

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