您的評論: 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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Main Authors:	Amirruddin, Amiratul Diyana, Muharam, Farrah Melissa, Ismail, Mohd Hasmadi, Tan, Ngai Paing, Ismail, Mohd Firdaus
格式:	Article
出版:	Elsevier 2022
在線閱讀:	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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