Evaluation of Three Feature Dimension Reduction Techniques for Machine Learning-Based Crop Yield Prediction Models

Evaluation of Three Feature Dimension Reduction Techniques for Machine Learning-Based Crop Yield Prediction Models

Machine learning (ML) has been widely used worldwide to develop crop yield forecasting models. However, it is still challenging to identify the most critical features from a dataset. Although either feature selection (FS) or feature extraction (FX) techniques have been employed, no research compares...

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Bibliographic Details
Main Authors: Pham, Hoa Thi, Awange, Joseph, Kuhn, Michael
Format: Journal Article
Language:English
Published: MDPI 2022
Subjects:
Science & Technology
Physical Sciences
Technology
Chemistry, Analytical
Engineering, Electrical & Electronic
Instruments & Instrumentation
Chemistry
Engineering
feature selection
feature extraction
machine learning
crop yield
VCI
TCI
VEGETATION HEALTH INDEXES
FEATURE-SELECTION
NEURAL-NETWORKS
DROUGHT
Algorithms
Forecasting
Machine Learning
Neural Networks, Computer
Support Vector Machine
Online Access:http://hdl.handle.net/20.500.11937/91904

Internet

http://hdl.handle.net/20.500.11937/91904

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