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...
| Main Authors: | , , |
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| Format: | Journal Article |
| Language: | English |
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MDPI
2022
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/91904 |
| _version_ | 1848765600205111296 |
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| author | Pham, Hoa Thi Awange, Joseph Kuhn, Michael |
| author_facet | Pham, Hoa Thi Awange, Joseph Kuhn, Michael |
| author_sort | Pham, Hoa Thi |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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 their performances and, more importantly, the benefits of combining both methods. Therefore, this paper proposes a framework that uses non-feature reduction (All-F) as a baseline to investigate the performance of FS, FX, and a combination of both (FSX). The case study employs the vegetation condition index (VCI)/temperature condition index (TCI) to develop 21 rice yield forecasting models for eight sub-regions in Vietnam based on ML methods, namely linear, support vector machine (SVM), decision tree (Tree), artificial neural network (ANN), and Ensemble. The results reveal that FSX takes full advantage of the FS and FX, leading FSX-based models to perform the best in 18 out of 21 models, while 2 (1) for FS-based (FX-based) models. These FXS-, FS-, and FX-based models improve All-F-based models at an average level of 21% and up to 60% in terms of RMSE. Furthermore, 21 of the best models are developed based on Ensemble (13 models), Tree (6 models), linear (1 model), and ANN (1 model). These findings highlight the significant role of FS, FX, and specially FSX coupled with a wide range of ML algorithms (especially Ensemble) for enhancing the accuracy of predicting crop yield. |
| first_indexed | 2025-11-14T11:37:49Z |
| format | Journal Article |
| id | curtin-20.500.11937-91904 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:37:49Z |
| publishDate | 2022 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-919042023-06-07T03:56:03Z Evaluation of Three Feature Dimension Reduction Techniques for Machine Learning-Based Crop Yield Prediction Models Pham, Hoa Thi Awange, Joseph Kuhn, Michael 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 TCI VCI crop yield feature extraction feature selection machine learning Algorithms Forecasting Machine Learning Neural Networks, Computer Support Vector Machine Algorithms Forecasting Machine Learning Support Vector Machine Neural Networks, Computer 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 their performances and, more importantly, the benefits of combining both methods. Therefore, this paper proposes a framework that uses non-feature reduction (All-F) as a baseline to investigate the performance of FS, FX, and a combination of both (FSX). The case study employs the vegetation condition index (VCI)/temperature condition index (TCI) to develop 21 rice yield forecasting models for eight sub-regions in Vietnam based on ML methods, namely linear, support vector machine (SVM), decision tree (Tree), artificial neural network (ANN), and Ensemble. The results reveal that FSX takes full advantage of the FS and FX, leading FSX-based models to perform the best in 18 out of 21 models, while 2 (1) for FS-based (FX-based) models. These FXS-, FS-, and FX-based models improve All-F-based models at an average level of 21% and up to 60% in terms of RMSE. Furthermore, 21 of the best models are developed based on Ensemble (13 models), Tree (6 models), linear (1 model), and ANN (1 model). These findings highlight the significant role of FS, FX, and specially FSX coupled with a wide range of ML algorithms (especially Ensemble) for enhancing the accuracy of predicting crop yield. 2022 Journal Article http://hdl.handle.net/20.500.11937/91904 10.3390/s22176609 English http://creativecommons.org/licenses/by/4.0/ MDPI fulltext |
| spellingShingle | 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 TCI VCI crop yield feature extraction feature selection machine learning Algorithms Forecasting Machine Learning Neural Networks, Computer Support Vector Machine Algorithms Forecasting Machine Learning Support Vector Machine Neural Networks, Computer Pham, Hoa Thi Awange, Joseph Kuhn, Michael Evaluation of Three Feature Dimension Reduction Techniques for Machine Learning-Based Crop Yield Prediction Models |
| title | Evaluation of Three Feature Dimension Reduction Techniques for Machine Learning-Based Crop Yield Prediction Models |
| title_full | Evaluation of Three Feature Dimension Reduction Techniques for Machine Learning-Based Crop Yield Prediction Models |
| title_fullStr | Evaluation of Three Feature Dimension Reduction Techniques for Machine Learning-Based Crop Yield Prediction Models |
| title_full_unstemmed | Evaluation of Three Feature Dimension Reduction Techniques for Machine Learning-Based Crop Yield Prediction Models |
| title_short | Evaluation of Three Feature Dimension Reduction Techniques for Machine Learning-Based Crop Yield Prediction Models |
| title_sort | evaluation of three feature dimension reduction techniques for machine learning-based crop yield prediction models |
| topic | 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 TCI VCI crop yield feature extraction feature selection machine learning Algorithms Forecasting Machine Learning Neural Networks, Computer Support Vector Machine Algorithms Forecasting Machine Learning Support Vector Machine Neural Networks, Computer |
| url | http://hdl.handle.net/20.500.11937/91904 |