Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices
Accurate crop yield forecasting is essential in the food industry’s decision-making process, where vegetation condition index (VCI) and thermal condition index (TCI) coupled with machine learning (ML) algorithms play crucial roles. The drawback, however, is that a one-fits-all prediction model is of...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
MDPI
2022
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/88918 |
| _version_ | 1848765113242222592 |
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| author | Pham, Hoa Thi Awange, Joseph Kuhn, Michael Van Nguyen, B. Bui, L.K. |
| author_facet | Pham, Hoa Thi Awange, Joseph Kuhn, Michael Van Nguyen, B. Bui, L.K. |
| author_sort | Pham, Hoa Thi |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Accurate crop yield forecasting is essential in the food industry’s decision-making process, where vegetation condition index (VCI) and thermal condition index (TCI) coupled with machine learning (ML) algorithms play crucial roles. The drawback, however, is that a one-fits-all prediction model is often employed over an entire region without considering subregional VCI and TCI’s spatial variability resulting from environmental and climatic factors. Furthermore, when using nonlinear ML, redundant VCI/TCI data present additional challenges that adversely affect the models’ output. This study proposes a framework that (i) employs higher-order spatial independent component analysis (sICA), and (ii), exploits a combination of the principal component analysis (PCA) and ML (i.e., PCA-ML combination) to deal with the two challenges in order to enhance crop yield prediction accuracy. The proposed framework consolidates common VCI/TCI spatial variability into their respective subregions, using Vietnam as an example. Compared to the one-fits-all approach, subregional rice yield forecasting models over Vietnam improved by an average level of 20% up to 60%. PCA-ML combination outper-formed ML-only by an average of 18.5% up to 45%. The framework generates rice yield predictions 1 to 2 months ahead of the harvest with an average of 5% error, displaying its reliability. |
| first_indexed | 2025-11-14T11:30:05Z |
| format | Journal Article |
| id | curtin-20.500.11937-88918 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:30:05Z |
| publishDate | 2022 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-889182022-07-27T07:33:13Z Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices Pham, Hoa Thi Awange, Joseph Kuhn, Michael Van Nguyen, B. Bui, L.K. Science & Technology Physical Sciences Technology Chemistry, Analytical Engineering, Electrical & Electronic Instruments & Instrumentation Chemistry Engineering crop yield prediction vegetation condition index (VCI) thermal condition index (TCI) independent component analysis (ICA) principle component analysis (PCA) machine learning WINTER-WHEAT YIELD RICE YIELD MODEL DROUGHT CORN Accurate crop yield forecasting is essential in the food industry’s decision-making process, where vegetation condition index (VCI) and thermal condition index (TCI) coupled with machine learning (ML) algorithms play crucial roles. The drawback, however, is that a one-fits-all prediction model is often employed over an entire region without considering subregional VCI and TCI’s spatial variability resulting from environmental and climatic factors. Furthermore, when using nonlinear ML, redundant VCI/TCI data present additional challenges that adversely affect the models’ output. This study proposes a framework that (i) employs higher-order spatial independent component analysis (sICA), and (ii), exploits a combination of the principal component analysis (PCA) and ML (i.e., PCA-ML combination) to deal with the two challenges in order to enhance crop yield prediction accuracy. The proposed framework consolidates common VCI/TCI spatial variability into their respective subregions, using Vietnam as an example. Compared to the one-fits-all approach, subregional rice yield forecasting models over Vietnam improved by an average level of 20% up to 60%. PCA-ML combination outper-formed ML-only by an average of 18.5% up to 45%. The framework generates rice yield predictions 1 to 2 months ahead of the harvest with an average of 5% error, displaying its reliability. 2022 Journal Article http://hdl.handle.net/20.500.11937/88918 10.3390/s22030719 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 crop yield prediction vegetation condition index (VCI) thermal condition index (TCI) independent component analysis (ICA) principle component analysis (PCA) machine learning WINTER-WHEAT YIELD RICE YIELD MODEL DROUGHT CORN Pham, Hoa Thi Awange, Joseph Kuhn, Michael Van Nguyen, B. Bui, L.K. Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices |
| title | Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices |
| title_full | Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices |
| title_fullStr | Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices |
| title_full_unstemmed | Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices |
| title_short | Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices |
| title_sort | enhancing crop yield prediction utilizing machine learning on satellite-based vegetation health indices |
| topic | Science & Technology Physical Sciences Technology Chemistry, Analytical Engineering, Electrical & Electronic Instruments & Instrumentation Chemistry Engineering crop yield prediction vegetation condition index (VCI) thermal condition index (TCI) independent component analysis (ICA) principle component analysis (PCA) machine learning WINTER-WHEAT YIELD RICE YIELD MODEL DROUGHT CORN |
| url | http://hdl.handle.net/20.500.11937/88918 |