Extreme gradient boosting (XGBoost) regressor and shapley additive explanation for crop yield prediction in agriculture
The primary purpose of precision agriculture is to maximize crop yields while utilizing a limited amount of land resources. Apart from industrialization, which fuelled Malaysia's significant economy and development, the country's agriculture industry performs a major role in guaranteeing f...
| Main Authors: | , , , |
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| Format: | Proceedings |
| Language: | English English |
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Institute of Electrical and Electronics Engineers Inc.
2022
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| Subjects: | |
| Online Access: | https://eprints.ums.edu.my/id/eprint/37720/1/ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/37720/2/FULLTEXT.pdf |
| _version_ | 1796911950186151936 |
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| author | Dennis A/L Mariadass Ervin Gubin Moung Maisarah Mohd Sufian Ali Farzamnia |
| author_facet | Dennis A/L Mariadass Ervin Gubin Moung Maisarah Mohd Sufian Ali Farzamnia |
| author_sort | Dennis A/L Mariadass |
| collection | UMS |
| description | The primary purpose of precision agriculture is to maximize crop yields while utilizing a limited amount of land resources. Apart from industrialization, which fuelled Malaysia's significant economy and development, the country's agriculture industry performs a major role in guaranteeing food security and safety, as well as long-term development and wealth creation. To increase the nation's food security, policymakers must rely on accurate crop yield predictions in order to easily obtain trade - related evaluations. Machine Learning can help anticipate yields more accurately. This paper proposes to use the XGBoost model for annual crop yield prediction in Malaysia. Experiments on the generated yield dataset show promising results with 0.98 R-Squared value and outperformed the current models. The implementation of the suggested model is extensively evaluated using the Shapley Additive Explanation (SHAP) to discover the essential features such as average temperature, average rainfall, and pesticide in the crop yield prediction. The estimates provided by machine learning algorithms will aid farmers in deciding what to grow because of this research. |
| first_indexed | 2024-03-06T03:26:22Z |
| format | Proceedings |
| id | ums.eprints-37720 |
| institution | Universiti Malaysia Sabah |
| language | English English |
| last_indexed | 2024-03-06T03:26:22Z |
| publishDate | 2022 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | ums.eprints-377202023-11-29T02:31:09Z https://eprints.ums.edu.my/id/eprint/37720/ Extreme gradient boosting (XGBoost) regressor and shapley additive explanation for crop yield prediction in agriculture Dennis A/L Mariadass Ervin Gubin Moung Maisarah Mohd Sufian Ali Farzamnia S1-(972) Agriculture (General) The primary purpose of precision agriculture is to maximize crop yields while utilizing a limited amount of land resources. Apart from industrialization, which fuelled Malaysia's significant economy and development, the country's agriculture industry performs a major role in guaranteeing food security and safety, as well as long-term development and wealth creation. To increase the nation's food security, policymakers must rely on accurate crop yield predictions in order to easily obtain trade - related evaluations. Machine Learning can help anticipate yields more accurately. This paper proposes to use the XGBoost model for annual crop yield prediction in Malaysia. Experiments on the generated yield dataset show promising results with 0.98 R-Squared value and outperformed the current models. The implementation of the suggested model is extensively evaluated using the Shapley Additive Explanation (SHAP) to discover the essential features such as average temperature, average rainfall, and pesticide in the crop yield prediction. The estimates provided by machine learning algorithms will aid farmers in deciding what to grow because of this research. Institute of Electrical and Electronics Engineers Inc. 2022 Proceedings PeerReviewed text en https://eprints.ums.edu.my/id/eprint/37720/1/ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/37720/2/FULLTEXT.pdf Dennis A/L Mariadass and Ervin Gubin Moung and Maisarah Mohd Sufian and Ali Farzamnia (2022) Extreme gradient boosting (XGBoost) regressor and shapley additive explanation for crop yield prediction in agriculture. https://ieeexplore.ieee.org/document/9960069 |
| spellingShingle | S1-(972) Agriculture (General) Dennis A/L Mariadass Ervin Gubin Moung Maisarah Mohd Sufian Ali Farzamnia Extreme gradient boosting (XGBoost) regressor and shapley additive explanation for crop yield prediction in agriculture |
| title | Extreme gradient boosting (XGBoost) regressor and shapley additive explanation for crop yield prediction in agriculture |
| title_full | Extreme gradient boosting (XGBoost) regressor and shapley additive explanation for crop yield prediction in agriculture |
| title_fullStr | Extreme gradient boosting (XGBoost) regressor and shapley additive explanation for crop yield prediction in agriculture |
| title_full_unstemmed | Extreme gradient boosting (XGBoost) regressor and shapley additive explanation for crop yield prediction in agriculture |
| title_short | Extreme gradient boosting (XGBoost) regressor and shapley additive explanation for crop yield prediction in agriculture |
| title_sort | extreme gradient boosting xgboost regressor and shapley additive explanation for crop yield prediction in agriculture |
| topic | S1-(972) Agriculture (General) |
| url | https://eprints.ums.edu.my/id/eprint/37720/1/ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/37720/2/FULLTEXT.pdf |
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