A state-of-the-art novel approach to predict potato crop coefficient (Kc) by integrating advanced machine learning tools
The crop coefficient (Kc) is one of the important elements of the actual evapotranspiration estimation. The current study aims to develop a machine learning approach to estimate the crop coefficient of potatoes (Russet Burbank variety) in Prince Edward Island province, one of Canada's most impo...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525001297 |
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| author | Saad Javed Cheema Masoud Karbasi Gurjit S. Randhawa Suqi Liu Travis J. Esau Kuljeet Singh Grewal Farhat Abbas Qamar Uz Zaman Aitazaz A. Farooque |
| author_facet | Saad Javed Cheema Masoud Karbasi Gurjit S. Randhawa Suqi Liu Travis J. Esau Kuljeet Singh Grewal Farhat Abbas Qamar Uz Zaman Aitazaz A. Farooque |
| author_sort | Saad Javed Cheema |
| collection | DOAJ |
| description | The crop coefficient (Kc) is one of the important elements of the actual evapotranspiration estimation. The current study aims to develop a machine learning approach to estimate the crop coefficient of potatoes (Russet Burbank variety) in Prince Edward Island province, one of Canada's most important producers. The study was conducted at drainage-type lysimeters placed in the potato field with three types of soils (sandy loam, loamy sand, and loam). A machine learning approach using XGBoost, optimized with the Chaos Game algorithm (CGO-XGBoost), was employed to predict Kc. Three input scenarios (meteorological + soil data, soil-only, meteorological-only) were tested. Three other machine learning techniques, K-nearest neighbor (KNN), Adaptive Boosting (AdaBoost), and Multilayer Perceptron Neural Network (MLP), were used to compare with the newly developed model. Different performance metrics such as correlation coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used to compare different model's performance. Results showed that the CGO-XGBoost model outperformed conventional machine learning models. A comparison of different input scenarios revealed that combination 2 (Soil data only) gave the best results. Combination 3 (only meteorological data) performs weakest among input scenarios. The best model (Combination2 + CGO-XGBoost) achieved the highest accuracy (R = 0.9961, RMSE = 0.0185, MAPE = 2.20%), outperforming traditional methods. SHapley Additive exPlanations (SHAP) interpretability analysis indicates that soil moisture exerts the greatest impact on potato Kc. Field Capacity (FC) and Minimum temperature rank as the second and third most significant factors. The integration of SHAP values in the proposed solution improves the interpretability of the model, offering valuable insights into the environmental and soil factors affecting Kc predictions. The results showed that the proposed model can accurately predict Kc, demonstrating its potential to enhance water-use efficiency and support precision irrigation strategies. |
| format | Article |
| id | doaj-art-fa3358df389d4ed59d3a700d15a2cd30 |
| institution | OA Journals |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-fa3358df389d4ed59d3a700d15a2cd302025-08-20T02:10:03ZengElsevierSmart Agricultural Technology2772-37552025-08-011110089610.1016/j.atech.2025.100896A state-of-the-art novel approach to predict potato crop coefficient (Kc) by integrating advanced machine learning toolsSaad Javed Cheema0Masoud Karbasi1Gurjit S. Randhawa2Suqi Liu3Travis J. Esau4Kuljeet Singh Grewal5Farhat Abbas6Qamar Uz Zaman7Aitazaz A. Farooque8Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island, St Peters Bay, PE, CanadaCanadian Centre for Climate Change and Adaptation, University of Prince Edward Island, St Peters Bay, PE, CanadaSchool of Computer Science, University of Guelph, Guelph, ON, Canada; Corresponding author.Department of Agriculture, Prince Edward Island, Charlottetown, PE, CanadaDepartment of Engineering, Faculty of Agriculture, Dalhousie University, Truro, NS, CanadaFaculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE, CanadaCollege of Engineering and Technology, University of Doha for Science and Technology, Doha, P. O. Box 24449, QatarDepartment of Engineering, Faculty of Agriculture, Dalhousie University, Truro, NS, CanadaCanadian Centre for Climate Change and Adaptation, University of Prince Edward Island, St Peters Bay, PE, Canada; Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE, CanadaThe crop coefficient (Kc) is one of the important elements of the actual evapotranspiration estimation. The current study aims to develop a machine learning approach to estimate the crop coefficient of potatoes (Russet Burbank variety) in Prince Edward Island province, one of Canada's most important producers. The study was conducted at drainage-type lysimeters placed in the potato field with three types of soils (sandy loam, loamy sand, and loam). A machine learning approach using XGBoost, optimized with the Chaos Game algorithm (CGO-XGBoost), was employed to predict Kc. Three input scenarios (meteorological + soil data, soil-only, meteorological-only) were tested. Three other machine learning techniques, K-nearest neighbor (KNN), Adaptive Boosting (AdaBoost), and Multilayer Perceptron Neural Network (MLP), were used to compare with the newly developed model. Different performance metrics such as correlation coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used to compare different model's performance. Results showed that the CGO-XGBoost model outperformed conventional machine learning models. A comparison of different input scenarios revealed that combination 2 (Soil data only) gave the best results. Combination 3 (only meteorological data) performs weakest among input scenarios. The best model (Combination2 + CGO-XGBoost) achieved the highest accuracy (R = 0.9961, RMSE = 0.0185, MAPE = 2.20%), outperforming traditional methods. SHapley Additive exPlanations (SHAP) interpretability analysis indicates that soil moisture exerts the greatest impact on potato Kc. Field Capacity (FC) and Minimum temperature rank as the second and third most significant factors. The integration of SHAP values in the proposed solution improves the interpretability of the model, offering valuable insights into the environmental and soil factors affecting Kc predictions. The results showed that the proposed model can accurately predict Kc, demonstrating its potential to enhance water-use efficiency and support precision irrigation strategies.http://www.sciencedirect.com/science/article/pii/S2772375525001297Potato Crop CoefficientXGBoostChaos Game algorithmSHAPIrrigation ManagementSustainable Agriculture |
| spellingShingle | Saad Javed Cheema Masoud Karbasi Gurjit S. Randhawa Suqi Liu Travis J. Esau Kuljeet Singh Grewal Farhat Abbas Qamar Uz Zaman Aitazaz A. Farooque A state-of-the-art novel approach to predict potato crop coefficient (Kc) by integrating advanced machine learning tools Smart Agricultural Technology Potato Crop Coefficient XGBoost Chaos Game algorithm SHAP Irrigation Management Sustainable Agriculture |
| title | A state-of-the-art novel approach to predict potato crop coefficient (Kc) by integrating advanced machine learning tools |
| title_full | A state-of-the-art novel approach to predict potato crop coefficient (Kc) by integrating advanced machine learning tools |
| title_fullStr | A state-of-the-art novel approach to predict potato crop coefficient (Kc) by integrating advanced machine learning tools |
| title_full_unstemmed | A state-of-the-art novel approach to predict potato crop coefficient (Kc) by integrating advanced machine learning tools |
| title_short | A state-of-the-art novel approach to predict potato crop coefficient (Kc) by integrating advanced machine learning tools |
| title_sort | state of the art novel approach to predict potato crop coefficient kc by integrating advanced machine learning tools |
| topic | Potato Crop Coefficient XGBoost Chaos Game algorithm SHAP Irrigation Management Sustainable Agriculture |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525001297 |
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