Comparison Between Machine Learning Models for Yield Forecast in Cocoa Crops in Santander, Colombia
The identification of influencing factors in crop yield (kg·ha-1) provides essential information for decision-making processes related to the prediction and improvement of productivity, which gives farmers the opportunity to increase their income. The current study investigates the application of mu...
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| Format: | Article |
| Language: | English |
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Universidad Pedagógica y Tecnológica de Colombia
2020-05-01
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| Series: | Revista Facultad de Ingeniería |
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| Online Access: | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/10853 |
| _version_ | 1819086596673110016 |
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| author | Henry Lamos-Díaz, Ph. D. David Esteban Puentes-Garzón, M.Sc. Diego Alejandro Zarate-Caicedo, Ph. D. |
| author_facet | Henry Lamos-Díaz, Ph. D. David Esteban Puentes-Garzón, M.Sc. Diego Alejandro Zarate-Caicedo, Ph. D. |
| author_sort | Henry Lamos-Díaz, Ph. D. |
| collection | DOAJ |
| description | The identification of influencing factors in crop yield (kg·ha-1) provides essential information for decision-making processes related to the prediction and improvement of productivity, which gives farmers the opportunity to increase their income. The current study investigates the application of multiple machine learning algorithms for cocoa yield prediction and influencing factors identification. The Support Vector Machines (SVM) and Ensemble Learning Models (Random Forests, Gradient Boosting) are compared with Least Absolute Shrinkage and Selection Operator (LASSO) regression models. The considered predictors were climate conditions, cocoa variety, fertilization level and sun exposition in an experimental crop located in Rionegro, Santander. Results showed that Gradient Boosting is the best prediction alternative with Coefficient of determination (R2) = 68%, Mean Absolute Error (MAE) = 13.32, and Root Mean Square Error (RMSE) = 20.41. The crop yield variability is explained mainly by the radiation one month before harvest, the accumulated rainfall on the harvest month, and the temperature one month before harvest. Likewise, the crop yields are evaluated based on the kind of sun exposure, and it was found that radiation one month before harvest is the most influential factor in shade-grown plants. On the other hand, rainfall and soil moisture are determining variables in sun-grown plants, which is associated with the water requirements. These results suggest a differentiated management for crops depending on the kind of sun exposure to avoid compromising productivity, since there is no significant difference in the yield of both agricultural managements. |
| first_indexed | 2024-12-21T21:22:46Z |
| format | Article |
| id | doaj.art-af4a7884f3454464a738d2e260e6f4fa |
| institution | Directory Open Access Journal |
| issn | 0121-1129 2357-5328 |
| language | English |
| last_indexed | 2024-12-21T21:22:46Z |
| publishDate | 2020-05-01 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | Article |
| series | Revista Facultad de Ingeniería |
| spelling | doaj.art-af4a7884f3454464a738d2e260e6f4fa2022-12-21T18:49:50ZengUniversidad Pedagógica y Tecnológica de ColombiaRevista Facultad de Ingeniería0121-11292357-53282020-05-012954e10853e1085310.19053/01211129.v29.n54.2020.1085310853Comparison Between Machine Learning Models for Yield Forecast in Cocoa Crops in Santander, ColombiaHenry Lamos-Díaz, Ph. D.0David Esteban Puentes-Garzón, M.Sc.1Diego Alejandro Zarate-Caicedo, Ph. D.2Universidad Industrial de SantanderUniversidad Industrial de SantanderCorporación Colombiana de Investigación Agropecuaria-AGROSAVIAThe identification of influencing factors in crop yield (kg·ha-1) provides essential information for decision-making processes related to the prediction and improvement of productivity, which gives farmers the opportunity to increase their income. The current study investigates the application of multiple machine learning algorithms for cocoa yield prediction and influencing factors identification. The Support Vector Machines (SVM) and Ensemble Learning Models (Random Forests, Gradient Boosting) are compared with Least Absolute Shrinkage and Selection Operator (LASSO) regression models. The considered predictors were climate conditions, cocoa variety, fertilization level and sun exposition in an experimental crop located in Rionegro, Santander. Results showed that Gradient Boosting is the best prediction alternative with Coefficient of determination (R2) = 68%, Mean Absolute Error (MAE) = 13.32, and Root Mean Square Error (RMSE) = 20.41. The crop yield variability is explained mainly by the radiation one month before harvest, the accumulated rainfall on the harvest month, and the temperature one month before harvest. Likewise, the crop yields are evaluated based on the kind of sun exposure, and it was found that radiation one month before harvest is the most influential factor in shade-grown plants. On the other hand, rainfall and soil moisture are determining variables in sun-grown plants, which is associated with the water requirements. These results suggest a differentiated management for crops depending on the kind of sun exposure to avoid compromising productivity, since there is no significant difference in the yield of both agricultural managements.https://revistas.uptc.edu.co/index.php/ingenieria/article/view/10853agricultural-yieldagroforestry-systemcocoamachine-learningpredictionproductivity |
| spellingShingle | Henry Lamos-Díaz, Ph. D. David Esteban Puentes-Garzón, M.Sc. Diego Alejandro Zarate-Caicedo, Ph. D. Comparison Between Machine Learning Models for Yield Forecast in Cocoa Crops in Santander, Colombia Revista Facultad de Ingeniería agricultural-yield agroforestry-system cocoa machine-learning prediction productivity |
| title | Comparison Between Machine Learning Models for Yield Forecast in Cocoa Crops in Santander, Colombia |
| title_full | Comparison Between Machine Learning Models for Yield Forecast in Cocoa Crops in Santander, Colombia |
| title_fullStr | Comparison Between Machine Learning Models for Yield Forecast in Cocoa Crops in Santander, Colombia |
| title_full_unstemmed | Comparison Between Machine Learning Models for Yield Forecast in Cocoa Crops in Santander, Colombia |
| title_short | Comparison Between Machine Learning Models for Yield Forecast in Cocoa Crops in Santander, Colombia |
| title_sort | comparison between machine learning models for yield forecast in cocoa crops in santander colombia |
| topic | agricultural-yield agroforestry-system cocoa machine-learning prediction productivity |
| url | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/10853 |
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