Attribute Selection Impact on Linear and Nonlinear Regression Models for Crop Yield Prediction
Efficient cropping requires yield estimation for each involved crop, where data-driven models are commonly applied. In recent years, some data-driven modeling technique comparisons have been made, looking for the best model to yield prediction. However, attributes are usually selected based on exper...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/509429 |
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| author | Alberto Gonzalez-Sanchez Juan Frausto-Solis Waldo Ojeda-Bustamante |
| author_facet | Alberto Gonzalez-Sanchez Juan Frausto-Solis Waldo Ojeda-Bustamante |
| author_sort | Alberto Gonzalez-Sanchez |
| collection | DOAJ |
| description | Efficient cropping requires yield estimation for each involved crop, where data-driven models are commonly applied. In recent years, some data-driven modeling technique comparisons have been made, looking for the best model to yield prediction. However, attributes are usually selected based on expertise assessment or in dimensionality reduction algorithms. A fairer comparison should include the best subset of features for each regression technique; an evaluation including several crops is preferred. This paper evaluates the most common data-driven modeling techniques applied to yield prediction, using a complete method to define the best attribute subset for each model. Multiple linear regression, stepwise linear regression, M5′ regression trees, and artificial neural networks (ANN) were ranked. The models were built using real data of eight crops sowed in an irrigation module of Mexico. To validate the models, three accuracy metrics were used: the root relative square error (RRSE), relative mean absolute error (RMAE), and correlation factor (R). The results show that ANNs are more consistent in the best attribute subset composition between the learning and the training stages, obtaining the lowest average RRSE (86.04%), lowest average RMAE (8.75%), and the highest average correlation factor (0.63). |
| format | Article |
| id | doaj-art-41538ddf4aff4d0fa7c44b0ec681c7f4 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-41538ddf4aff4d0fa7c44b0ec681c7f42025-02-03T06:00:19ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/509429509429Attribute Selection Impact on Linear and Nonlinear Regression Models for Crop Yield PredictionAlberto Gonzalez-Sanchez0Juan Frausto-Solis1Waldo Ojeda-Bustamante2IMTA, Boulevard Cuauhnáhuac 8532, Colonia Progreso, 62550 Jiutepec, MOR, MexicoUPEMOR, Boulevard Cuauhnáhuac 566, Colonia Lomas del Texcal, 62550 Jiutepec, MOR, MexicoIMTA, Boulevard Cuauhnáhuac 8532, Colonia Progreso, 62550 Jiutepec, MOR, MexicoEfficient cropping requires yield estimation for each involved crop, where data-driven models are commonly applied. In recent years, some data-driven modeling technique comparisons have been made, looking for the best model to yield prediction. However, attributes are usually selected based on expertise assessment or in dimensionality reduction algorithms. A fairer comparison should include the best subset of features for each regression technique; an evaluation including several crops is preferred. This paper evaluates the most common data-driven modeling techniques applied to yield prediction, using a complete method to define the best attribute subset for each model. Multiple linear regression, stepwise linear regression, M5′ regression trees, and artificial neural networks (ANN) were ranked. The models were built using real data of eight crops sowed in an irrigation module of Mexico. To validate the models, three accuracy metrics were used: the root relative square error (RRSE), relative mean absolute error (RMAE), and correlation factor (R). The results show that ANNs are more consistent in the best attribute subset composition between the learning and the training stages, obtaining the lowest average RRSE (86.04%), lowest average RMAE (8.75%), and the highest average correlation factor (0.63).http://dx.doi.org/10.1155/2014/509429 |
| spellingShingle | Alberto Gonzalez-Sanchez Juan Frausto-Solis Waldo Ojeda-Bustamante Attribute Selection Impact on Linear and Nonlinear Regression Models for Crop Yield Prediction The Scientific World Journal |
| title | Attribute Selection Impact on Linear and Nonlinear Regression Models for Crop Yield Prediction |
| title_full | Attribute Selection Impact on Linear and Nonlinear Regression Models for Crop Yield Prediction |
| title_fullStr | Attribute Selection Impact on Linear and Nonlinear Regression Models for Crop Yield Prediction |
| title_full_unstemmed | Attribute Selection Impact on Linear and Nonlinear Regression Models for Crop Yield Prediction |
| title_short | Attribute Selection Impact on Linear and Nonlinear Regression Models for Crop Yield Prediction |
| title_sort | attribute selection impact on linear and nonlinear regression models for crop yield prediction |
| url | http://dx.doi.org/10.1155/2014/509429 |
| work_keys_str_mv | AT albertogonzalezsanchez attributeselectionimpactonlinearandnonlinearregressionmodelsforcropyieldprediction AT juanfraustosolis attributeselectionimpactonlinearandnonlinearregressionmodelsforcropyieldprediction AT waldoojedabustamante attributeselectionimpactonlinearandnonlinearregressionmodelsforcropyieldprediction |