Durum wheat yield forecasting using machine learning
A reliable and accurate forecasting model for crop yields is crucial for effective decision-making in every agricultural sector. Machine learning approaches allow for building such predictive models, but the quality of predictions decreases if data is scarce. In this work, we proposed data-augmentat...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2022-01-01
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| Series: | Artificial Intelligence in Agriculture |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589721722000137 |
| _version_ | 1811196700786688000 |
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| author | Nabila Chergui |
| author_facet | Nabila Chergui |
| author_sort | Nabila Chergui |
| collection | DOAJ |
| description | A reliable and accurate forecasting model for crop yields is crucial for effective decision-making in every agricultural sector. Machine learning approaches allow for building such predictive models, but the quality of predictions decreases if data is scarce. In this work, we proposed data-augmentation for wheat yield forecasting in the presence of small data sets of two distinct Provinces in Algeria. We first increased the dimension of each data set by adding more features, and then we augmented the size of the data by merging the two data sets. To assess the effectiveness of data-augmentation approaches, we conducted three sets of experiments based on three data sets: the primary data sets, data sets with additional features and the augmented data sets obtained by merging, using five regression models (Support Vector Regression, Random Forest, Extreme Learning Machine, Artificial Neural Network, Deep Neural Network). To evaluate the models, we used cross-validation; the results showed an overall increase in performance with the augmented data. DNN outperformed the other models for the first Province with a Root Mean Square Error (RMSE) of 0.04 q/ha and R_Squared (R2) of 0.96, whereas the Random Forest outperformed the other models for the second Province with RMSE of 0.05 q/ha. The data-augmentation approach proposed in this study showed encouraging results. |
| first_indexed | 2024-04-12T01:03:25Z |
| format | Article |
| id | doaj.art-546aeb186708475abb89e44b467f5c1f |
| institution | Directory Open Access Journal |
| issn | 2589-7217 |
| language | English |
| last_indexed | 2024-04-12T01:03:25Z |
| publishDate | 2022-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Artificial Intelligence in Agriculture |
| spelling | doaj.art-546aeb186708475abb89e44b467f5c1f2022-12-22T03:54:21ZengKeAi Communications Co., Ltd.Artificial Intelligence in Agriculture2589-72172022-01-016156166Durum wheat yield forecasting using machine learningNabila Chergui0Corresponding author.; Faculty of Technology, Ferhat Abbas University, Setif 1. MISC Laboratory Abdelhamid Mehri University, Constantine 2, Algeria.A reliable and accurate forecasting model for crop yields is crucial for effective decision-making in every agricultural sector. Machine learning approaches allow for building such predictive models, but the quality of predictions decreases if data is scarce. In this work, we proposed data-augmentation for wheat yield forecasting in the presence of small data sets of two distinct Provinces in Algeria. We first increased the dimension of each data set by adding more features, and then we augmented the size of the data by merging the two data sets. To assess the effectiveness of data-augmentation approaches, we conducted three sets of experiments based on three data sets: the primary data sets, data sets with additional features and the augmented data sets obtained by merging, using five regression models (Support Vector Regression, Random Forest, Extreme Learning Machine, Artificial Neural Network, Deep Neural Network). To evaluate the models, we used cross-validation; the results showed an overall increase in performance with the augmented data. DNN outperformed the other models for the first Province with a Root Mean Square Error (RMSE) of 0.04 q/ha and R_Squared (R2) of 0.96, whereas the Random Forest outperformed the other models for the second Province with RMSE of 0.05 q/ha. The data-augmentation approach proposed in this study showed encouraging results.http://www.sciencedirect.com/science/article/pii/S2589721722000137Machine learningYield forecastDeep learningData augmentationRegressionClimate data |
| spellingShingle | Nabila Chergui Durum wheat yield forecasting using machine learning Artificial Intelligence in Agriculture Machine learning Yield forecast Deep learning Data augmentation Regression Climate data |
| title | Durum wheat yield forecasting using machine learning |
| title_full | Durum wheat yield forecasting using machine learning |
| title_fullStr | Durum wheat yield forecasting using machine learning |
| title_full_unstemmed | Durum wheat yield forecasting using machine learning |
| title_short | Durum wheat yield forecasting using machine learning |
| title_sort | durum wheat yield forecasting using machine learning |
| topic | Machine learning Yield forecast Deep learning Data augmentation Regression Climate data |
| url | http://www.sciencedirect.com/science/article/pii/S2589721722000137 |
| work_keys_str_mv | AT nabilachergui durumwheatyieldforecastingusingmachinelearning |