A comparative study of machine learning-based classification of tomato fungal diseases: Application of GLCM texture features
Publication Type
Journal Article
Publication Date (Issue Year)
2023
Journal Name
Heliyon
Abstract
Over the Sahel region, air temperature is anticipated to rise by 2.0 to 4.3 C by 2080. This increase is likely to affect human life. Thus, air temperature forecasting is an important research topic. This study compares the performance of stacked Ensemble Model and three regressors: Gradient Boosting, CatBoost and Light Gradient Boosting Machine for daily Maximum Temperature and Minimum Temperature forecasting based on the five lagged values. Results obtained demonstrate that the Ensemble Model outperformed the regressors as follows for each parameter; Maximum Temperature: MSE 2.8038, RMSE 1.6591 and 0.8205. For Minimum Temperature: MSE 1.1329, RMSE 1.0515 and 0.9018. Considering these results, Ensemble Model is observed to be feasible for daily Maximum and Minimum Temperature forecasting.
Keywords
Regressors, Stacked Ensemble Model, Daily Temperature Forecasting, Senegal
Rsif Scholar Name
Chimango Nyasulu
Thematic Area
ICTs Including Big Data and Artificial Intelligence
Africa Host University (AHU)
University of Gaston Berger (UGB), Senegal
Recommended Citation
Nyasulu, C., Diattara, A., Traore, A., Deme, A., & Ba, C. (2023). A comparative study of machine learning-based classification of tomato fungal diseases: Application of GLCM texture features. Heliyon, e21697. https://doi.org/10.1016/j.heliyon.2023.e21697