Combining SMOTE sampling and Machine Learning for Forecasting Wheat Yields in France

Amine Chemchem (1) , Francois Alin (1) , Michaël Krajecki (1)

1 CRESTIC - Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804

This paper describes a method of predicting wheat yields based on machine learning, which accurately determines the value of wheat yield losses in France. Obtaining reliable value from yield losses is difficult because we are tackling a highly unbalanced classification problem. As part of this study, we propose applying the Synthetic Minor Oversampling technique (SMOTE) as a pretreatment step before applying machine learning methods. The approach proposed here improves the accuracy of learning and allows better results on the set of tests by measuring the operating characteristic of the ROC receiver. The comparative study shows that the best result obtained is 90.07% on the set of tests, obtained by hybridizing the SMOTE algorithm with the Random Forest algorithm. The results obtained in this study for wheat yield can be extended to many other crops such as maize, barley, ...

Mots clés: Supervised Classification; Smart Agriculture; Knowledge Discovery; Index Terms-Machine Learning; Sampling Methods; Imbalanced Learning

Domaines: Intelligence artificielle [cs.AI]; Base de données [cs.DB]; Apprentissage [cs.LG]; Réseau de neurones [cs.NE]

Amine Chemchem, Francois Alin, Michaël Krajecki. Combining SMOTE sampling and Machine Learning for Forecasting Wheat Yields in France. International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), 2019, Cagliari, Italy. ⟨10.1109/AIKE.2019.00010⟩. ⟨hal-02523637⟩