The proposed works are part of the Moroccan national project 'Green Morocco', which aims to develop the agricultural sector to prevent famine and support the economic development of the country. The objective is to propose a new method for yield estimation, crop forecasts and pre-harvest yields. This new methodology developed for introducing unsupervised learning and data mining algorithms. The method used, predicting yields using agro-climatic data analysis and climate change predictions. Our approach based on three models: first, we propose to use climate change-based climate predictions for climate classification in a given region and analysed the changes that have affected this climate. Second, using climate rules, we will look for climatic rules for each crop. Thirdly, to put in place a predictive model that will later enable climate change adaptation solutions for the future. Finally, we are also introducing a new dimensional reduction of features.
Fathi, MT; Ezziyyani, M
How can data mining help us to predict the influence of climate change on Mediterranean agriculture?
International Journal Of Sustainable Agricultural Management And Informatics