MLCROP

Machine Learning fro crop production in Iran

01/07/2023 to 30/06/2026

Crop production in Iran as one of the developing countries due to the majority of land has not been determined their
suitability for cultivation is low. Furthermore, population growth and the reduction of productive land and water resources led to extra pressure on the Iran’s crop production. There are many arable lands for agriculture in Iran, although they are not identifiable. Land suitability maps can identify very suitable areas for agricultural products and help increase their production. There is a shortage of land suitability assessment and associated information for agricultural crop in Iran because the surveying and mapping of land suitability in this country have followed the conventional approaches. The DSA approach applies ML algorithms and detailed and readily available environmental covariate data such as climate, terrain and vegetation to quantity decision making and support. herein, the DSA method and the SHL and DL approaches will be applied in Iran using soil performance data and environmental variables, where the land suitability maps are traditional and rare, to assess the impacts of climate change on land suitability changes and predicting the potential distribution of crops.