As a traditionally agricultural country, Bangladesh has a major economic dependency on the crops it grows. Predicting the production of crops is a significant part of the economic program of the country. Among the many domains that can be used to perform the prediction, this report adopts the parameters of the weather to foretell the production of crops. Correlating between the yield of crops and the climate has been widely experimented upon across the globe. This study adapts and improves on the techniques and processes introduced in previous studies to derive a broader, localized and intuitive correlation between the two entities. Using standard approaches of machine learning – linear regression, support vector machine, random forest and more – this paper not only provides appropriate prediction models for all the crops considered but also infers the numeric effect of various climate factors on the unit production 14 of the crops.
Using standard approaches of machine learning – linear regression, support vector machine, random forest and more – this study provides appropriate prediction models for all the crops considered and infers the numeric effect of various climate factors on the unit production of the crops.
Machine learning
