Abstract:
The agricultural sector is becoming increasingly significant in the global economy. The
daily growth of the world population necessitates a high level of crop production and
yield rate in order for people to live. However, as the human population grows, the
environment also changes as a result of human activity. So, it has led to difficulties
in weather prediction, which is essential to crop cultivation. This demands a proper
mechanism for predicting weather for farming. Farmers will be benefited if they can have
an estimate of how much yield rate they can harvest and what is the price range they will
be able to get for their efforts. As a result, machine learning technologies have become
novel and trending technology among the agricultural sector due to their ability to
provide accurate predictions regarding farming. Among all of these, selecting the suitable
crops for cultivation has become critical. This study has proposed a machine learning
approach to predict the right crop for a specified period. Decision Tree Regression and
Random Forest Regression machine learning models have been used in the study to
predict the rainfall and price of the crops. To select the best performing models, the
authors have used root mean square error and R square value for the coefficient of
determination. In the case of rainfall prediction, the Decision Tree obtained 12.07 and
0.03 for RMSE and R squared respectively. In the case of price prediction, Random
Forest obtained 10.58 and 0.92 for RMSE and R squared respectively.
