Abstract:
Bangladesh is an agricultural country, and it is the backbone of our nation. Agriculture
employs over half of Bangladesh's people, and crops occupy more than 70% of the
country's territory. More than 12 percent of revenue comes from the agriculture sector. But
our lands are limited, and our population is growing day by day. That's why we need more
food and increase the demand for crops continuously. So, it is a huge challenge to increase
crop production. But our farmers face different types of problems during cultivation. They
cannot justify which crop should be cultivated. Because of this, they did not get the
expected yield. We know that machine learning plays a vital role in agricultural prediction.
Crop prediction is a complex process. A massive amount of data is needed, like
temperature, humidity, precipitation, wind speed, dew etc. In this system, we applied
different types of machine learning algorithms and checked which algorithm gives us better
accuracy. We get Random Forest gives us the best accuracy. So, we applied it to our
dataset. We collect weather data from NASA Power Access Viewer. And crop data from
different sources. Then we apply training and testing to this dataset. We took 80% data for
training and 20% for testing. We get 91% accuracy from the Random Forest algorithm,
which will help the farmer to decide which crop should be cultivated. It will increase crop
production. It Removes hunger and poverty from our country.
