Machine Learning Based Crop Prediction on Region Wise Weather Data

Abstract

Agriculture is a primordial occupation for human civilization, whereby farmers cultivate domesticated species of food. It refers to farming in general, which is an art and science that attempts to reform a component of the Earth's exterior through the cultivation of plants and other crops, as well as raising livestock for sustenance or other necessities for the soul and economic gain. As a result of the vital role that sustainable agriculture plays in the overall health of the nation, this sector of the economy has been the incubator for some of the most cutting-edge technological advances in recent history. Scientists and farmers have been working together to discover new methods that will allow them to increase crop production while simultaneously decreasing their water consumption and lessening then negative effects on the environment. Machine learning, deep learning, and a number of other methodologies are some examples of these approaches. A crop's expansion and maturation are both heavily influenced by the climate in which it is grown. The local climate, namely its wind speed, temperature, rainfall, and humidity, is the most exigent factor in determining the advancement or failure of crop production. If the weather is predicted prior to crop cultivation, it will be beneficial to the farmer. Machine learning is a new innovation that can solve people's real-life problems. It is a technique where a machine can act like a human and learn through experiences and the use of different types of data. Now a day. Agriculture is one of the fields of machine learning where we use different types of machine learning algorithms to predict crop production based on climate data which can benefited farmers to increase the production of the crop. In these studies, we are going to predict crop yield using LSTM based on predicted weather data.