Agricultural Crop Recommendations Based on Productivity and Season

Tamil Nadu, a coastal state, faces uncertainty in agricultural productivity. With more people and area available, increasing output should be possible, however, it is not. Farmers once relied on word of mouth, but current environmental conditions make that impossible. Agricultural metrics and environmental factors produce the data that is used to get insights into the Agri-facts. In order to give farmers accurate agricultural information, the IT sector's growth has resulted in several improvements in agriculture sciences. It is desirable in the current environment to be able to use modern technology in agriculture. Making predictions and building a precise model from the data is possible with the use of machine learning techniques. Agricultural problems like crop forecasting, crop rotation, and crop protection and water fertilizer needs are solvable. Due to the constantly changing seasons, it is essential to use a systematic approach to make crop growing easier and to support farmers in their farming operations and administration. Aspiring farmers could be able to use better agricultural techniques as a result. A farmer might receive a system of recommendations to help with crop output by means of data mining. Depending on their quantity and weather conditions, crops are recommended for usage in this method.