Abstract

India is a country of farmers, and the sector employs millions of people worldwide and forms the core of the Indian economy. Because they don't know anything about the conditions of the soil, farmers just keep cultivating the same crops with haphazard amounts of fertilizer. In the end, this causes the soil and its top layer to become acidic. If farmers know how to develop things that fit their own living environments, they can attain larger yields. Precision agriculture is being used in this research to create a crop suggestion system that will assist farmers in raising crop yields. Therefore, in order to solve these issues, we used machine learning techniques such as Random Forest and Decision Tree to create a crop recommendation system. The goal of this research is to assist farmers by recommending the best crop to produce based on soil conditions, weather, and other factors such as temperature, humidity, rainfall, and levels of phosphorus, potassium, and nitrogen in the soil. Therefore, farmers can learn about the many crops they need to produce through our project in order to maximize their production, which will enhance their profit and reduce soil pollution.