Enhanced Crop Yield Prediction by Machine Learning Techniques

India's economy is based primarily on agriculture. The need for agriculture has increased along with India's population growth during the previous ten years. However, climatic changes and poor irrigation is a major threat to agricultural practices in India. Many farmers practice agriculture solely based on their experience which in a way reduces productivity and effectiveness. At present, machine learning techniques provide effective recommendations and predictions, helping to scale the production of crops across the country effectively. There are many machine learning based algorithms and techniques which help us accomplish effective solutions to such problems. In this work, Random Forest algorithm proposed which predicts the crops based on weather factors and soil factors. The weather factors include Rainfall, Temperature and Humidity. The soil factors include the chemical composition of the soil such as NPK values and pH of the soil. Real-time data used for the weather conditions, which makes the model robust and scalable. The test results will show two crop names along with its yield which are best suited for the respective conditions. Random Forest Classifier used for crop name recommendation and Random Forest Regressor for crop yield prediction. These results will assist farmers to grow crops effectively and also increase production of crops.