Web Application Development for Site Specific Crop Prediction using Machine Learning
Site-specific crop prediction (SSCP) is a crop prediction technique that is based upon observing, measuring, and responding to various aspects such as rainfall, climate, and soil conditions that affect the growth and yield of crops. It is one of many modern farming techniques which can make production more efficient. And to ensure soil conservation practices also. With the SSCP farmers can take a better yield and profit, the whole farming area can be utilized effectively, and the overall farm efficiency will also increase. In this we make use of the weather data, humidity, rainfall, land location, and other factors which affect the crop growth and yield rate to predict the best cost-effective crop using a prediction-based machine learning algorithm. The algorithm which we have used is the Random Forest Algorithm and some attributes i.e., rainfall, average maximum temperature, and average minimum temperature of a certain location, and gives prediction based on yield rate per unit area. By making use of this technique, we can produce better yield and can avoid losses.