A Comparative Study of Different Algorithms used to Predict the Crop, its Yield and Price
Keywords:Machine Learning, Decision Tree Regressor, Random Forest, KNN, Neural Network, PSO, BP, Crop Recommendation, Yield Prediction, Back Propogation
India is considered an agricultural land and many people have agriculture as their occupation. So India is in dire need of having Crop and its yield as well as its price prediction. Based on soil pH, Rainfall, humidity, temperature, and various factors we can predict the result. Our system will try to predict and recommend crop by considering some soil and atmospheric parameters. So there are various algorithms and techniques that can be taken into consideration like Decision tree Regressor, Random Forest, Particle Swarm Optimization (PSO)-Back Propagation (BP) Neural Network Model, K- Nearest Neighbor (KNN). After comparing the algorithms our aim is to find the best suitable algorithms for prediction which will lead us to find a proper crop according to a given set of factors.