Crop Suggester Using Machine Learning Techniques

India's agricultural sector and its affiliated industries are undoubtedly among the most important sources of income, contributing a significant portion to the nation's GDP. The country's vast rural areas provide great potential, but agricultural output per hectare remains alarmingly low compared to global standards. This discrepancy may contribute to the high suicide rates among India's marginal farmers. This study presents a practical and user-friendly crop yield forecasting technique aimed at assisting these farmers. The proposed system integrates GPS technology to identify the user's location and connect them to the system. Farmers input data such as their region and soil type, and machine learning algorithms are applied to predict crop yield or recommend the most suitable crops. Crop yield prediction methods used in this study include Random Forest(RF), Logistic Regression(LR), XGBoost, ADAboost Among these, Random Forest achieved the highest accuracy, with 99%. Additionally, the algorithm suggests optimal crops based on the maximum crop suggested by other models, which makes confident to choose as best model.