Abstract: Agriculture is absolutely essential to human existence. India is the place where there is agribusiness and it is the significant wellspring of economy. Most of Indian populace straight forwardly depends on farming. So, the prediction of crops is especially important in agriculture and mostly dependent on the soil and environmental factors, such as temperature, humidity and rainfall. Prior research has achieved accurate classification through appropriate feature selection; however, the prediction of feature selection is time consuming. The Hybrid Classification Algorithm is presented by combining the Improved Recursive Feature Selection Algorithm with classification is produced. Now, this present Feature Selection methods greatly enhance the circumstances that redundant features will appear in the final subset, so that finding and removing them can improve the classification accuracy in most cases. To address this issue hybrid classification algorithm has used. Comparing this hybrid classification algorithm to other classification algorithms like Naive Bayes, Random Forest algorithms reveals that it has a high performance metrics called Precision, Recall, Accuracy and F1-Score ratio (The Predictive ability of the model).