Smart Farming-A Crop Recommendation System

India appears to be progressing in every area, but there are few prospects and no economic growth in the agriculture sector. This disadvantage is primarily caused by the use of conventional farming techniques. Agronomic expansion is one of the key elements that support the nation's increased economic growth. India is the greatest country for crop cultivation, with the largest amount of agricultural land, approximately 179.9 million hectares, according to statistics and surveys. However, recent changes in the environment's climate have resulted in a sharp decline in crop productivity, which has had a significant impact on farmers. In the current world, it is difficult to predict the climate for growing crops because of the dominant mineral, soil, and temperature elements. Discovering what kind of crops are required to grow a given soil has grown to be a significant problem. This paper includes theoretical and integrated machine learning techniques that can help predict crop cultivation with a higher percentage of accuracy and efficiency. These techniques include Simple Vector Machine, Random Forest Classifier, Logistic Regression, and Natural Language Processing associated with Artificial Intelligence. In addition to improving agricultural productivity, this machine learning technology aids farmers in estimating production costs. By only forecasting the crops that can be grown in the ideal conditions, it makes farming easier. The training of the data models, to which the entire agricultural community has adapted, is the foundation for the outcomes we generate. This research can help farmers choose which crops are suitable for the current farming conditions.