Most of the countries in this world are dependent on agriculture, and a lot of the population depends on their predator’s knowledge in choosing the crop selection. However, as the years pass, change in agricultural land makes it difficult for the farmers in getting the expected yield. To improve the yield from the agricultural land, a change in the crop is recommended. Instead of using predator’s knowledge for crop selection, we can make it easy and productive by using precision agriculture a new farming technique that recommends crops based on nutrition values in the land and weather conditions. In our work, we build a deep learning model using convolutional neural networks (CNNs) to predict a crop for selected land based on nutrition values in the land and weather conditions. The CNN model is trained and tested using research data with nutrition values and weather conditions that predict suitable crops for that land. Our model achieved an accuracy score of 93.69% on training data and 94.20% on testing data. The CNN model is compared with other baseline models.