The agricultural sector has recognized that, for crop management to thrive, acquiring relevant information on plants is needed. Weeds have a devastating impact on crop production and yield in general. Current practice uses uniform application of herbicides leading to high costs and degradation of the environment and the field productivity. The identification and classification of weeds are of major technical and economic importance in the agricultural industry. To automate these activities, like in shape, color and texture, a weed control system is feasible. A new Deep learning method proposed in this project identifies the type of crop using CNN. In this project different data augmentation techniques have been used for improving the classification accuracies which has been discussed to increase the performance which will help in improving the validation and training accuracies and characterization of exactness of the CNN model and accomplished various results.