One of the most significant elements affecting agricultural yield is weeds. The waste and contamination of rural environments caused by full-coverage herbicide spraying is becoming more visible. With the continual improvement in agricultural production levels, it is critical to differentiate crops from weeds and to achieve accurate weed-only spraying. However, precise weed and crop identification and localization are required for spraying. In order to increase the crop yield and reduce the threats imposed by weeds in agriculture, a measure is taken to identify and classify the weeds and crops with the help of deep learning techniques. Convolutional neural networks render a good way to identify the weeds that harms the crop’s growth. Aiming at achieving a greater accuracy, models such as CNN and MASKR-CNN were built. Comparatively, CNN resulted with 94.29% as training accuracy and 100% accuracy of validation in VGG16 architecture. Therefore, by the suggested method, there is a lot of possibility to reduce the manual work to identify crops and weeds. According to results, with the fine tuning of hyper parameters, accuracy can be increased.