Initializing the weights plays an essential role in a convolutional neural network model. This paper investigates how Glorot and He''s initialization methods behave in Mobilenet and Resnet models on the weeds classification problem. Experiments show that pointwise and depthwise convolution in Mobilenet reduces the variance of feature maps from earlier layers. Using the He’s method, shortcut connection in Resnet saturate values in logistic classify layer. The accuracy of Mobilenet and Resnet, using Glorot''s method, are 0.9568 and 0.9711, respectively. While using He''s method, we obtain 0.9471 using Mobilenet and 0.9645 using Resnet. Also, both models converge faster and better generalization using Glorot''s method than using He''s method.