A Novel Crop Weed Recognition Method Based on Transfer Learning from VGG16 Implemented by Keras

Abstract

This study used a method based on convolutional neural network model, VGG16, to identify images of weeds in the field. As the basic network, VGG16 has very good classification performance, and the network structure is unconventional. It is relatively easy to modify. It can fine-tune other data sets on this basis. Therefore, the transfer learning method is applied to our own Kaggle competition website. Download the weed data set. The site covers approximately 3, 500 images in 12 categories. Due to limited data and computational power, our model fixes the first 14 layers of VGG16 parameters for layer-by-layer automatic extraction of features, adding an average pooling layer, convolution layer, Dropout layer, fully connected layer, and softmax for classifiers. The layer has a total of 5 layers, for a total of 19 layers. The experimental results show that the final model performs well in the classification effect of 12 weed images. The accuracy rate on the training set is 98.99%, and the accuracy on the verification set is 91.08%. It can be applied to crop weed identification. It provides accurate and reliable judgment basis for positioning and quantitative chemical pesticide spraying, and is the key to achieving refined agriculture.