Bok Choy and Weed Identification Based on Deep Convolutional Neural Networks
Due to the diversity and complex distribution of weeds in bok choy fields,the existing methods for weed identification have the problems of low efficiency,poor accuracy and lack of robustness.This study proposed a method to identify bok choy and weeds based on deep convolutional neural networks,using seedling stage bok choy and their associated weeds as the research objects.Firstly,image processing methods were used to mark images containing green plants,and then a neural network model was used to distinguish bok choy and weeds.In order to investigate the recognition effect of different neural network models,the DenseNet model,GoogLeNet model and ResNet model were used to recognize images containing bok choy or weed images,and the F1 value,overall accuracy and recognition speed were used as evaluation criteria.The experimental results showed that the 3 neural network models could effectively distinguish bok choy and weeds,and the ResNet model was the optimal model,with an overall accuracy and recognition speed of 97.2%and 78.34 frames·s-1 on the testing datasets,respectively.The bok choy and weed identification method proposed in this study could effectively reduce the complexity of weed identification,improve the robustness and generalization ability of identification,and laid the foundation for the research on precision weed control in bok choy fields.
deep learningconvolutional neural networkbok choy recognitionweed recognition
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