Intelligent image annotation system for smart agriculture
This study proposed an intelligent image annotation system for smart agriculture,aiming to solve the problem of time-consuming image annotation in smart agriculture scenes.Firstly,based on the idea of active learning,a novel query strategy was designed for image filtering.The images to be annotated were classified into difficult to annotate and easy to annotate images.The difficult to annotate images were used to optimize the automatic annotation model,which was beneficial for improving the accuracy of automatic annotation and providing convenience for annotating small-scale datasets;then,a deep neural network model was used to predict the annotated regions and categories of easily annotated images.The boundary information and label categories of the predicted regions were written into a json format file to achieve automatic annotation.By improving the Yoact instance segmentation model,overlapping images were annotated;finally,the json format file obtained from automatic annotation were manually verified and adjusted.Through detailed comparative experiments of automatic annotation results(such as wheat,grapes,Saint Mary's fruit,etc.),it wash proven that the intelligent annotation system showed excellent performance in the field of smart agriculture;through quantitative comparative experiments such as manual correction proportion,annotation time,and annotation efficiency,it was verified that compared to traditional manual annotation methods,it had higher efficiency;through comparative experiments with other existing intelligent image annotation systems,it was found that the proposed intelligent image annotation system was more suitable for the field of smart agriculture.This research result provides a new intelligent annotation tool for smart agricultural image annotation,which has important application value and promotion prospects.We believe that this intelligent image annotation system will have a profound impact on the future field of smart agriculture.
NETL
NSTL
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