In response to problems such as slow detection speed of text in agricultural materials image and lack of mobile applications,based on the agricultural materials image dataset,a Ghost module-based text detection algorithm for agricultural materials image was proposed,which improved the DB network,used the MobileNetv2 network to extract the base features,introduced a multi-scale fea-ture fusion module to obtain feature fusion between multiple layers,and used a differentiable binary post-processing algorithm to pre-dict the text,making it possible to quickly detect the text in agricultural materials image.The accuracy of the algorithm on the agricul-tural materials image dataset was basically up to the standard of mainstream algorithms,with a detection speed of 18.6 img/s and a cen-sus count of 2.99 M,with lightweight features,and the algorithm was deployed to mobile devices and ran successfully.
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