Agricultural greenhouses are an essential component of modern agricultural facilities,and accurate identification and dynamic monitoring of their distribution provide a reliable scientific basis for the government to carry out agricultural subsidy accounting and agricultural production decision-making.In this paper SFNet-F image processing technology is proposed to address the issue of low accuracy in traditional image recognition methods.By collecting agricultural greenhouse datasets of different types,periods,and regions,the FixMatch semi-supervised learning module is combined with SFNet to improve the efficiency and quality of sample library establishment,reduce costs,and achieve high-precision semi-supervised adaptive segmentation.In order to evaluate the feasibility of this method,multiple accuracy evaluation indicators were selected for accuracy validation in Pingquan city,Hebei province,and compared with U-Net,HBRNet,and DeepLabV3+.The results show that the deep learning model based on the SFNet-F embedded SuperMap platform can identify agricultural greenhouses on a large scale quickly and accurately.The recognition effect is the best in all accuracy indicators compared to several popular methods.
维普
万方数据