Agricultural greenhouse information extraction based on SFNet-F land feature recognition technology

doi:10.13474/j.cnki.11-2246.2024.0712

Abstract

Abstract: 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.

Key words: agricultural greenhouse, information extraction, SFnet-F, ground-objects identification

CLC Number:

P237

FU Lizhao, YANG Qinggang, CHEN Yongli, HAN Jinting, YANG Xinjia. Agricultural greenhouse information extraction based on SFNet-F land feature recognition technology[J]. Bulletin of Surveying and Mapping, 2024, 0(7): 65-70.

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