Researchers from China University of Mining and Technology Publish Research in Agricultural Insurance (Winter Wheat Lodging Area Extraction Using Deep Learning with GaoFen-2 Satellite Imagery): Agriculture – Agricultural Insurance

2022 NOV 01 (NewsRx) -- By a News Reporter-Staff News Editor at Agriculture Daily -- Investigators publish new report on agricultural insurance. According to news reporting originating from Xuzhou, People’s Republic of China, by NewsRx correspondents, research stated, “The timely and accurate detection of wheat lodging at a large scale is necessary for loss assessments in agricultural insurance claims.”

Financial supporters for this research include Xuzhou Key R&D Program; Fundamental Research Funds For The Central Universities.

Our news correspondents obtained a quote from the research from China University of Mining and Technology: “Most existing deep-learning-based methods of wheat lodging detection use data from unmanned aerial vehicles, rendering monitoring wheat lodging at a large scale difficult. Meanwhile, the edge feature is not accurately extracted. In this study, a semantic segmentation network model called the pyramid transposed convolution network (PTCNet) was proposed for large-scale wheat lodging extraction and detection using GaoFen-2 satellite images with high spatial resolutions. Multi-scale high-level features were combined with low-level features to improve the segmentation’s accuracy and to enhance the extraction sensitivity of wheat lodging areas in the proposed model. In addition, four types of vegetation indices and three types of edge features were added into the network and compared to the increment in the segmentation’s accuracy. The F1 score and the intersection over union of wheat lodging extraction reached 85.31% and 74.38% by PTCNet, respectively, outperforming other compared benchmarks, i.e., SegNet, PSPNet, FPN, and DeepLabv3+ networks.”

According to the news reporters, the research concluded: “PTCNet can achieve accurate and large-scale extraction of wheat lodging, which is significant in the fields of loss assessment and agricultural insurance claims.”

For more information on this research see: Winter Wheat Lodging Area Extraction Using Deep Learning with GaoFen-2 Satellite Imagery. Remote Sensing, 2022,14(4887):4887. (Remote Sensing - http://www.mdpi.com/journal/remotesensing/). The publisher for Remote Sensing is MDPI AG.

A free version of this journal article is available at https://doi.org/10.3390/rs14194887.

Our news editors report that more information may be obtained by contacting Ziqian Tang, School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, People’s Republic of China. Additional authors for this research include Yaqin Sun, Guangtong Wan, Kefei Zhang, Hongtao Shi, Yindi Zhao, Shuo Chen, Xuewei Zhang.

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