Findings from China Agricultural University in the Area of Agricultural Insurance Described (Deep Edge Enhancement-based Semantic Segmentation Network for Farmland Segmentation With Satellite Imagery): Agriculture – Agricultural Insurance

2022 NOV 21 (NewsRx) -- By a News Reporter-Staff News Editor at Agriculture Daily -- New research on Agriculture - Agricultural Insurance is the subject of a report. According to news reporting from Beijing, People’s Republic of China, by NewsRx journalists, research stated, “The participation of insured smallholders and involvement of related requirements have been emerging and increasing with the popularization and promotion of agricultural insurance. Compared with traditional field surveys, remote sensing and deep learning technologies, with their rapid development in recent years, have provided an automatic and effective means for underwriting control in the agricultural insurance industry.”

Financial support for this research came from Science and technology innovation project of Chinese Academy of Agricultural Sciences.

The news correspondents obtained a quote from the research from China Agricultural University, “Moreover, ‘smallholder-wise underwriting ‘ is intensively expected by the local government and insurance companies to ensure precise claim settlements. In this process, farmland segmentation is a crucial and fundamental step for smallholder farmland claim settlements. However, segmentation of smallholder farmland parcels from satellite images remains a challenge, as the ridges and roads between farmland parcels are usually narrow and display confusing characteristics with their neighboring farmlands. This challenge causes undersegmentation on the farmland boundaries, leading to inaccurate and incomplete farmland parcel recognition. In this study, we aim to solve this problem by proposing a novel Deep Edge Enhancement Semantic Segmentation Network to refine parcels’ boundary segmentation and improve the closure level of the farmland segmentation. We designed a framework for farmland edge enhancement through pseudo road label generation and model fusion. The proposed network aggregates feature extraction for ridge and road segmentation to improve its performance on farmland parcel recognition. Furthermore, we found that the commonly used image segmentation evaluation metric, such as pixel-wise-based mean Intersection over Union, cannot objectively reflect the effectiveness of the smallholder farmland segmentation. Therefore, we proposed two block-wise farmland evaluation metrics that are consistent with the practical evaluation rules and requirements of farmland segmentation at the parcel level.”

According to the news reporters, the research concluded: “We implemented experiments in the study area of Jiaxiang County in northern China using the GaoFen-1 4-band multispectral images (red, green, blue, and near-infrared) at a spatial resolution of 2 m. Experimental results demonstrated the effectiveness of our method, which outperformed the baseline DeepLabv3+ network.”

This research has been peer-reviewed.

For more information on this research see: Deep Edge Enhancement-based Semantic Segmentation Network for Farmland Segmentation With Satellite Imagery. Computers and Electronics in Agriculture, 2022;202. Computers and Electronics in Agriculture can be contacted at: Elsevier Sci Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, Oxon, England. (Elsevier - www.elsevier.com; Computers and Electronics in Agriculture - http://www.journals.elsevier.com/computers-and-electronics-in-agriculture/)

Our news journalists report that additional information may be obtained by contacting Wenyi Sheng, China Agricultural University, Ministry of Education, Key Lab Smart Agr Syst Integrat, Beijing 100083, People’s Republic of China. Additional authors for this research include Wei Sun, Yuxia Zhu, Ailian Chen, Sijian Zhao, Qiao Zhang and Rong Zhou.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.compag.2022.107273. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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