A segmentation network for farmland ridge based on encoder-decoder architecture in combined with strip pooling module and ASPP

In order to effectively support wheat breeding, farmland ridge segmentation can be used to visualize the size and spacing of a wheat field. At the same time, accurate ridge information collecting can deliver useful data support for farmland management. However, in the farming ridge segmentation scen...

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Bibliographic Details
Published in:Frontiers in plant science Vol. 15; p. 1328075
Main Authors: Hong, Qingqing, Zhu, Yue, Liu, Wei, Ren, Tianyu, Shi, Changrong, Lu, Zhixin, Yang, Yunqin, Deng, Ruiting, Qian, Jing, Tan, Changwei
Format: Journal Article
Language:English
Published: Switzerland Frontiers Media SA 01.02.2024
Frontiers Media S.A
Subjects:
Agricultural land
Connectivity
Crops
Data collection
Data processing
Datasets
Design
encoder-decoder
Encoders-Decoders
farmland ridge
Farms
Feature maps
Information processing
Modules
Plant breeding
Plant Science
Remote sensing
Semantic segmentation
Semantics
Software
Strip
strip pooling
Wheat
Jiangsu China
China
farmland ridge
remote sensing
semantic segmentation
strip pooling
encoder-decoder
ISSN:1664-462X, 1664-462X
Online Access:Get full text
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Summary:In order to effectively support wheat breeding, farmland ridge segmentation can be used to visualize the size and spacing of a wheat field. At the same time, accurate ridge information collecting can deliver useful data support for farmland management. However, in the farming ridge segmentation scenarios based on remote sensing photos, the commonly used semantic segmentation methods tend to overlook the ridge edges and ridge strip features, which impair the segmentation effect. In order to efficiently collect ridge information, this paper proposes a segmentation method based on encoder-decoder of network with strip pooling module and ASPP module. First, in order to extract context information for multi-scale features, ASPP module are integrated in the deepest feature map. Second, the remote dependence of the ridge features is improved in both horizontal and vertical directions by using the strip pooling module. The final segmentation map is generated by fusing the boundary features and semantic features using an encoder and decoder architecture. As a result, the accuracy of the proposed method in the validation set is 98.0% and mIoU is 94.6%. The results of the experiments demonstrate that the method suggested in this paper can precisely segment the ridge information, as well as its value in obtaining data on the distribution of farmland and its potential for practical application.
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Edited by: Marcin Wozniak, Silesian University of Technology, Poland
Yuanfang Chen, Hangzhou Dianzi University, China
Reviewed by: Jinling Zhao, Anhui University, China
ISSN:1664-462X
1664-462X
DOI:10.3389/fpls.2024.1328075