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- Title
Farmland boundary extraction based on the AttMobile-DeeplabV3+ network and least squares fitting of straight lines.
- Authors
Hao Lu; Hao Wang; Zhifeng Ma; Yaxin Ren; Weiqiang Fu; Yongchao Shan; Shupeng Hu; Guangqiang Zhang; Zhijun Meng
- Abstract
The rapid extraction of farmland boundaries is key to implementing autonomous operation of agricultural machinery. This study addresses the issue of incomplete farmland boundary segmentation in existing methods, proposing a method for obtaining farmland boundaries based on unmanned aerial vehicle (UAV) remote sensing images. Themethod is divided into two steps: boundary image acquisition and boundary line fitting. To acquire the boundary image, an improved semantic segmentation network, AttMobile-DeeplabV3+, is designed. Subsequently, a boundary tracing function is used to track the boundaries of the binary image. Lastly, the least squares method is used to obtain the fitted boundary line. The paper validates the method through experiments on both crop-covered and noncrop- covered farmland. Experimental results show that on crop-covered and non-crop-covered farmland, the network's intersection over union (IoU) is 93.25% and 93.14%, respectively; the pixel accuracy (PA) for crop-covered farmland is 96.62%. The average vertical error and average angular error of the extracted boundary line are 0.039 and 1.473°, respectively. This research provides substantial and accurate data support, offering technical assistance for the positioning and path planning of autonomous agricultural machinery.
- Subjects
LEAST squares; GEOGRAPHIC boundaries; DRONE aircraft; REMOTE sensing; FARM management
- Publication
Frontiers in Plant Science, 2023, p1
- ISSN
1664-462X
- Publication type
Article
- DOI
10.3389/fpls.2023.1228590