Convolutional neural network based encoder-decoder architectures for semantic segmentation of plants

Recent advancements in the area of computer vision-based plant phenotyping are playing an important role, in determining the quantitative phenotypes of plants, and crop yield. Automatic segmentation of plants and its associated structures is the first and most important step in image based plant phe...

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Veröffentlicht in:Ecological informatics Jg. 64; S. 101373
Hauptverfasser: Kolhar, Shrikrishna, Jagtap, Jayant
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.09.2021
Schlagworte:
Arabidopsis
computer vision
crop yield
data collection
Fig plant segmentation
figs
leaves
neural networks
phenotype
Plant leaf segmentation
Plant semantic segmentation
plant stress
Residual U-Net
SegNet
tobacco
Fig plant segmentation
Plant leaf segmentation
SegNet
Plant semantic segmentation
Residual U-Net
ISSN:1574-9541
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Zusammenfassung:Recent advancements in the area of computer vision-based plant phenotyping are playing an important role, in determining the quantitative phenotypes of plants, and crop yield. Automatic segmentation of plants and its associated structures is the first and most important step in image based plant phenotyping. We design and implement convolutional neural network (CNN) based modified residual U-Net for semantic segmentation of plants from the background. We also use SegNet and U-Net architectures for comparison purpose. In this paper, residual U-Net, SegNet and U-Net models are tested on leaf segmentation challenge (LSC) dataset and fig dataset that are publicly available. LSC dataset consists of images of Arabidopsis and tobacco plants grown under controlled conditions whereas fig dataset includes top view images of fig plants captured in open-field conditions. We have used 8 evaluation metrics for analyzing and comparing the performance of residual U-Net, SegNet and U-Net architectures with the existing algorithms in the literature. Residual U-Net with 15.32 million trainable parameters, outperforms SegNet and other state-of-the-art methods whereas achieves comparable performance with respect to U-Net. Residual U-Net achieves dice coefficient of 0.9709 on LSC dataset and 0.9665 on fig dataset, respectively. The segmentation networks used in this paper can be used for other plant related applications such as plant trait estimation or in quantification of plant stress. •Semantic segmentation of plants and plant leaves•Segmentation performance analysis of SegNet, U-Net and residual U-Net on leaf segmentation challenge (LSC) and fig datasets•Comparative analysis of SegNet, U-Net and residual U-Net results with state-of-the-art methods
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ISSN:1574-9541
DOI:10.1016/j.ecoinf.2021.101373