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A segmentation-guided deep learning framework for leaf counting
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Fan, Xijian, Zhou, Rui, Tjahjadi, Tardi, Das Choudhury, Sruti and Ye, Qiaolin (2022) A segmentation-guided deep learning framework for leaf counting. Frontiers in Plant Science, 13 . 844522. doi:10.3389/fpls.2022.844522 ISSN 1664-462X.
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WRAP-segmentation-guided-deep-learning-framework-leaf-counting-Tjahjadi-2022.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (4Mb) | Preview |
Official URL: https://doi.org/10.3389/fpls.2022.844522
Abstract
Deep learning-based methods have recently provided a means to rapidly and effectively extract various plant traits due to their powerful ability to depict a plant image across a variety of species and growth conditions. In this paper, we focus on dealing with two fundamental tasks in plant phenotyping, i.e., plant segmentation and leaf counting, and propose a two-steam deep learning framework for segmenting plants and counting leaves with various size and shape from two-dimensional plant images. In the first stream, a multi-scale segmentation model using spatial pyramid is developed to extract leaves with different size and shape, where the fine-grained details of leaves are captured using deep feature extractor. In the second stream, a regression counting model is proposed to estimate the number of leaves without any pre-detection, where an auxiliary binary mask from segmentation stream is introduced to enhance the counting performance by effectively alleviating the influence of complex background. Extensive pot experiments are conducted on the CVPPP 2017 Leaf Counting Challenge dataset, which contains images of Arabidopsis and tobacco plants. Experimental results demonstrate that the proposed framework achieves a promising performance both in plant segmentation and leaf counting, providing a reference for the automatic analysis of plant phenotypes.
| Item Type: | Journal Article | ||||||
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| Subjects: | Q Science > Q Science (General) Q Science > QH Natural history > QH426 Genetics Q Science > QK Botany |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||
| Library of Congress Subject Headings (LCSH): | Foliar diagnosis -- Data processing, Plant genetics -- Data processing, Growth (Plants) -- Data processing, Plant health -- Data processing, Phenotype -- Data processing, Deep learning (Machine learning) | ||||||
| Journal or Publication Title: | Frontiers in Plant Science | ||||||
| Publisher: | Frontiers Media S.A. | ||||||
| ISSN: | 1664-462X | ||||||
| Official Date: | 19 May 2022 | ||||||
| Dates: |
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| Volume: | 13 | ||||||
| Article Number: | 844522 | ||||||
| DOI: | 10.3389/fpls.2022.844522 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 18 May 2022 | ||||||
| Date of first compliant Open Access: | 18 May 2022 |
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