See the published version of this article at Plant Methods.
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Research
Qinghua Guo
Chinese Academy of Sciences
Corresponding Author
Zhenghui Liu
Nanjing Agricultural University
Corresponding Author
ORCiD: https://orcid.org/0000-0003-0590-3092
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Plant Methods.
Background : Identification and characterization of new traits with a sound physiological foundation is essential for crop breeding and management. Deep learning has been widely used in image data analysis to explore spatial and temporal information on crop growth and development, thus strengthening the power of the identification of physiological traits. This study aims to develop a novel trait that indicates source and sink relation in japonica rice based on deep learning.
Results : We applied a deep learning approach to accurately segment leaf and panicle and subsequently developed the procedure of GvCrop to calculate the leaf to panicle ratio (LPR) of rice populations during grain filling. Images of the training dataset were captured in the field experiments, with large variations in camera shooting angle, the elevation angle and the azimuth angle of the sun, rice genotype, and plant phenological stages. Accurately labeled by manually annotating all the panicle and leaf regions, the resulting dataset were used to train FPN-Mask (Feature Pyramid Network Mask) models, consisting of a backbone network and a task-specific sub-network. The model with the highest accuracy is then selected to study the variations in LPR among 192 rice germplasms and among agronomical practices. Despite the challenging field conditions, FPN-Mask models achieved a high detection accuracy, with Pixel Accuracy being 0.99 for panicles and 0.98 for leaves. The calculated LPRs showed large spatial and temporal variations as well as genotypic differences.
Conclusion : Deep learning techniques can achieve high accuracy in simultaneously detecting panicle and leaf data from complex rice field images. The proposed FPN-Mask model is applicable for detecting and quantifying crop performance under field conditions. The newly identified trait of LPR should provide a high throughput protocol for breeders to select superior rice cultivars as well as for agronomists to precisely manage field crops that have a good balance of source and sink.
Keywords
Plant phenotyping, Leaf and panicle detection, Deep learning, Physiological trait, Leaf to panicle ratio (LPR), Japonica rice
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On 26 Aug, 2020
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On 17 Aug, 2020
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On 17 Aug, 2020
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On 13 Aug, 2020
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On 12 Aug, 2020
Editor invited
On 12 Aug, 2020
Version 2
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Editorial decision: Major revision
On 04 Aug, 2020
Review #1 received
Received 23 Jul, 2020
Review #2 received
Received 23 Jul, 2020
Editor assigned
On 22 Jul, 2020
Reviewers invited
Invitations sent on 22 Jul, 2020
Reviewer #1 agreed
On 22 Jul, 2020
Reviewer #2 agreed
On 22 Jul, 2020
Submission checks complete
On 21 Jul, 2020
Editor invited
On 21 Jul, 2020
No comments provided
Editorial decision: Major revision
On 22 Jun, 2020
Review #2 received
Received 20 Jun, 2020
Review #1 received
Received 15 Jun, 2020
Reviewer #2 agreed
On 10 Jun, 2020
Reviewer #1 agreed
On 25 May, 2020
Reviewers invited
Invitations sent on 08 May, 2020
Editor assigned
On 27 Apr, 2020
Editor invited
On 26 Apr, 2020
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This preprint is under consideration at Plant Methods. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Qinghua Guo
Chinese Academy of Sciences
Corresponding Author
Zhenghui Liu
Nanjing Agricultural University
Corresponding Author
ORCiD: https://orcid.org/0000-0003-0590-3092
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Published
On 26 Aug, 2020
No community comments so far
Reviewers invited
Invitations sent on 17 Aug, 2020
Reviewer #1 agreed
On 17 Aug, 2020
Review #1 received
Received 17 Aug, 2020
Editorial decision: Accept
On 17 Aug, 2020
Editor assigned
On 13 Aug, 2020
Submission checks complete
On 12 Aug, 2020
Editor invited
On 12 Aug, 2020
Version 2
Posted 29 Jul, 2020
No comments provided
Editorial decision: Major revision
On 04 Aug, 2020
Review #1 received
Received 23 Jul, 2020
Review #2 received
Received 23 Jul, 2020
Editor assigned
On 22 Jul, 2020
Reviewers invited
Invitations sent on 22 Jul, 2020
Reviewer #1 agreed
On 22 Jul, 2020
Reviewer #2 agreed
On 22 Jul, 2020
Submission checks complete
On 21 Jul, 2020
Editor invited
On 21 Jul, 2020
No comments provided
Editorial decision: Major revision
On 22 Jun, 2020
Review #2 received
Received 20 Jun, 2020
Review #1 received
Received 15 Jun, 2020
Reviewer #2 agreed
On 10 Jun, 2020
Reviewer #1 agreed
On 25 May, 2020
Reviewers invited
Invitations sent on 08 May, 2020
Editor assigned
On 27 Apr, 2020
Editor invited
On 26 Apr, 2020
Submission checks complete
On 24 Apr, 2020
First submitted
On 23 Apr, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Plant Methods.
Background : Identification and characterization of new traits with a sound physiological foundation is essential for crop breeding and management. Deep learning has been widely used in image data analysis to explore spatial and temporal information on crop growth and development, thus strengthening the power of the identification of physiological traits. This study aims to develop a novel trait that indicates source and sink relation in japonica rice based on deep learning.
Results : We applied a deep learning approach to accurately segment leaf and panicle and subsequently developed the procedure of GvCrop to calculate the leaf to panicle ratio (LPR) of rice populations during grain filling. Images of the training dataset were captured in the field experiments, with large variations in camera shooting angle, the elevation angle and the azimuth angle of the sun, rice genotype, and plant phenological stages. Accurately labeled by manually annotating all the panicle and leaf regions, the resulting dataset were used to train FPN-Mask (Feature Pyramid Network Mask) models, consisting of a backbone network and a task-specific sub-network. The model with the highest accuracy is then selected to study the variations in LPR among 192 rice germplasms and among agronomical practices. Despite the challenging field conditions, FPN-Mask models achieved a high detection accuracy, with Pixel Accuracy being 0.99 for panicles and 0.98 for leaves. The calculated LPRs showed large spatial and temporal variations as well as genotypic differences.
Conclusion : Deep learning techniques can achieve high accuracy in simultaneously detecting panicle and leaf data from complex rice field images. The proposed FPN-Mask model is applicable for detecting and quantifying crop performance under field conditions. The newly identified trait of LPR should provide a high throughput protocol for breeders to select superior rice cultivars as well as for agronomists to precisely manage field crops that have a good balance of source and sink.
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