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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On 26 Aug, 2020
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On 27 Apr, 2020
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On 24 Apr, 2020
On 23 Apr, 2020
On 26 Aug, 2020
Invitations sent on 17 Aug, 2020
On 17 Aug, 2020
Received 17 Aug, 2020
On 17 Aug, 2020
On 13 Aug, 2020
On 12 Aug, 2020
On 12 Aug, 2020
Posted 29 Jul, 2020
On 04 Aug, 2020
Received 23 Jul, 2020
Received 23 Jul, 2020
On 22 Jul, 2020
Invitations sent on 22 Jul, 2020
On 22 Jul, 2020
On 22 Jul, 2020
On 21 Jul, 2020
On 21 Jul, 2020
On 22 Jun, 2020
Received 20 Jun, 2020
Received 15 Jun, 2020
On 10 Jun, 2020
On 25 May, 2020
Invitations sent on 08 May, 2020
On 27 Apr, 2020
On 26 Apr, 2020
On 24 Apr, 2020
On 23 Apr, 2020
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.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
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