Leaf to panicle ratio (LPR): a new physiological trait indicative of source and sink relation in japonica rice based on deep learning

Abstract

Background: Identification and characterization of new traits with sound physiological foundation is essential for crop breeding and production 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 identification of physiological traits. Taking the advantage of deep learning, this study aims to develop a novel trait of canopy structure that integrate source and sink in japonica rice. 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 canopy during grain filling stage. Images of training dataset were captured in the field experiments, with large variations in camera shooting angle, the elevation and the azimuth angles of the sun, rice genotype, and plant phenological stages. Accurately labeled by manually annotating 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 was then selected to check 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 LPR displayed large spatial and temporal variations as well as genotypic differences. In addition, it was responsive to agronomical practices such as nitrogen fertilization and spraying of plant growth regulators. Conclusion: Deep learning technique can achieve high accuracy in simultaneous detection of panicle and leaf data from complex rice field images. The proposed FPN-Mask model is applicable to detect and quantify 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.

Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives

“…PRS enables the proposition of novel biologically meaningful traits. Using RGB cameras and deep learning, Yang et al. (2020b) proposedLPR as a novel phenotypic trait indicative of source-sink relationships, revealing unique canopy light interception patterns of ideal-plant-architecture varieties from a solar perspective.…”

Field rice panicle detection and counting based on deep learning

“…Ma et al (2020) proposed EarSegNet based on semantic segmentation for winter wheat ears segmentation and the F1 score was 87.25%. Yang et al (2020) used FPN-Mask model to segment panicles during grain filling stage and the pixel accuracy was 0.99. Misra et al (2020) proposed SpikeSegNet for wheat spike detection and counting and the average accuracy for spike counting was 95%.…”

Image-Based Phenotyping for Non-Destructive In Situ Rice (Oryza sativa L.) Tiller Counting Using Proximal Sensing

“…To date, 90% of the accuracy in rice-panicle counting can be expected using region-based fully convolutional networks (R-FCN) [ 6 ] and 95% by segmenting panicles, leaves, and background. However, it requires more sophisticated analysis than merely estimating panicle numbers [ 7 ].…”