High-throughput phenotyping and deep learning to analyze dynamic panicle growth and dissect the genetic architecture of yield formation

Abstract

The dynamic growth of shoots and panicles determines the final agronomic traits and yield. However, it is difficult to quantify such dynamics manually for large populations. In this study, based on the high-throughput rice automatic phenotyping platform and deep learning, we developed a novel image analysis pipeline (Panicle-iAnalyzer) to extract image-based traits (i-traits) including 52 panicle and 35 shoot i-traits and tested the system using a recombinant inbred line population derived from a cross between Zhenshan 97 and Minghui 63. At the maturity stage, image recognition using a deep learning network (SegFormer) was applied to separate the panicles from the shoot in the image. Eventually, with these obtained i-traits, the yield could be well predicted, and the R² was 0.862. Quantitative trait loci (QTL) mapping was performed using an extra-high density single nucleotide polymorphism (SNP) bin map. A total of 3,586 time-specific QTLs were identified for the traits and parameters at various time points. Many of the QTLs were repeatedly detected at different time points. We identified the presence of cloned genes, such as TAC1, Ghd7.1, Ghd7, and Hd1, at QTL hotspots and evaluated the magnitude of their effects at different developmental stages. Additionally, this study identified numerous new QTL loci worthy of further investigation.

Keywords

Deep learning / Growth prediction / High-throughput phenotyping / Panicle growth / QTL / Rice

Abbreviations

DAH: day after heading

DAS: day after sowing

GPAR: the ratio of green projected area

IOU: intersection over union

MLP: multilayer perceptual

PS: panicle stage

QTL: quantitative trait loci

RI: recombinant inbred

SNP: single nucleotide polymorphism

TPA: the projected area of the panicle

UAV: unmanned aerial vehicle

YTS: the yield traits scorer

Availability of data and materials

All crop images (including 343,440 side views and 22,896 top views of 212 lines), i-traits, and genotype data are publicly available via the following link: (http://plantphenomics.hzau.edu.cn/usercrop/Rice/image/2014-Yunrui%20Lu). We also glad to provide reasonable help involving our original images and data if the corresponding author is contacted.

Authors' contributions

Z.G. and Y.L. performed the experiments, analyzed the data, and wrote the manuscript; L.D., H.C, Z.W., J.Z., and X.W. also performed experiments or analyzed the data; Z.L. and R.Z. helped design the executable program; Y.O. provided the materials and supported data; and W.Y. designed the research, analyzed the data, and wrote the manuscript.

Declaration of competing interest

The authors declare no competing interests.

Acknowledgements

This work was supported by the National Key Research and Development Plan (2022YFD2002304), National Natural Science Foundation of China (U21A20205), Key Projects of Natural Science Foundation of Hubei Province (2021CFA059), Major Science and Technology Project of Hubei Province (2021AFB002), and Fundamental Research Funds for the Central Universities (2021ZKPY006, 2662022JC006). We thank Prof. Shizhong Xu (University of California, USA) for his help in quantitative genetic analysis. We thank Dr. Weijuan Hu (IGDB, CAS, China) and Prof. John H. Doonan (Aberystwyth University, UK) for providing part of the pot-grown rice and wheat images. We thank Prof. Qifa Zhang (Huazhong Agricultural University, China) for his constructive suggestions on this work.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.crope.2023.10.005.

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