Background Recent advances in image-based plant phenotyping have improved our capability to study vegetative stage growth dynamics. However, more complex agronomic traits such as inflorescence architecture (IA), which predominantly contributes to grain crop yield are more challenging to quantify and hence are relatively less explored. Previous efforts to estimate inflorescence-related traits using image-based phenotyping have been limited to destructive end-point measurements. Development of non-destructive inflorescence phenotyping platforms could accelerate the discovery of the phenotypic variation with respect to inflorescence dynamics and mapping of the underlying genes regulating critical yield components. Results The major objective of this study is to evaluate post-fertilization development and growth dynamics of inflorescence at high spatial and temporal resolution in rice. For this, we developed the Panicle Imaging Platform (PI-Plat) to comprehend multi-dimensional features of IA in a non-destructive manner. We used 11 rice genotypes to capture multi-view images of primary panicle on weekly basis after the fertilization. These images were used to reconstruct a 3D point cloud of the panicle, which enabled us to extract digital traits such as voxel count and color intensity. We found that the voxel count of developing panicles is positively correlated with seed number and weight at maturity. The voxel count from developing panicles projected overall volumes that increased during the grain filling phase, wherein quantification of color intensity estimated the rate of panicle maturation. Our 3D based phenotyping solution showed superior performance compared to conventional 2D based approaches. Conclusions For harnessing the potential of the existing genetic resources, we need a comprehensive understanding of the genotype-to-phenotype relationship. Relatively low-cost sequencing platforms have facilitated high-throughput genotyping, while phenotyping, especially for complex traits, has posed major challenges for crop improvement. PI-Plat offers a low cost and high-resolution platform to phenotype inflorescence-related traits using 3D reconstruction-based approach. Further, the non-destructive nature of the platform facilitates analyses of the same panicle at multiple developmental time points, which can be utilized to explore the genetic variation for dynamic inflorescence traits in cereals.

中文翻译：

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PI-Plat：一种基于高分辨率图像的 3D 重建方法，用于估计水稻花序性状的生长动态。

背景 基于图像的植物表型分析的最新进展提高了我们研究营养阶段生长动态的能力。然而，更复杂的农艺性状，例如主要影响粮食作物产量的花序结构（IA），量化起来更具挑战性，因此研究相对较少。以前使用基于图像的表型分析来估计花序相关性状的努力仅限于破坏性终点测量。开发非破坏性花序表型平台可以加速发现花序动态方面的表型变异以及调节关键产量成分的潜在基因图谱。结果本研究的主要目的是在高时空分辨率下评估水稻受精后的发育和花序的生长动态。为此，我们开发了Panicle Imaging Platform (PI-Plat)，以非破坏性的方式理解IA的多维特征。我们使用 11 种水稻基因型在受精后每周捕获初生穗的多视图图像。这些图像用于重建圆锥花序的 3D 点云，这使我们能够提取体素计数和颜色强度等数字特征。我们发现，发育中的圆锥花序的体素计数与成熟时的种子数量和重量呈正相关。来自发育中的圆锥花序的体素计数预测了在籽粒灌浆阶段增加的总体积，其中颜色强度的量化估计了圆锥花序的成熟速率。与传统的基于 2D 的方法相比，我们基于 3D 的表型分析解决方案表现出卓越的性能。结论 为了充分利用现有遗传资源的潜力，我们需要全面了解基因型与表型的关系。相对低成本的测序平台促进了高通量基因分型，而表型分析，尤其是复杂性状的表型分析，给作物改良带来了重大挑战。PI-Plat 提供了一个低成本、高分辨率的平台，可使用基于 3D 重建的方法对花序相关性状进行表型分析。此外，该平台的非破坏性有助于在多个发育时间点对同一穗进行分析，可用于探索谷物动态花序性状的遗传变异。