Bridging Time-series Image Phenotyping and Functional-Structural Plant Modeling to Predict Adventitious Root System Architecture.

IF 7.6 1区 农林科学 Q1 AGRONOMY
Plant Phenomics Pub Date : 2023-12-21 eCollection Date: 2023-01-01 DOI:10.34133/plantphenomics.0127
Sriram Parasurama, Darshi Banan, Kyungdahm Yun, Sharon Doty, Soo-Hyung Kim
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引用次数: 0

Abstract

Root system architecture (RSA) is an important measure of how plants navigate and interact with the soil environment. However, current methods in studying RSA must make tradeoffs between precision of data and proximity to natural conditions, with root growth in germination papers providing accessibility and high data resolution. Functional-structural plant models (FSPMs) can overcome this tradeoff, though parameterization and evaluation of FSPMs are traditionally based in manual measurements and visual comparison. Here, we applied a germination paper system to study the adventitious RSA and root phenology of Populus trichocarpa stem cuttings using time-series image-based phenotyping augmented by FSPM. We found a significant correlation between timing of root initiation and thermal time at cutting collection (P value = 0.0061, R2 = 0.875), but little correlation with RSA. We also present a use of RhizoVision [1] for automatically extracting FSPM parameters from time series images and evaluating FSPM simulations. A high accuracy of the parameterization was achieved in predicting 2D growth with a sensitivity rate of 83.5%. This accuracy was lost when predicting 3D growth with sensitivity rates of 38.5% to 48.7%, while overall accuracy varied with phenotyping methods. Despite this loss in accuracy, the new method is amenable to high throughput FSPM parameterization and bridges the gap between advances in time-series phenotyping and FSPMs.

连接时序图像表型和植物功能结构模型,预测不定根系统结构
根系结构(RSA)是衡量植物如何导航并与土壤环境相互作用的重要指标。然而,目前研究 RSA 的方法必须在数据精度和接近自然条件之间做出权衡,而发芽论文中的根系生长提供了可访问性和高数据分辨率。植物功能结构模型(FSPMs)可以克服这种取舍,但 FSPMs 的参数设置和评估传统上以人工测量和目测比较为基础。在这里,我们利用基于时间序列图像的表型技术,并辅以 FSPM,应用萌芽纸系统研究了毛白杨(Populus trichocarpa)茎插条的不定根 RSA 和根表型。我们发现根的萌发时间与扦插采集时的热时间之间存在明显的相关性(P 值 = 0.0061,R2 = 0.875),但与 RSA 的相关性很小。我们还介绍了利用 RhizoVision [1] 从时间序列图像中自动提取 FSPM 参数和评估 FSPM 模拟的方法。在预测二维生长时,参数化的准确性很高,灵敏度达到 83.5%。但在预测三维生长时,这一准确性有所下降,灵敏度为 38.5% 至 48.7%,而总体准确性则随表型方法的不同而变化。尽管精度有所下降,但这种新方法适合于高通量 FSPM 参数化,并缩小了时间序列表型和 FSPM 之间的差距。
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来源期刊
Plant Phenomics
Plant Phenomics Multiple-
CiteScore
8.60
自引率
9.20%
发文量
26
审稿时长
14 weeks
期刊介绍: Plant Phenomics is an Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and published by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. The mission of Plant Phenomics is to publish novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics. The scope of the journal covers the latest technologies in plant phenotyping for data acquisition, data management, data interpretation, modeling, and their practical applications for crop cultivation, plant breeding, forestry, horticulture, ecology, and other plant-related domains.
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