Falk Kevin G, Jubery Talukder Z, Mirnezami Seyed V, Parmley Kyle A, Sarkar Soumik, Singh Arti, Ganapathysubramanian Baskar, Singh Asheesh K
1Department of Agronomy, Iowa State University, Ames, USA.
2Department of Mechanical Engineering, Iowa State University, Ames, USA.
Plant Methods. 2020 Jan 23;16:5. doi: 10.1186/s13007-019-0550-5. eCollection 2020.
Root system architecture (RSA) traits are of interest for breeding selection; however, measurement of these traits is difficult, resource intensive, and results in large variability. The advent of computer vision and machine learning (ML) enabled trait extraction and measurement has renewed interest in utilizing RSA traits for genetic enhancement to develop more robust and resilient crop cultivars. We developed a mobile, low-cost, and high-resolution root phenotyping system composed of an imaging platform with computer vision and ML based segmentation approach to establish a seamless end-to-end pipeline - from obtaining large quantities of root samples through image based trait processing and analysis.
This high throughput phenotyping system, which has the capacity to handle hundreds to thousands of plants, integrates time series image capture coupled with automated image processing that uses optical character recognition (OCR) to identify seedlings via barcode, followed by robust segmentation integrating convolutional auto-encoder (CAE) method prior to feature extraction. The pipeline includes an updated and customized version of the Automatic Root Imaging Analysis (ARIA) root phenotyping software. Using this system, we studied diverse soybean accessions from a wide geographical distribution and report genetic variability for RSA traits, including root shape, length, number, mass, and angle.
This system provides a high-throughput, cost effective, non-destructive methodology that delivers biologically relevant time-series data on root growth and development for phenomics, genomics, and plant breeding applications. This phenotyping platform is designed to quantify root traits and rank genotypes in a common environment thereby serving as a selection tool for use in plant breeding. Root phenotyping platforms and image based phenotyping are essential to mirror the current focus on shoot phenotyping in breeding efforts.
根系结构（RSA）性状是育种选择中所关注的；然而，测量这些性状难度大、资源消耗多且结果变异性大。计算机视觉和机器学习（ML）实现的性状提取和测量技术的出现，重新激发了人们利用RSA性状进行遗传改良以培育更健壮、更具韧性的作物品种的兴趣。我们开发了一种移动、低成本且高分辨率的根系表型分析系统，该系统由一个成像平台和基于计算机视觉及ML的分割方法组成，以建立一个无缝的端到端流程——从获取大量根系样本到基于图像的性状处理与分析。
这个高通量表型分析系统有能力处理数百到数千株植物，它集成了时间序列图像捕捉以及自动化图像处理，该处理利用光学字符识别（OCR）通过条形码识别幼苗，随后在特征提取之前采用集成卷积自动编码器（CAE）方法进行稳健分割。该流程包括自动根系成像分析（ARIA）根系表型分析软件的更新定制版本。利用这个系统，我们研究了来自广泛地理分布的不同大豆种质，并报告了RSA性状的遗传变异性，包括根的形状、长度、数量、质量和角度。
该系统提供了一种高通量、经济高效且无损的方法，可提供有关根系生长和发育的生物学相关时间序列数据，用于表型组学、基因组学和植物育种应用。这个表型分析平台旨在在共同环境中量化根系性状并对基因型进行排名，从而作为植物育种中的选择工具。根系表型分析平台和基于图像的表型分析对于反映当前育种工作中对地上部表型分析的关注至关重要。
