High throughput phenotyping of novel root traits for early stage root bulking in cassava using an Aeroponic imaging platform

热带块根作物木薯（Manihot esculenta）是全世界约8亿人的主食，占世界人口的十分之一以上。降低木薯作为工业作物潜力的一个主要障碍是其长而多变的生长周期。选择早期贮藏根膨胀（ESB）-通过吸收淀粉形成可食用的贮藏根-将大大提高小木薯种植者的生产力、效率和财务稳定性，并为更密集的商业生产方法打开大门。木薯的早期贮藏根膨胀（ESB）在需要增加可用土地产量的地方变得很重要，在半干旱地区，可以依靠ESB品种在一个雨季周期内成熟至关重要。ESB可以增加经济稳定性和小农的回报，并使木薯过渡到更密集的商业生产系统，在一年中的大部分时间为工厂提供原材料。为了满足这些需求，开发出能够在8个月内收获而不损失产量的ESB品种是迫切需要的。近年来，基于图像的植物表型分析引起了人们越来越多的兴趣，因为缺乏能够提供准确、定量的植物结构和功能数据的高通量自动化工具已被广泛认为是生产更高效作物的新瓶颈。然而，它通常需要昂贵的自动化图像采集硬件，以及复杂的软件，这些软件为有待开发并嵌入可靠工具中的具有挑战性的图像分析问题提供解决方案。在这个项目中，诺丁汉大学和哥伦比亚的国际热带农业中心（CIAT）的合作，我们建议开发低成本的生长和成像设施，以便在木薯根在气培环境中生长时对其进行自动成像。我们将开发并发布免费软件来自动分析这些图像，并在木薯根生长时提供测量结果。这将使诸如根的体积等性状能够被测量和监测。这可以用来记录植物对不同养分和水分水平的反应，也可以提供有关不同木薯品种对这些环境的反应的信息。整个成像系统的设计将使整个低收入和中等收入国家（LMICs）易于复制。该项目中的系统将用于识别LMICs中商业育种者和小规模木薯农民所需的ESB品种。

The tropical root crop cassava (Manihot esculenta) is a staple food for an estimated 800 million people worldwide: more than a tenth of the world's population. A major obstacle reducing cassava's industrial crop potential is its long and variable growth cycle. Selection for early storage root bulking (ESB) - the formation of edible storage roots via the uptake of starch - would both significantly increase the productivity, efficiency and financial stability of small cassava farmers and open the door to more intensive commercial production methods. Early storage root bulking (ESB) in cassava has become important where increased production on available land is necessary, and in semi-arid regions where ESB cultivars that can be relied upon to mature within one rainy cycle are crucial. ESB can increase both economic stability and returns for smallholder farmers and allow cassava's transition to more intensive commercial production systems that supply factories with raw materials during greater portions of the year. To meet these demands, development of ESB varieties that can be harvested in less than 8 months without loss of yield are urgently required.Image-based plant phenotyping has attracted rapidly-increasing interest in recent years, as the absence of high-throughput, automatic tools capable of providing accurate, quantitative data on plant structure and function has been widely recognised as the new bottleneck to the production of more efficient crops. However, it usually requires expensive, automated image acquisition hardware, and complex software that provides solutions to challenging image analysis problems to be developed and embedded in reliable tools.In this project, a partnership between the University of Nottingham and the International Center for Tropical Agriculture (CIAT) in Columbia, we propose to develop low-cost growth and imaging facilities to allow for automated imaging of cassava roots as they grow in an aeroponic environment. We will develop and release for free software to analyze these images automatically, and provide measurements for the cassava roots as they grow. This will enable traits such as the volume of the roots to be measured and monitored. This can be used to record plant response to different nutrient and water levels, and also to provide information regarding how different cultivars of cassava respond to these environments. The complete imaging system will be designed to allow easy replication throughout low and middle-income countries (LMICs).The system in this project will be used to identify the ESB cultivars needed by commercial breeders and small scale farmers of cassava in LMICs.