High throughput phenotyping of novel root traits for early stage root bulking in cassava using an Aeroponic imaging platform 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. The Global Challenges Research Fund (GCRF) supports cutting-edge research to address challenges faced by developing countries. The fund addresses the UN sustainable development goals. It aims to maximise the impact of research and innovation to improve lives and opportunity in the developing world. The fund addresses the UN sustainable development goals. It aims to maximise the impact of research and innovation to improve lives and opportunity in the developing world.