AirMeasurer

ABOUT THE OPEN-RESOURCE

Background 

Unmanned aerial vehicles (UAV) such as drones, have become more popular for field-based plant phenotyping in the last few years due to their portability, adaptability, and lower cost compared to other phenotyping approaches. Most of the studies using UAV focus on collecting data at a specific time, often miss the dynamic nature of plant growth and development. Another limitation in most of the studies is related to how to extract meaningful information from the images collected with UAV. Thinking about how to acquire high-quality data at key plant growth stages, Prof Ji Zhou and collaborators developed AirMeasurer.  

Function

AirMeasurer is an open source and expandable platform that combines automated image analysis, machine learning and dynamic phenotyping algorithms to perform trait analysis using 2D/3D aerial imagery acquired by low-cost drones. It generates a range of static and dynamic traits, including crop height, canopy coverage, vegetative indices, and their growth rates, which can be used to screen varieties, identify genetic variants associated with target traits using genetic mapping, and explore new candidate genes that are 
key to crop improvement.

Development process

Similar to CropQuant-3D, AirMeasurer was developed through a collaboration between NIAB, the Chinese Academy of Sciences (CAS), and Nanjing Agricultural University. The researchers performed a range of field experiments in different locations in China and in the UK, to make sure that the results obtained were reproducible in different environments. During the tests, the group was able to access possible limitations in the platform, including the fact that UAVs cannot be operated in unstable weather (such as high or gusty wind, rainfall, or heavy fog); and how to try to mitigate image colour variation due to changes in light conditions.

Comparison to other technologies

The main advantage that AirMeasurer brings compared to other technologies available is the dynamic phenotypic analysis, producing static and dynamic traits using fitted-growth-profiles that were previously difficult to generate under complex field conditions.

IMPACT

Current use

The platform has been applied to studies in sites located in both China and in the UK, covering over 5,000 rice/wheat genotypes from 2018 to present. Recently, it has been employed in Bayer Crop Sciences’ (US) global G4T programme (additional information can be found here), and the UK based company RAGT seeds (additional information can be found here) started to use the AirMeasurer  platform to process thousands of wheat yield plots to quantify performance traits that were highly correlated with yield production and hence, the yield prediction in the field. CGIAR (additional information can be found here), a leader in global research partnership for food-security, will trial the platform in their programmes too. 

GOING FORWARD - WHERE TO IN THE NEXT 3-5 YEARS?

Even though AirMeasurer has already shown its valuable advance in dynamic aerial phenotyping, Prof Zhou explains that there is potential to explore further applications of dynamic phenotyping (e.g., timing and duration of phenotypic changes) in genetic mapping and molecular marker identification. Therefore, it will help scientists to gain insights into the genetic makeup of the studied crops, to understand the inheritance of traits, and potentially to discover genes responsible for certain desirable traits. Further developments that could be included in AirMeasurer are the analysis of more specific traits, difficult to analyse by conventional phenotyping approaches. For example, it could be applied for selecting specific flowering characteristics, and grain-filling timing. The developers also see a big potential to use AirMeasurer not just in rice, but in other crops such as wheat.

Authors and affiliations

Gang Sun[1], Hengyun Lu[2], Yan Zhao[2], Jie Zhou[1], Robert Jackson[3], Yongchun Wang[2], Ling-xiang Xu1, Ahong Wang[2], Joshua Colmer[4], Eric Ober[3], Qiang Zhao[2], Bin Han[2], Ji Zhou[1],[3]

1 State Key Laboratory of Crop Genetics and Germplasm Enhancement, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing, China 
2 National Center for Gene Research, CAS Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, China 
3 Cambridge Crop Research, National Institute of Agricultural Botany (NIAB), Cambridge, UK 
4 Earlham Institute, Norwich Research Park, Norwich, UK