Stories from the University of Cambridge
CropQuant-3D
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Yulei Zhu[1], Gang Sun[1], Guohui Ding[1], Jie Zhou[1], Mingxing Wen[1],[2], Shichao Jin[1], Qiang Zhao[3], Joshua Colmer[4], Yanfeng Ding[1], Eric S. Ober[5], Ji Zhou[1],[5]
1 State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, 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 Zhenjiang Institute of Agricultural Science in Hill Area of Jiangsu Province, Jurong, China 3 National Center for Gene Research, CAS Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai, China 4 Earlham Institute, Norwich Research Park, Norwich, UK 5 Cambridge Crop Research, National Institute of Agricultural Botany (NIAB), Cambridge, UK
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2021
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Zhu Y, Sun G, Ding G, Zhou J, Wen M, Jin S, Zhao Q, Colmer J, Ding Y, Ober ES, Zhou J. 2021. Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural variation in wheat. Plant Physiology 187, 716-738.
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Biotechnology and Biological Sciences Research Council National Productivity Investment Fund Award, Norwich Research Park’s Biosciences Doctoral Training Partnership (UK), Chinese Academy of Sciences (China), Fundamental Research Funds for the Central Universities in China (China), Jiangsu Collaborative Innovation Center for Modern Crop Production (China), National Natural Science Foundation of China (China), Natural Science Foundation of Jiangsu Province (China), United Kingdom Research and Innovation Biotechnology and Biological Sciences Research Council Designing Future Wheat Strategic Programme (UK)
ABOUT THE OPEN-RESOURCE
Background
Light detection and ranging (LiDAR) is a remote sensing technique that sends out laser pulses and then measures the time it takes for the laser pulses to return. Using this technique, LiDAR collects 3D point clouds (the collections of 3D points) that capture the spatial information of objects or surfaces. In plant phenotyping, this technology can provide information on plant morphological and structural traits. Mobile, or backpack LiDAR, is advantageous for large-scale field experiments phenotyping as it is easy to transport and use. However, the widespread use of LiDAR-based phenotyping has been limited by the capacity to process and evaluate the massive datasets generated by this technique, especially in average computing powers. Prof Zhou and collaborators addressed this issue by developing CropQuant-3D, which processes large LiDAR-acquired 3D point cloud data and consists of original algorithms packaged into user-friendly graphical user interface (GUI) software (a user interface that uses mouse, icons, and menus) to output multiple 3D canopy traits.
Function
It is a large-scale phenotyping solution that combines a commercial backpack LiDAR device and the analytic CropQuant-3D software. The use of LiDAR can acquire millions of 3D points to represent spatial features of crops, and CropQuant-3D can extract meaningful canopy-level performance traits from large, complex point clouds.
Development process
CropQuant-3D was developed through a collaboration between NIAB, the Chinese Academy of Sciences (CAS), and Nanjing Agricultural University. Some 3D point clouds analysis algorithms were jointly developed and implemented in the UK and China, followed by in-field testing and improvement back in the two countries.
Comparison to other technologies
The CropQuant-3D open source solution shares its source code with the community, including not only access to the software, but also the data analysis approach that Zhou’s lab established for processing large-scale 3D point cloud datasets.
Although mobile LiDAR is more affordable than other large-scale phenotyping systems, Prof Zhou points out that the equipment is still relatively expensive.
IMPACT
The technology has been employed to screen the effect of a range of nitrogen treatments on crop growth and structural variation in wheat varieties, for breeding and agronomic purposes studies. The studies were performed both at NIAB (UK), and Nanjing Agricultural University (China). Recently, CropQuant has been licenced and commercialised by a leading Agri-Tech company in Far East Asia. Several academic and industrial research groups are using the platform, including Nebraska-Lincoln University (UK), Nanjing Agricultural University (China), NIAB (UK), and ZealQuest Ltd. (Shanghai; additional information can be found here).
GOING FORWARD - WHERE TO IN THE NEXT 3-5 YEARS?
Some colour- or spectral-related features in plants (e.g., senescence of the lower canopy or water deficit), were not intended to be addressed by CropQuant-3D software when it was created. Therefore, adjustments to how LiDAR is used are required to capture such traits. The next steps of the research will also further broaden the use of CropQuant-3D, enabling the algorithms created for wheat to be applied to dealing with biological issues in other crop species.
Prof Zhou is looking for technology developers and academic partners who are interested in 3D trait analysis (from plant level to population level), as well as interested in sharing and jointly benefiting the plant research community.
3D segmentation of apple trees using CropQuant-3D, pseudo colour applied to 3D points according to a height scale bar (0–250+ cm), indicating trees’ spatial features. © 2023, The Zhou Laboratory, licensed under CC-BY 4.0 (individual, open license).