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Journal of Integrative Agriculture  2022, Vol. 21 Issue (6): 1606-1619    DOI: 10.1016/S2095-3119(20)63571-7
Special Issue: 玉米耕作栽培合辑Maize Physiology · Biochemistry · Cultivation · Tillage 油料作物合辑Oil Crops
Crop Science Advanced Online Publication | Current Issue | Archive | Adv Search |
Image-based root phenotyping for field-grown crops: An example under maize/soybean intercropping
HUI Fang1, XIE Zi-wen1, LI Hai-gang2, GUO Yan1, LI Bao-guo1, LIU Yun-ling1, MA Yun-tao1
1 College of Land Science and Technology, China Agricultural University, Beijing 100193, P.R.China
2 College of Grassland, Resource and Environment, Inner Mongolia Agricultural University, Hohhot 010011, P.R.China
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本文构建了一种基于图像的半自动的大田作物根系表型分析方法,包括图像采集、图像去噪与分割、特征提取和数据分析四个模块,能够提取5个全局特征和40个局部特征。通过对比人类统计的一级侧根分支数和本文构建的方法提取的结果,发现二者之间具有较好的一致性,R2高达0.97。在玉米/大豆间作系统中,基于该方法提取的根系表型特征参数,进一步发现玉米的种间优势主要表现在5-7轮节根基部5cm内,而间作系统对大豆的明显抑制作用主要体现在主根基部20 cm范围内。因此,本文为大田根系形态和拓扑表型特征的研究提供了一种高通量和高精度的新方法,可以潜在的应用于大田根系三维结构的重建,以及根系生长、溶质运输和水分吸收的模型模拟(例如OpenSimRoot)

Root architecture, which determines the water and nutrient uptake ability of crops, is highly plastic in response to soil environmental changes and different cultivation patterns.  Root phenotyping for field-grown crops, especially topological trait extraction, is rarely performed.  In this study, an image-based semi-automatic root phenotyping method for field-grown crops was developed.  The method consisted of image acquisition, image denoising and segmentation, trait extraction and data analysis.  Five global traits and 40 local traits were extracted with this method.  A good consistency in 1st-order lateral root branching was observed between the visually counted values and the values extracted using the developed method, with R2=0.97.  Using the method, we found that the interspecific advantages for maize mainly occurred within 5 cm from the root base in the nodal roots of the 5th–7th nodes, and that the obvious inhibition of soybean was mostly reflected within 20 cm from the root base.  Our study provides a novel approach with high-throughput and high-accuracy for field research on root morphology and branching features.  It could be applied to the 3D reconstruction of field-grown root system architecture to improve the inputs to data-driven models (e.g., OpenSimRoot) that simulate root growth, solute transport and water uptake.
Keywords:  root phenotyping        high-throughput        image analysis        intercropping, maize (Zea mays L.)        soybean (Glycine max L.)  
Received: 16 July 2020   Accepted: 30 November 2020
Fund: This work was supported by the National Key Research and Development Program of China (2016YFD0300202), the Science and Technology Project of Yunna, China (2017YN07) and the Science and Technology Major Project of Inner Mongolia, China (2019ZD024 and 2020GG0038).
About author:  Correspondence MA Yun-tao, E-mail:

Cite this article: 

HUI Fang, XIE Zi-wen, LI Hai-gang, GUO Yan, LI Bao-guo, LIU Yun-ling, MA Yun-tao. 2022. Image-based root phenotyping for field-grown crops: An example under maize/soybean intercropping. Journal of Integrative Agriculture, 21(6): 1606-1619.

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