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Smart Agriculture ›› 2022, Vol. 4 ›› Issue (2): 150-162.doi: 10.12133/j.smartag.SA202203009

• 信息感知与获取 • 上一篇    下一篇

基于三维数字化的小麦植株表型参数提取方法

郑晨曦1,2,3(), 温维亮1,2(), 卢宪菊1,2, 郭新宇1,2, 赵春江1,2,3   

  1. 1.北京市农林科学院信息技术研究中心,北京 100097
    2.国家农业信息化工程技术研究中心数字植物北京市重点实验室,北京 100097
    3.西北农林科技大学 信息工程学院,陕西杨凌 712100
  • 收稿日期:2022-03-14 出版日期:2022-06-30
  • 基金资助:
    北京市农林科学院协同创新中心建设专项(KJCX201917);财政部和农业农村部国家现代农业产业技术体系项目(CARS-03);北京市农林科学院科研创新平台建设(PT2022-31)
  • 作者简介:郑晨曦(1997-),女,硕士研究生,研究方向为植物三维表型解析。E-mail:cissie_zheng@163.com
  • 通信作者: 温维亮(1983-),男,博士,副研究员,研究方向为植物三维表型高通量获取与自动解析、植物三维重建与可视化计算。E-mail:wenwl@nercita.org.cn

Phenotypic Traits Extraction of Wheat Plants Using 3D Digitization

ZHENG Chenxi1,2,3(), WEN Weiliang1,2(), LU Xianju1,2, GUO Xinyu1,2, ZHAO Chunjiang1,2,3   

  1. 1.Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
    2.Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China
    3.College of Information Engineering, Northwest A&F University, Yangling, 712100, China
  • Received:2022-03-14 Online:2022-06-30
  • Supported by:
    Beijing Academy of Agriculture and Forestry Sciences Collaborative Innovation Center Project (KJCX201917); National Modern Agricultural Industrial Technology System Project of the Ministry of Finance and the Ministry of Agriculture and Rural Affairs (CARS-03); Research Innovation Platform Construction of Beijing Academy of Agriculture and Forestry Sciences (PT2022-31)

摘要:

针对小麦植株分蘖多、器官间交叉遮挡严重,难以用图像或点云准确提取植株和器官表型的问题,本研究提出了基于三维数字化的小麦植株表型参数提取方法。首先提出了小麦植株各器官数字化表达方法,制定了适用于小麦全生育期的三维数字化数据获取规范,并依据该规范进行数据获取。根据三维数字化数据的空间位置语义信息和表型参数的定义,提出了小麦植株表型参数计算方法,实现了小麦植株和器官长度、粗度和角度等3类共11个常规可测表型参数的计算。进一步提出了定量描述小麦株型和叶形的表型指标。其中,植株围度通过基于最小二乘法拟合三维离散坐标计算,用于定量化描述小麦植株松散/紧凑程度;小麦叶片卷曲和扭曲程度为定量化叶形的指标,根据叶面向量方向变化计算得到。利用丰抗13号、西农979号和济麦44号三个品种小麦起身期、拔节期、抽穗期三个时期的人工测量值和提取值进行验证。结果表明,在保持植株原始三维形态结构的前提下,提取的茎长、叶长、茎粗、茎叶夹角与实测数据精度相对较高,R2 分别为0.93、0.98、0.93、0.85;叶宽和叶倾角与实测数据的R2 分别为0.75、0.73。本方法能便捷、精确地提取小麦植株和器官形态结构表型参数,为小麦表型相关研究提供了有效技术支撑。

关键词: 小麦, 三维数字化, 可视化, 表型参数提取

Abstract:

Aiming at the difficulty of accurately extract the phenotypic traits of plants and organs from images or point clouds caused by the multiple tillers and serious cross-occlusion among organs of wheat plants, to meet the needs of accurate phenotypic analysis of wheat plants, three-dimensional (3D) digitization was used to extract phenotypic parameters of wheat plants. Firstly, digital representation method of wheat organs was given and a 3D digital data acquisition standard suitable for the whole growth period of wheat was formulated. According to this standard, data acquisition was carried out using a 3D digitizer. Based on the definition of phenotypic parameters and semantic coordinates information contained in the 3D digitizing data, eleven conventional measurable phenotypic parameters in three categories were quantitative extracted, including lengths, thicknesses, and angles of wheat plants and organs. Furthermore, two types of new parameters for shoot architecture and 3D leaf shape were defined. Plant girth was defined to quantitatively describe the looseness or compactness by fitting 3D discrete coordinates based on the least square method. For leaf shape, wheat leaf curling and twisting were defined and quantified according to the direction change of leaf surface normal vector. Three wheat cultivars including FK13, XN979, and JM44 at three stages (rising stage, jointing stage, and heading stage) were used for method validation. The Open3D library was used to process and visualize wheat plant data. Visualization results showed that the acquired 3D digitization data of maize plants were realistic, and the data acquisition approach was capable to present morphological differences among different cultivars and growth stages. Validation results showed that the errors of stem length, leaf length, stem thickness, stem and leaf angle were relatively small. The R2 were 0.93, 0.98, 0.93, and 0.85, respectively. The error of the leaf width and leaf inclination angle were also satisfactory, the R2 were 0.75 and 0.73. Because wheat leaves are narrow and easy to curl, and some of the leaves have a large degree of bending, the error of leaf width and leaf angle were relatively larger than other parameters. The data acquisition procedure was rather time-consuming, while the data processing was quite efficient. It took around 133 ms to extract all mentioned parameters for a wheat plant containing 7 tillers and total 27 leaves. The proposed method could achieve convenient and accurate extraction of wheat phenotypes at individual plant and organ levels, and provide technical support for wheat shoot architecture related research.

Key words: wheat, three-dimensional digitization, visualization, phenotypic traits extraction

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