• EI
    • CSA
    • CABI
    • 卓越期刊
    • CA
    • Scopus
    • CSCD
    • 核心期刊

整株干物质量分配指数模型模拟冬小麦各器官形态参数

李世娟, 诸叶平, 张红英, 刘升平, 刘海龙, 杜鸣竹

李世娟, 诸叶平, 张红英, 刘升平, 刘海龙, 杜鸣竹. 整株干物质量分配指数模型模拟冬小麦各器官形态参数[J]. 农业工程学报, 2019, 35(9): 155-164. DOI: 10.11975/j.issn.1002-6819.2019.09.019
引用本文: 李世娟, 诸叶平, 张红英, 刘升平, 刘海龙, 杜鸣竹. 整株干物质量分配指数模型模拟冬小麦各器官形态参数[J]. 农业工程学报, 2019, 35(9): 155-164. DOI: 10.11975/j.issn.1002-6819.2019.09.019
Li Shijuan, Zhu Yeping, Zhang Hongying, Liu Shengping, Liu Hailong, Du Mingzhu. Simulating winter wheat geometrical parameters of each organ using whole plant dry matter weight distribution index model[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(9): 155-164. DOI: 10.11975/j.issn.1002-6819.2019.09.019
Citation: Li Shijuan, Zhu Yeping, Zhang Hongying, Liu Shengping, Liu Hailong, Du Mingzhu. Simulating winter wheat geometrical parameters of each organ using whole plant dry matter weight distribution index model[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(9): 155-164. DOI: 10.11975/j.issn.1002-6819.2019.09.019

整株干物质量分配指数模型模拟冬小麦各器官形态参数

基金项目: 国家重点研发计划项目(2016YFD0200600);国家重点研发计划课题(2016YFD0200601);中国农科院协同创新任务(CAAS-XTCX2016006);中国农科院信息所创新团队及基本科研业务费(JBYW-AII-2017-25)

Simulating winter wheat geometrical parameters of each organ using whole plant dry matter weight distribution index model

  • 摘要: 作物生长机理模型可以定量描述作物生长发育及其与环境因子的动态关系,具有通用性、动态性和预测性的特点,但基于生长机理模型模拟结果的作物虚拟技术与方法尚缺乏研究。针对基于生理过程的小麦功能模型与三维结构模型之间不能很好衔接的问题,该文开展越冬期后不同小麦品种的主茎干物质量在不同器官之间的分配研究,以有效积温和干物质量为连接纽带,构建小麦叶片、叶鞘、茎秆、穗各器官的几何特征模拟模型,并用独立数据进行了验证。结果显示:穗干物质量分配指数模拟效果最好,RRMSE值和EF值分别为6.58%和0.98;叶片、叶鞘和茎秆的分配指数模拟效果较好,RRMSE值分别为13.86%、10.83%和14.87%,EF值分别为0.98、0.97和0.91。麦穗形态参数模型和叶鞘长度模型具有非常好的模拟性能;叶片长度和最大叶宽模型、茎秆长度和直径模型具有较好的模拟性能;叶鞘形态参数模型对于叶鞘展开宽度的模拟效果一般,需要在后续研究中对拟合方程和模型参数进一步修正。该系列模型以干物质量为参数输入,能够生成小麦主茎三维形态模拟所需的各器官逐日几何特征参数,参数反映了品种特性、生长环境及气象因素对作物生长的影响,是一种实现小麦功能模型与结构模型实际结合的有效方法。
    Abstract: According to the data sources, two main research directions are found in crop virtual research. One is crop geometry simulation and visualization based on external morphological parameters. Without regarding the impact of external morphology and the management measures on the crop, this type of models are focusing on the authenticity of visual effects, generally having no biological significant. Another is the primary structure-function simulation model of crop morphological structure based on simple statistics. This type of primary functional-structure model considers environmental parameters, crop developing processes and a series of important growth characteristic parameters. But most of them are empirical models, in which considering the effects of certain environmental factors on plant growth, and assuming that other environmental factors are appropriate. Thus the modeling method is not closely integrated with wheat physiological processes. The model cannot reflect the impact of changes such as instant photosynthesis, water and fertilizer dynamics on the growth of crops, and thus cannot reflect the instantaneous changes of the virtual forms of crops. The crop growth mechanism model takes data related with soil, meteorological and species as parameters to simulate the dry matter, leaf area and water-fertilizer dynamics in soil-crop system day by day, which can quantitatively describe the dynamic relationship between crop growth and environmental factors. And it is versatile, dynamic and predictive. Researchers have paid more and more attention to the integration and fusion of crop growth mechanism model with morphological structure model. Aiming at the problem that the winter wheat (Triticum aestivum L.) functional model and the three-dimensional structural model can't be well connected, the distribution of the dry matter in different organs after the wintering period and the relationship between dry matter and morphological parameters are studied for 3 wheat varieties by setting the field experiment in this paper. Based on the effective accumulated temperature and dry matter, the dry matter distribution index model and the geometrical parameters simulation model of various organs such as wheat leaf, sheath, stem and ear were constructed, and then were verified by independent data. The results showed that the ear dry matter distribution index had the best simulation effect, with the RRMSE value and EF value of 6.58% and 0.98, respectively. The distribution index of leaf, leaf sheath and stem were well simulated with he RRMSE values of 13.86%, 10.83% and 14.87% respectively, and the EF values of 0.98, 0.97 and 0.91, respectively. The ear morphological parameter model and the sheath length model performed pretty good with the RRMSE values of 7.39%, 9.61% and 6.22% for ear length, width and thickness , and the EF values of 0.83, 0.94 and 0.92, respectively. The RRMSE and EF values of leaf sheath length were 8.62% and 0.81. The simulation for sheath expansion width of leaf sheath morphology parameter model had a general simulation effect, and need to be further corrected. This series of models might take dry matter simulated from the wheat growth model as input to generate daily geometrical parameters of each organ required for three-dimensional morphological simulation of wheat main stem.
  • [1] Paulus S, Behmann J, Mahlein A K, et al. Low-cost 3D systems: Suitable tools for plant phenotyping[J]. Sensors, 2014, 14(2): 3001-3018.
    [2] Gomez F E, Carvalho G, Shi F H, et al. High throughput phenotyping of morpho-anatomical stem properties using X-ray computed tomography in sorghum[J]. Plant Methods, 2018, 14(1): 59.
    [3] Thapa S, Zhu F Y, Walia H A, et al. Novel liDAR-based instrument for high-throughput, 3D measurement of morphological traits in maize and sorghum[J]. Sensors, 2018, 18: 1187. DOI: 10.3390/s18041187
    [4] Brocks S, Bendig J, Bareth G. Toward an automated low-cost three-dimensional crop surface monitoring system using oblique stereo imagery from consumer-grade smart cameras[J]. Journal of Applied Remote Sensing, 2016, 10(4): 046021. DOI: 10.1117/1.JRS.10.046021
    [5] 雷晓俊. 基于组件的小麦生长可视化技术[D].南京: 南京农业大学,2010.Lei Xiaojun. Component-based Visualization Technology of Wheat Growth[D]. Nanjing: Nanjing Agricultural University, 2010. (in Chinese with English abstract)
    [6] 雷晓俊,汤亮,张永会,等. 小麦麦穗形态几何模型构建与可视化[J]. 农业工程学报,2011,27(3):179-184.Lei Xiaojun, Tang Liang, Zhang Yonghui, et al. Geometric model and visualization of wheat spike[J].Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2011, 27(3): 179-184. (in Chinese with English abstract)
    [7] 谭子辉. 小麦植株形态建成的模拟模型研究[D]. 南京: 南京农业大学,2006.Tan Zihui. Studies on Simulation Model of Morphologial Development in Wheat Plant[D]. Nanjing: Nanjing Agricultural University, 2006. (in Chinese with English abstract)
    [8] Zhao C J, Wang J H, Wu H R, et al. Simulation models and deduction system for interspace description of wheat leaf shape[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2002, 18(5): 221-225.
    [9] 丁维龙,徐利锋,危扬. 水稻株型优化设计系统的设计与实现[J]. 系统仿真学报,2015,27(10):2467-2774.Ding Weilong, Xu Lifeng, Wei Yang. Design and realization of optimization system for rice type[J]. Journal of System Simulation, 2015, 27(10): 2467-2774. (in Chinese with English abstract)
    [10] 汤亮,雷晓俊,刘小军,等. 小麦群体生长状态实时绘制技术及实现[J]. 农业工程学报,2011,27(9):128-135.Tang Liang, Lei Xiaojun, Liu Xiaojun, et al. Real-time rendering of wheat population growth status and its realization[J]. Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE), 2011, 27(9): 128-135. (in Chinese with English abstract)
    [11] 温维亮,郭新宇,赵春江,等. 作物根系构型三维探测与重建方法研究进展[J]. 中国农业科学,2015,48(3):436-448.Wen Weiliang, Guo Xinyu, Zhao Chunjiang, et al. Crop roots configuration and visualization: A review[J]. Scientia Agricultura Sinica, 2015, 48(3): 436-448. (in Chinese with English abstract)
    [12] Landl M, Schnepf A, Vanderborght J, et al. Measuring root system traits of wheat in 2D images to parameterize 3D root architecture models[J]. Plant and Soil, 2018, 425(1/2): 457-477.
    [13] Clark R T, MacCurdy R B, Jung J K, et al. Three-dimensional root phenotyping with a novel imaging and software platform[J]. Plant Physiology, 2011, 156: 455-465.
    [14] 吴迪,杨万能,牛智有,等. 小麦分蘖形态学特征X射线-CT无损检测[J]. 农业工程学报,2017,33(14):196-201.Wu Di, Yang Wanneng, Niu Zhiyou, et al. Non-destructive detection of wheat tiller morphological traits based on X-ray CT technology[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2017, 33(14): 196-201. (in Chinese with English abstract)
    [15] Baret F, De Solan B, Lopez-Lozano R, et al. GAI estimates of row crops from downward looking digital photos taken perpendicular to rows at 57.5 zenith angle: Theoretical considerations based on 3D architecture models and application to wheat crops[J]. Agricultural and Forest Meteorology, 2010, 150(11): 1393-1401.
    [16] 郭焱,李保国. 玉米冠层的数学描述与三维重建研究[J]. 应用生态学报,1999,10(1):39-41.Guo Yan, Li Baoguo. Mathematical description and three-dimensional reconstruction of maize canopy[J]. Chinese Journal of Applied Ecology, 1999, 10(1): 39-41. (in Chinese with English abstract)
    [17] 李书钦,诸叶平,刘海龙,等. 基于有效积温的冬小麦返青后植株三维形态模拟[J]. 中国农业科学,2017,50(9):1594-1605.Li Shuqin, Zhu Yeping, Liu Hailong, et al. 3D shape simulation of winter wheat after turning green stage based on effective accumulated temperature[J]. Scientia Agricultura Sinica, 2017, 50(9): 1594-1605. (in Chinese with English abstract)
    [18] Gladstone E A, Dokoozlian N K. Influence of leaf area density and trellis/training system on the light microclimate within grapevine canopies[J]. VITIS-Journal of Grapevine Research, 2015, 42(3): 123.
    [19] Henke M, Kurth W, Buck-Sorlin G H. FSPM-P: Towards a general functional-structural plant model for robust and comprehensive model development[J]. Frontiers of Computer Science, 2016, 10(6): 1103-1117.
    [20] Zhu J, Dai Z, Vivin P, et al. A 3-D functional-structural grapevine model that couples the dynamics of water transport with leaf gas exchange[J]. Annals of Botany, 2018, 121(5): 833-848.
    [21] Baattrup-Pedersen A, Garssen A, Gothe E, et al. Structural and functional responses of plant communities to climate change-mediated alterations in the hydrology of riparian areas in temperate Europe[J]. Ecology and Evolution, 2018, 8(8): 4120-4135.
    [22] Vos J, Marcelis L F M, Evers J B. Functional-structural plant modelling in crop production: adding a dimension[C] // Vos J, Marcelis L F M, Visser P H B, et al. Functional- Structural Plant Modelling in Crop Production. Dordrecht: Springer, 2007: 1-12.
    [23] Garin G, Pradal C, Fournier C, et al. Modelling interaction dynamics between two foliar pathogens in wheat: A multiscale approach[J]. Annals of Botany, 2018, 121(5): 927-940.
    [24] Liu S Y, Baret F, Andrieu B, et al. Modeling the spatial distribution of plants on the row for wheat crops: Consequences on the green fraction at the canopy level[J]. Computer and Electronics in Agriculture, 2017, 136: 147-156.
    [25] 丁维龙,马培良,程志君. 基于结构-功能互反馈机制的植物生长可视化建模与仿真[J]. 农业工程学报,2008,24(11):165-168.Ding Weilong, Ma Peiliang, Cheng Zhijun. Visual modeling and simulation of plant growth based on plant functional-structural mutual feedback mechanism[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2008, 24(11): 165-168. (in Chinese with English abstract)
    [26] 石春林,朱艳,曹卫星. 水稻叶片几何参数的模拟分析[J]. 中国农业科学,2006,39(5):910-915.Shi Chunlin, Zhu Yan, Cao Weixing. A simulation analysis on geometrical parameters of rice leaf blade[J]. Scientia Agricultura Sinica, 2006, 39(5): 910-915. (in Chinese with English abstract)
    [27] Barillot R, Chambon C, Andrieu B. CN-Wheat, a functional-structural model of carbon and nitrogen metabolism in wheat culms after anthesis. I. Model description[J]. Annals of Botany, 2016, 118(5): 997-1013.
    [28] Barillot R, Chambon C, Andrieu B. CN-Wheat, a functional-structural model of carbon and nitrogen metabolism in wheat culms after anthesis. II. Model evaluation[J]. Annals of Botany, 2016, 118(5): 1015-1031.
    [29] Makdessia N A, Jeanb P A, Ecarnot M, et al. How plant structure impacts the biochemical leaf traits assessmentfrom in-field hyperspectral images: A simulation study based on light propagation modeling in 3D virtual wheat scenes[J]. Field Crops Research, 2017, 205: 95-105.
    [30] 宋有洪,郭焱,李保国,等. 基于器官生物量构建植株形态的玉米虚拟模型[J]. 生态学报,2003,23(12):2579-2586.Song Youhong, Guo Yan, Li Baoguo, et al. Virtual maize model, I. plant morphological constructing based on organ biomass accumulation[J]. Acta Ecologica Sinica, 2003, 23(12): 2579-2586. (in Chinese with English abstract)
    [31] 张伟欣,曹宏鑫,朱艳,等. 基于生物量的油菜越冬前植株叶片空间形态结构模型[J]. 作物学报,2015,41(2):318-328.Zhang Weixin, Cao Hongxin, Zhu Yan, et al. Morphological structure model of leaf space based on biomass at pre-overwintering stage in rapeseed (Brassica napus L.) plant[J]. Acta Agronomica Sinica, 2015, 41(2): 318-328. (in Chinese with English abstract)
    [32] 陈昱利,杨平,张文宇,等. 基于生物量的冬小麦穗部主要形态参数模型[J]. 作物学报,2017,43(3):399-406.Chen Yuli, Yang Ping, Zhang Wenyu, et al. Biomass-based main spike morphological parameter model for winter wheat[J]. Acta Agronomica Sinica, 2017, 43(3): 399-406. (in Chinese with English abstract)
    [33] 陈昱利,杨平,张文宇,等. 基于生物量的冬小麦越冬前植株地上部形态结构模型[J]. 作物学报,2016,42(5):743-750.Chen Yuli, Yang Ping, Zhang Wenyu, et al. Aboveground architecture model based on biomass of winter wheat before overwintering[J]. Acta Agronomica Sinica, 2016, 42(5): 743-750. (in Chinese with English abstract)
    [34] 刘自华. 冬小麦叶面积矫正系数及叶面积指数的研究[J]. 河北农业科学,1996,1:12-14.Liu Zihua. Study on winter wheat leaf area compensation coefficient and LAI[J]. Journal of Hebei Agricultural Sciences, 1996, 1: 12-14. (in Chinese with English abstract)
    [35] Marcelis L F M, Heuvelink E, Goudriaan J. Modelling biomass production and yield of horticultural crops: A review[J]. Scientia Horticulturae, 1998, 74(1): 83-111.
    [36] Abichou M, Fournier C, Dornbusch T, et al. Parameterising wheat leaf and tiller dynamics for faithful reconstruction of wheat plants by structural plant models[J]. Field Crops Research, 2018, 218: 213-230.
    [37] 苗腾,郭新宇,温维亮,等. 基于农学参数的玉米叶片表观建模与可视化方法[J]. 农业工程学报,2017,33(19):187-195.Miao Teng, Guo Xinyu, Wen Weiliang, et al. Appearance modeling and visualization of maize leaf with agronomic parameters[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2017, 33(19): 187-195. (in Chinese with English abstract)
    [38] De Reffye P, Houllier F. Modelling plant growth and architecture: Some recent advances and applications to agronomy and forestry[J]. Current Science, 1997, 73(11): 984-992.
  • 期刊类型引用(10)

    1. 董建舒,申孝军,衣若晨,李强,苗昊翠,侯献飞,陈军伟,薛铸. 花生叶面积指数精准快速监测方法研究. 节水灌溉. 2024(02): 88-94+104 . 百度学术
    2. 王泽鹏,梁志国,范凤翠,杜凤焕,刘胜尧,贾宋楠,赵楠,张哲,秦勇,郭文忠. 基于辐热积的日光温室嫁接茄子养分积累模型. 中国蔬菜. 2023(02): 83-90 . 百度学术
    3. 薛惠芬,于晓池,付鹏跃,肖遥,刘冰洋,杨桂娟,王军辉,赵曦阳,麻文俊. 黄心梓木优良无性系评价与初选. 西北林学院学报. 2022(02): 108-114 . 百度学术
    4. 王晓婷,赵展,王阳,李林. 基于改进Mask R-CNN的植物表型智能检测算法. 中国农机化学报. 2022(08): 151-157 . 百度学术
    5. 杨凡,张吴平,郑小南,刘宇平,梁靓,李富忠. 基于有效积温的谷子生长模型构建. 湖北农业科学. 2021(05): 18-20+24 . 百度学术
    6. 倪纪恒,王媛媛,刘勇,毛罕平. 基于蔗糖产量时域变化的温室番茄光合作用的模拟与验证. 农业工程学报. 2021(08): 223-228 . 本站查看
    7. 黄语燕,王涛,廖水兰,钟陈声,陈永快. 基于有效积温的NFT栽培生菜生长模型. 北方园艺. 2021(14): 39-45 . 百度学术
    8. 姚程程,王俊臣,胡继文,肖遥,杨桂娟,王军辉,翟文继,麻文俊. 香椿种质生长及叶部表型性状的遗传变异分析. 植物科学学报. 2020(01): 112-122 . 百度学术
    9. 杨红云,路艳,孙爱珍,杨乐. 水稻叶片几何参数无损测量方法研究. 江西农业大学学报. 2020(02): 407-418 . 百度学术
    10. 陈永快,黄语燕,王涛,廖水兰,钟陈声,赵健. 基于有效积温的NFT栽培小白菜生长模型. 江苏农业科学. 2020(17): 229-233 . 百度学术

    其他类型引用(3)

计量
  • 文章访问数:  1189
  • HTML全文浏览量:  0
  • PDF下载量:  400
  • 被引次数: 13
出版历程
  • 收稿日期:  2018-10-10
  • 修回日期:  2019-03-29
  • 发布日期:  2019-04-30

目录

    /

    返回文章
    返回