Research Review on Crop Digital Twin System for Monitoring Growth Status and Environmental Response

doi:10.13304/j.nykjdb.2021.0235

Abstract

Abstract:

At the digital and intelligent stage of agricultural production， people need to comprehensively and accurately understand crop growth status and farmland environment in real-time， and make corresponding analysis， feedback， and decision through relevant information. Aiming at this problem， this paper drew on the concept of the industrial digital twin system and summarized the interactive system between the synchronization of actual crop growth and simulated growth， the real-time status of crops， and the real-time management strategy of crops as a crop digital twin system. Based on the research at home and abroad， this paper pointed out that the crop digital twin system included the stages of acquiring and transmitting data， building models， and visualization interaction； its key technologies included sensor technology， image segmentation technology， modeling technology， and visualization technology. In addition， this paper considered that the research in this field could be carried out from the aspects of basic crop data， system model capabilities， and multi-platform collaborative interaction. The related research on crop digital twin system for monitoring growth status and environmental response had greater practical significance for intelligent and digital agricultural production， and also had greater application value in theory and application.

Key words: digital twin, growth monitoring, virtual crop, smart agriculture

摘要：

在农业生产数字化和智慧化阶段，需实时、全面、准确地了解作物生长状态和农田环境，并根据相关信息做出相应的分析、反馈、决策。针对这一问题，借鉴工业数字孪生系统的概念，将作物实际生长与模拟生长的同步、作物实时状态与作物实时管理策略之间的交互系统概括为作物数字孪生系统。在梳理国内外研究现状的基础上，指出作物数字孪生系统包含数据获取与传输、模型构建、可视化交互等阶段；其关键技术包括传感器技术、图像分割技术、建模技术以及可视化技术等。同时指出该领域的研究可以从作物基础数据、系统模型能力、多方协同交互等方面进行。监测生长状态和环境响应的作物数字孪生系统的相关研究对农业生产的智慧化和数字化有较大的现实意义，从理论和应用层面也有较大的价值。

关键词: 数字孪生, 生长监测, 虚拟作物, 智慧农业

CLC Number:

S126

Wei LI, Deli ZHU, Qing WANG, Shaohua ZENG. Research Review on Crop Digital Twin System for Monitoring Growth Status and Environmental Response[J]. Journal of Agricultural Science and Technology, 2022, 24(6): 90-105.

李炜, 朱德利, 王青, 曾绍华. 监测生长状态和环境响应的作物数字孪生系统研究综述[J]. 中国农业科技导报, 2022, 24(6): 90-105.

Figures/Tables 10

References 65

1	李仁旺,肖人彬.数字孪生驱动的大数据制造服务模式[J].科技导报,2020,38(14):116-125.
	LI R W, XIAO R B. Big data manufacturing service model driven by digital twin [J]. Sci. Technol. Rev., 2020, 38(14):116-125.
2	郭亮,张煜.数字孪生在制造中的应用进展综述[J].机械科学与技术,2020,39(4):590-598.
	GUO L, ZHANG Y. Overview of the application progress of digital twins in manufacturing [J]. Mech. Sci. Technol. Aerospace Eng.,2020,39(4):590-598.
3	GARCIA-SANCHEZ A J, GARCIA-SANCHEZ F, GARCIA-HARO J. Wireless sensor network deployment for integrating video-surveillance and data-monitoring in precision agriculture over distributed crops [J].Comput. Electron. Agricu., 2011, 75(2):288-303.
4	屈利华,赵春江,杨信廷,等. Zigbee无线传感器网络在温室多源数据采集系统中的应用综述[J].中国农机化,2012(4):179-183.
	QU L H, ZHAO C J, YANG X T, et al.. Application of Zigbee wireless sensor network in multiple-source data acquisition system of greenhouse [J]. J. Chin. Agric. Mech., 2012(4):179-183.
5	张文宇,曹宏鑫,葛道阔,等.基于模型的智慧农业平台的构建[J].江苏农业科学, 2015, 43(12):478-481.
	ZHANG W Y, CAO H X, GE D K, et al.. Construction of a model-based smart agriculture platform [J]. Jiangsu Agric. Sci., 2015, 43(12):478-481.
6	张红卫, 葛红梅. 农作物生长动态监测技术综述[C]//中国气象学会.第27届中国气象学会年会现代农业气象防灾减灾与粮食安全分会场论文集.北京:中国气象学会,2010: 528-536.
	ZHANG H W, GE H M. Summary of dynamic monitoring technology of crop growth [C]// Chinese Meteorological Society. Proceedings of the 27th annual meeting of the chinese meteorological society. Beijing: Chinese Meteorological Society,2010: 528-536.
7	曹宏鑫, 赵锁劳, 葛道阔, 等. 作物模型发展探[J].中国农业科学, 2011, 44(17):3520 -3528.
	CAO H X, ZHAO S L, GE D K, et al.. Discussion on development of crop models [J]. Sci. Agric. Sin., 2011, 44(17):3520 -3528.
8	杨靖民, 杨靖一, 姜旭, 等. 作物模型研究进展[J]. 吉林农业大学学报, 2012, 34(5): 553-561.
	YANG J M, YANG J Y, JIANG X, et al.. Progress of crop model research [J]. J. Jilin Agric. Univ., 2012, 34(5): 553-561.
9	DE WIT C T, BROUWER R, PENNING D V F W T. The simulation of photosynthetic systems [C]// Prediction and measurement of photosynthetic productivity. Proceedings of the IBP/PP technical meeting. Wageningen: Pudoc, 1970: 47-70.
10	PENNING D V F W T, HVAN LAAR H. Simulation of growth processes and the model BACROS [M]// PENNING D V F W T, VAN LAAR H H. Simulation of plant growth and crop production. Wageningen: Pudoc, 1982: 114-135.
11	VAN K H, PENNING D V F W T, DREES E M. A summary model for crop growth [M]//PENNING D V F W T, VAN LAAR H H. Simulation of plant growth and crop production. Wageningen: Pudoc, 1982: 87-97.
12	HIJMANS R J, LENS I, DIEPEN C. WOFOST 6.0: user's guide for the WOFOST 6.0 crop growth simulation model [J]. DLO Winand Staring Centre, 1994: 928-944.
13	PENNING DE VRIE F W T, JANSE D M, BERGE H F M TEN, et al.. Vries FWTPD,DM Jansen,HFMT Berge,Simulation of Ecophysiological Processes of Growth in Several Annual Crops [M]. Monographs 29. Pudoc. Netherlands Wageningen, 1989:1-271.
14	SPITTERS C J T, SCHAPENDONK A. Evaluation of breeding strategies for drought tolerance in potato by means of crop growth simulation [J]. Genet. Aspects Plant Miner. Nutr., 2012, 42: 151.
15	RITCHIE J T. Description and performance of CERES wheat: a user-oriented wheat yield model [Z]. ARS Wheat Yield Project, 1985: 159-175.
16	MCCOWN R L, HAMMER G L, HARGREAVES J N G, et al.. APSIM: a novel software system for model development, model testing and simulation in agricultural systems research [J]. Agric. Syst, 1996, 50(3): 255-271.
17	高亮之, 金之庆, 黄耀, 等. 水稻计算机模拟模型及其应用之一水稻钟模型——水稻发育动态的计算机模型[J]. 中国农业气象, 1989, 10(3): 3-10.
	GAO L Z, JIN Z Q, HUANG Y, et al.. Rice clock model—a computer simulation model of rice development [J]. Chin. J. Agrometeorol., 1989, 10(3): 3-10.
18	BAKER D N, LAMBERT J R, MCKINION J M. GOSSYM: a simulator of cotton crop growth and yield [J]. South Carolina Agric. Exp. Station Tech. Bull (USA), 1983(1089): 134.
19	RITCHIE J T. Model for predicting evaporation from a row crop with incomplete cover [J]. Water Resour. Res., 1972, 8(5): 1204-1213.
20	WILKERSON G G, JONES J W, BOOTE K J, et al.. Modeling soybean growth for crop management [J]. Trans. ASAE, 1983, 26(1): 63-73.
21	BOOTE K J, JONES J W, HOOGENBOOM G, et al.. PNUTGRO v. 1.02. peanut crop growth simulation model. user's guide [J]. Florida Agric. Exp. Station J., 1989 (8420): 32611.
22	HOOGENBOOM G, WHITE J W, JONES J W, et al.. BEANGRO: a process‐oriented dry bean model with a versatile user interface [J]. Agron. J., 1994, 86(1): 182-190.
23	BOOTE K J, HOOGENBOOM G. The CROPGRO model for grain legumes [J]. Understanding Options Agric. Production, 2013, 7: 99.
24	HOOGENBOOM G, JONES J W, BOOTE K J. Modeling growth, development, and yield of grain legumes using SOYGRO, PNUTGRO, and BEANGRO: a review [J]. Trans. ASAE, 1992, 35(6): 2043-2056.
25	赵福成,谭禾平, 包斐, 等.作物生长模拟模型及其在玉米上的应用综述[J].农业科技通讯, 2016(11):12-14.
	ZHAO F C, TAN H P, BAO F, et al.. Summary of crop growth simulation model and its application on corn [J]. Bull. Agric. Sci. Technol., 2016(11):12-14.
26	JONES J W, HOOGENBOOM G, Porter C H, et al.. The DSSAT cropping system model [J]. Eur. J. Agron, 2003, 18(3-4): 235-265.
27	金之庆,高亮之,黄耀,等.RCSODS在推广水稻新品种上的应用[J].中国水稻科学,1994(4):217-222.
	JIN Z Q, GAO L Z, HUANG Y, et al.. Application of RCSODS in extending new rice varieties [J]. Chin. J. Rice Sci., 1994(4):217-222.
28	苏李君,刘云鹤,王全九.基于有效积温的中国水稻生长模型的构建[J].农业工程学报,2020,36(1):162-174.
	SU L J, LIU Y H, WANG Q J. Rice growth model in China based on growing degree days [J]. Trans. Chin. Soc. Agric. Eng., 2020,36(1):162-174.
29	孙宁,冯利平.小麦生长发育模拟模型在华北冬麦区适用性验证[J].中国生态农业学报,2006(1):71-72.
	SUN N, FENG L P. Validation of WheatSM model in winter wheat production region of north China [J]. Chin. J. Eco-Agric., 2006(1):71-72.
30	潘学标,韩湘玲,石元春.COTGROW:棉花生长发育模拟模型[J].棉花学报,1996(4):180-188.
	PAN X B, HAN X L, SHI Y C. COTGROW: cotton growth and development simulation model [J]. Cott. Sci., 1996(4):180-188.
31	冯利平,韩学信.棉花栽培计算机模拟决策系统(COTSYS)[J].棉花学报,1999(5):251-254.
	FENG L P, HAN X X. COTSYS cotton cultivational simulation and decision making system [J]. Cott. Sci., 1999(5):251-254.
32	SELEZNYOVA A N, HANAN J. Mechanistic modelling of coupled phloem and computational methods [J]. Ann. Bot,2018,121(5):991-1003.
33	VAZQUEZ-ARELLANO M, REISER D, PARAFOROS D S, et al..3-D reconstruction of maize plants using a time-of-flight camera [J]. Comput. Electron. Agric.,2018,145:235-247.
34	吴峰峰,朱波,周恺,等.虚拟作物模型的研究现状及展望[J].北方园艺, 2020(1):162-169.
	WU F F, ZHU B, ZHOU K, et al.. Research status and prospect of virtual crop model [J]. Northern Hortic., 2020(1):162-169.
35	COEN E, PRUSINKIEWICZ P. Quantitative modeling of Arabidopsis development [J]. Plant Physiol., 2005,139(2):960-968.
36	FRASSON R P D M, KRAJEWSKI W F. Three-dimensional digital model of a maize plant [J]. Agric. For. Meteorol.,2010,150(3):478-488.
37	JALLAS E, SEQUEIRA R, MARTIN P, et al.. Mechanistic virtual modeling: coupling a plant simulation model with a three-dimensional plant architecture component [J]. Environ. Model. Assess.,2009,14(1):29-45.
38	李书钦,刘海龙,诸叶平,等. 基于实测数据和NURBS曲面的小麦叶片三维可视化[J] .福建农业学报,2016,31(7):777-782.
	LI S Q, LIU H L, ZHU Y P, et al.. 3D visualization of wheat leaves using measured data and NURBS surface [J]. Fujian J. Agric. Sci., 2016,31(7):777-782.
39	王美丽,何东健.基于 L 系统的小麦根系可视化模拟研究[J].农机化研究,2008(3):36 -39.
	WANG M L, HE D J. Visualized simulation of wheat roots based on L-system [J]. J. Agric. Mech. Res., 2008(3):36-39.
40	LEE K H, EHSANI R. Comparison of two 2D laser scanners for sensing object distances, shapes, and surface patterns [J]. Comput. electron. Agric., 2008, 60(2): 250-262.
41	袁晓敏,温维亮,郭新宇,等.番茄群体冠层形态结构三维模拟—基于实测数据[J].农机化研究,2012,34(2):172-176.
	YUAN X M, WEN W L, GUO X Y,et al.. 3-Dimentional morphology simulation of tomato canopy—based on measured data [J]. J. Agric. Mech. Res.,2012,34(2):172-176.
42	王勇健,温维亮,郭新宇,等.基于点云数据的植物叶片三维重建[J].中国农业科技导报,2014,16(5):83-89.
	WANG Y J, WEN W L, GUO X Y, et al.. Three-dimensional reconstruction of plant leaf blade based on point cloud data [J]. J. Agric. Sci. Technol., 2014,16(5):83-89.
43	王孟博.基于点云的作物植株三维重建技术研究[J].鄂州大学学报,2018,25(1):104-106, 109.
	WANG M B. Research on 3D reconstruction technology of crop plant based on point cloud [J]. J. Ezhou Univ., 2018,25(1):104-106, 109.
44	邵小宁. 基于Kinect的植物三维点云获取与重建方法研究[D].陕西杨凌:西北农林科技大学,2016.
	SHAO X N. Research on 3D point cloud acquisition and reconstruction method of plant based on kinect [D]. Shaanxi Yangling:Northwest A&F University, 2016.
45	ZENG J, ZHANG Y, ZHAN S. 3D tree models reconstruction from a single image [C]//The IEEE Computer Society. The sixth international conference on intelligent systems design and applications. Piscataway: IEEE, 2006: 445-450.
46	杨亮,郭新宇,陆声链,等.基于多幅图像的黄瓜叶片形态三维重建[J].农业工程学报,2009,25(2):141-144.
	YANG L, GUO X Y, LU S L, et al.. 3D morphological reconstruction of cucumber leaf based on multiple images [J]. Trans. Chin. Soc. Agric. Eng., 2009, 25(2): 141-144.
47	王传宇,赵明,阎建河,等.基于双目立体视觉技术的玉米叶片三维重建[J].农业工程学报,2010,26(4):198-202.
	WANG C Y, ZHAO M, YAN J H, et al.. Three-dimensional reconstruction of maize leaves based on binocular stereovision system [J]. Trans. Chin. Soc. Agric. Eng., 2010, 26(4): 198-202.
48	袁挺,李伟,谭豫之,等.温室环境下黄瓜采摘机器人信息获取[J].农业机械学报,2009,40(10):151-155.
	YUAN T, LI W, TAN Y Z, et al.. Information acquisition for cucumber harvesting robot in greenhouse [J]. Trans. Chin. Soc. Agric. Mach., 2009, 40(10): 151-155.
49	朱冰琳,刘扶桑,朱晋宇,等.基于机器视觉的大田植株生长动态三维定量化研究[J].农业机械学报,2018,49(5):256-262.
	ZHU B L, LIU F S, ZHU J Y, et al.. Three-dimensional quantifications of plant growth dynamics in field-grown plants based on machine vision method [J]. Trans. Chin. Soc. Agric. Mach., 2018, 49(5): 256-262.
50	李书钦,诸叶平,刘海龙,等.小麦生长模拟与三维可视化系统构建技术研究[J].中国农业科技导报,2018,20(2):65-71.
	LI S Q, ZHU Y P, LIU H L, et al.. Wheat growth simulation and 3D visualization system construction technology [J]. J. Agric. Sci. Technol.,2018,20(2):65-71.
51	孙爱珍,杨红云,何火娇, 等.棉花苞叶三维形态可视化建模研究[J].农机化研究,2010,32(7):56-58, 64.
	SUN A Z, YANG H Y, HE H J, et al.. Study on modeling 3D shape and visualization of cotton bract [J]. J. Agric. Mech. Res., 2010, 32(7): 56-58, 64.
52	汪强,郑光,马新明,等.基于Unity 3D引擎的玉米栽培可视化系统[J].广西科学,2017,24(3):292-297.
	WANG Q, ZHENG G, MA X M, et al.. Corn cultivation visualization system based on unity 3D engine [J]. Guangxi Sci., 2017, 24(3): 292-297.
53	岳学军,蔡雨霖,王林惠,等.农情信息智能感知及解析的研究进展[J].华南农业大学学报,2020,41(6):14-28.
	YUE X J, CAI Y L, WANG L H, et al.. Research progress of intelligent perception and analytics of agricultural information [J]. J. South China Agric.Univ., 2020, 41(6): 14-28.
54	康孟珍,王秀娟,华净,等.平行农业:迈向智慧农业的智能技术[J].智能科学与技术学报,2019,1(2):107-117.
	KANG M Z, WANG X J, HUA J, et al.. Parallel agriculture: intelligent technology toward smart agriculture [J]. Chin. J. Intelligent Sci. Technol., 2019, 1(2): 107-117.
55	吴炳方,张淼,曾红伟,等.大数据时代的农情监测与预警[J].遥感学报,2016,20(5):1027-1037.
	WU B F, ZHANG M, ZENG H W, et al.. Agricultural monitoring and early warning in the era of big data [J]. National Remote Sens. Bull., 2016, 20(5): 1027-1037.
56	顾静秋. 农业数据智能感知与分析关键技术研究 [D].北京:北京交通大学, 2018.
	GU J Q. Research on smart perception and intelligent analysis of agricultural data [D]. Beijing: Beijing Jiaotong University, 2018.
57	刘慧,汤亮,张文宇,等.基于模型的可视化水稻生长系统的构建与实现[J].农业工程学报,2009,25(9):148-154, 362.
	LIU H, TANG L, ZHANG W Y, et al.. Construction and implementation of model-based visual rice growth system [J]. Trans. Chin. Soc. Agric. Eng., 2009, 25(9): 148-154, 362.
58	何火娇,杨红云,唐建军,等.水稻植株虚拟生长可视化系统设计及其实现[J].系统仿真学报,2009,21(14):4393-4396.
	HE H J, YANG H Y, TANG J J, et al.. Design and realization of virtual growth visualization system for rice plant [J]. J. System Simulation, 2009, 21(14): 4393-4396.
59	CHOY C B, XU D, GWAK J Y, et al.. 3d-r2n2: a unified approach for single and multi-view 3d object reconstruction [C]// European Conference on Computer Vision. Proceadings of the 14th European Conference. Berlin: Springer, 2016:628-644.
60	FAN H, SU H, GUIBAS L. A point set generation network for 3D object reconstruction from a single image [C]// IEEE Computer Society. 2017 IEEE conference on computer vision and pattern recognition (CVPR). Los Alamitos : IEEE Computer Society, 2017: 2463-2471.
61	WANG N Y, ZHANG Y, LI Z, et al.. Pixel2mesh: Generating 3d mesh models from single rgb images [C]// European Conference on Computer Vision. Proceadings of the 15th european conference. Berlin: Springer, 2018: 52-67.
62	YAO Y, LUO Z, LI S, et al.. Mvsnet: Depth inference for unstructured multi-view stereo [C]// European Conference on Computer Vision (ECCV). The 15th european conference. Berlin: Springer, 2018: 767-783.
63	MADEC S, JIN X, LU H, et al.. Ear density estimation from high resolution RGB imagery using deep learning technique [J]. Agricu. for. meteorol., 2019, 264: 225-234.
64	YOU J, LI X, LOW M, et al.. Deep gaussian process for crop yield prediction based on remote sensing data [C]// The AAAI Conference on Artificial Intelligence. Proceedings of the thirty-first AAAI conference on artificial intelligence. Menlo Park: AAAI Press, 2017: 4559-4565.
65	SONG X, ZHANG G, LIU F, et al.. Modeling spatio-temporal distribution of soil moisture by deep learning-based cellular automata model [J]. J. Arid Land, 2016, 8(5): 734-748.

模拟类型 Type of model	建模方法 Modeling method	适用范围 Applicable scope	采集图像 Image acquisition	数据量 Data volume	建模精度 Modeling accuracy	建模速度 Modeling speed
基于生长规则方法 Growth rule-based method	L-System 和AMAP方法 L-System and AMAP method	生长较为规则的作物 Regularly growing crops	无 No	★★★	★	★★
基于生长规则方法 Growth rule-based method	NURBS曲面方法 NURBS surface method	小麦、水稻叶片 Wheat and rice leaves	有 Yes	★★★	★★	★★★★
基于仪器方法 Instrument-based method	三维扫描仪 3D scanner	中小型作物 Middle and small crops	无 No	★★★	★★★★	★★
基于图像方法 Image-based method	Kinect深度图重建 Kinect depth map reconstruction	不限 No limit	有Yes	★★★★	★★★	★★★★
	单幅图像 Single image	不限 No limit	有Yes	★★	★★	★★★★★
	运动图像 Moving image	不限 No limit	有Yes	★★★★	★★★	★★★★
	立体视觉 Stereo vision	不限 No limit	有Yes	★★★	★★★	★★★★★

模拟类型 Type of model	建模方法 Modeling method	适用范围 Applicable scope	采集图像 Image acquisition	数据量 Data volume	建模精度 Modeling accuracy	建模速度 Modeling speed
基于生长规则方法 Growth rule-based method	L-System 和AMAP方法 L-System and AMAP method	生长较为规则的作物 Regularly growing crops	无 No	★★★	★	★★
基于生长规则方法 Growth rule-based method	NURBS曲面方法 NURBS surface method	小麦、水稻叶片 Wheat and rice leaves	有 Yes	★★★	★★	★★★★
基于仪器方法 Instrument-based method	三维扫描仪 3D scanner	中小型作物 Middle and small crops	无 No	★★★	★★★★	★★
基于图像方法 Image-based method	Kinect深度图重建 Kinect depth map reconstruction	不限 No limit	有Yes	★★★★	★★★	★★★★
	单幅图像 Single image	不限 No limit	有Yes	★★	★★	★★★★★
	运动图像 Moving image	不限 No limit	有Yes	★★★★	★★★	★★★★
	立体视觉 Stereo vision	不限 No limit	有Yes	★★★	★★★	★★★★★

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[5]	GUAN Dahai1, ZHANG Jun2, WANG Qingmei1, ZHANG Yanping1, . Climate-Smart Agriculture and its Enlightenment to Agricultural Development of China [J]. Journal of Agricultural Science and Technology, 2017, 19(10): 7-13.
[6]	CHEN Xiao-dong1, YUAN Xiang-yang1, GUO Ping-yi1*, NING Na1, GUO Mei-jun1, LAN Ya. Progress and Prospect in Agricultural Internet of Things [J]. , 2015, 17(2): 8-16.
[7]	SUN Zhong\\|fu1, DU Ke\\|ming1, ZHENG Fei\\|xiang1, YIN Shou\\|yi2. Perspectives of Research and Application of Big Data on Smart Agriculture [J]. , 2013, 15(6): 63-71.