基于无人机数码影像的水稻叶面积指数监测

doi:10.16819/j.1001-7216.2022.210712

中国水稻科学 ›› 2022, Vol. 36 ›› Issue (3): 308-317.DOI: 10.16819/j.1001-7216.2022.210712

基于无人机数码影像的水稻叶面积指数监测

曹中盛¹, 李艳大¹^,^*(), 黄俊宝¹, 叶春¹, 孙滨峰¹, 舒时富¹, 朱艳², 何勇³

¹江西省农业科学院农业工程研究所/江西省智能农机装备工程研究中心/江西省农业信息化工程技术研究中心，南昌 330200
²南京农业大学/国家信息农业工程技术中心，南京 210095
³浙江大学生物系统工程与食品科学学院，杭州 310029

收稿日期:2021-07-29 修回日期:2021-11-04 出版日期:2022-05-10 发布日期:2022-05-11
通讯作者: 李艳大
基金资助:
国家重点研发计划资助项目(2016YFD0300608);江西省“双千计划”资助项目;江西省科技计划资助项目(20202BBFL63044);江西省科技计划资助项目(20182BCB22015);江西省科技计划资助项目(20202BBFL63046);江西省科技计划资助项目(20192BBF60052);江西省农业科研协同创新项目(JXXTCXQN202110);江西省农业科学院创新基金博士启动项目(20182CBS001)

Monitoring Rice Leaf Area Index Based on Unmanned Aerial Vehicle (UAV) Digital Images

CAO Zhongsheng¹, LI Yanda¹^,^*(), HUANG Junbao¹, YE Chun¹, SUN Binfeng¹, SHU Shifu¹, ZHU Yan², HE Yong³

¹Institute of Agricultural Engineering, Jiangxi Engineering Research Center of Intelligent Agricultural Machinery Equipment/Jiangxi Engineering Research Center of Information Technology in Agriculture, Jiangxi Academy of Agricultural Sciences, Nanchang 330200, China
²National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
³College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China

Received:2021-07-29 Revised:2021-11-04 Online:2022-05-10 Published:2022-05-11
Contact: LI Yanda

摘要/Abstract

摘要：

【目的】为探究无人机数码影像监测水稻叶面积指数(Leaf area index, LAI)的可行性，明确利用无人机数码影像监测水稻LAI的最佳时期，构建基于无人机数码影像的水稻LAI监测模型。【方法】本研究基于不同品种和施氮量的水稻田间试验，于分蘖期、拔节期、孕穗期、抽穗期和灌浆期测定水稻LAI，同步使用无人机搭载数码相机获取水稻无人机数码影像并提取颜色指数及纹理特征，分析其在不同生育时期与水稻LAI之间的相关性，构建定量监测模型，并用独立试验数据对所建模型进行检验。【结果】无人机数码影像中颜色指数及纹理特征与水稻LAI之间的相关性在生育前期(分蘖期+拔节期)最高，高于所有单生育期、生育后期(孕穗期+抽穗期+灌浆期)和全生育期，可确定为监测的最佳时期;在颜色指数和纹理特征当中，纹理特征方差(Variance, VAR)在监测水稻生育前期LAI时表现最优，可构建监测模型LAI = 1.1656×exp^{(0.0174×VAR)}实现监测，模型构建时的决定系数(Determination coefficient, R²)为0.7980，模型检验时的相对均方根误差(Relative root mean square error, RRMSE)和偏差(bias, θ)分别为0.1658和0.1306。【结论】与人工测量LAI相比，基于无人机数码影像的水稻LAI监测方法可提高作业效率，降低成本，在水稻长势快速准确监测和丰产高效栽培中具有应用价值。

关键词: 水稻, 叶面积指数, 无人机, 数码影像, 纹理特征, 监测模型

Abstract:

【Obiective】Leaf area index (LAI) is a crucial variable for assessing rice growth, and unmanned aerial vehicle (UAV) digital images can serve as an efficient way to real-time, no-destructive monitoring of crop growth parameters. However, it remains unclear which parameter in digital images can be used to estimate rice LAI. In addition, the optimal growth stage for monitoring is also unknown. 【Method】In this study, the UAV digital images were initially collected from two field experiments encompassing variations over two years with four cultivars at four nitrogen application levels. Then, the relationship between UAV digital image parameters (nine color indices and eight texture features) and rice LAI at different growth stages (tillering stage, jointing stage, booting stage, heading stage and filling stage) were analyzed. 【Result】The results suggested that the early growth stages, including both tillering stage and jointing stage, were suitable for rice LAI monitoring through UAV digital images, and the texture feature variance (VAR) exhibits greatest accuracy in model calibration with a determination coefficient (R²) of 0.7980. In the validation based on independent experiment, this texture feature also performs well with relative root mean square error (RRMSE) of 0.1658 and bias(θ) of 0.1306. 【Conclusion】Taking the accuracy and convenience in application into consideration, we found that the texture feature VAR could be used to monitor rice LAI in early growth stage with estimation models of LAI = 1.1656×exp^{(0.0174×VAR)}.

Key words: rice, leaf area index, unmanned aerial vehicle, digital image, texture feature, monitoring model

曹中盛, 李艳大, 黄俊宝, 叶春, 孙滨峰, 舒时富, 朱艳, 何勇. 基于无人机数码影像的水稻叶面积指数监测[J]. 中国水稻科学, 2022, 36(3): 308-317.

CAO Zhongsheng, LI Yanda, HUANG Junbao, YE Chun, SUN Binfeng, SHU Shifu, ZHU Yan, HE Yong. Monitoring Rice Leaf Area Index Based on Unmanned Aerial Vehicle (UAV) Digital Images[J]. Chinese Journal OF Rice Science, 2022, 36(3): 308-317.

图/表 9

参考文献 32

[1]	龚金龙, 邢志鹏, 胡雅杰, 张洪程, 戴其根, 霍中洋, 许轲, 魏海燕, 高辉. 籼、粳超级稻光合物质生产与转运特征的差异[J]. 作物学报, 2014, 40(3): 497-510.
	Gong J L, Xing Z P, Hu Y J, Zhang H C, Dai Q G, Huo Z Y, Xu K, Wei H Y, Gao H. Difference of characteristics of photosynthesis, matter production and translocation between indica and japonica super rice[J]. Acta Agronomica Sinica, 2014, 40(3): 497-510. (in Chinese with English abstract)
[2]	姜元华, 许轲, 赵可, 孙建军, 韦还和, 许俊伟, 魏海燕, 郭保卫, 霍中洋, 戴其根, 张洪程. 甬优系列籼粳杂交稻的冠层结构与光合特性[J]. 作物学报, 2015, 41(2): 286-296.
	Jiang Y H, Xu K, Zhao K, Sun J J, Wei H H, Xu J W, Wei H Y, Guo B W, Huo Z Y, Dai Q G, Zhang H C. Canopy structure and photosynthetic characteristics of Yongyou series of indica-japonica hybrid rice under high-yielding cultivation condition[J]. Acta Agronomica Sinica, 2015, 41(2): 286-296. (in Chinese with English abstract)
[3]	殷尧翥, 郭长春, 孙永健, 武云霞, 余华清, 孙知白, 张桥, 王海月, 杨志远, 马均. 稻油轮作下油菜秸秆还田与水氮管理对杂交稻群体质量和产量的影响[J]. 中国水稻科学, 2019, 33(3): 257-268.
	Yin Y Z, Guo C C, Sun Y J, Wu Y X, Yu H Q, Sun Z B, Zhang Q, Wang H Y, Yang Z Y, Ma J. Effects of rape straw retention and water and nitrogen management on population quality and yield of hybrid rice under rice-rape rotation[J]. Chinese Journal of Rice Science, 2019, 33(3): 257-268. (in Chinese with English abstract)
[4]	侯佳敏, 罗宁, 王溯, 孟庆锋, 王璞. 增密对我国玉米产量-叶面积指数-光合速率的影响[J]. 中国农业科学, 2021, 54(12): 2538-2546.
	Hou J M, Luo N, Wang S, Meng Q F, Wang P. Effects of increasing planting density on grain yield, leaf area index and photosynthetic rate of maize in China[J]. Scientia Agricultura Sinica, 2021, 54(12): 2538-2546. (in Chinese with English abstract)
[5]	李艳大, 孙滨峰, 曹中盛, 叶春, 舒时富, 黄俊宝, 何勇. 基于作物生长监测诊断仪的双季稻叶面积指数监测模型[J]. 农业工程学报, 2020, 36(10): 141-149.
	Li Y D, Sun B F, Cao Z S, Ye C, Shu S F, Huang J B, He Y. Model for monitoring leaf area index of double cropping rice based on crop growth monitoring and diagnosis apparatus[J]. Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE) , 2020, 36(10): 141-149. (in Chinese with English abstract)
[6]	Qiao K, Zhu W Q, Xie Z Y, Li, P X. Estimating the seasonal dynamics of the leaf area index using piecewise LAI-VI relationships based on phenophases[J]. Remote Sensing, 2019, 11(6): 689.
[7]	李艳大, 舒时富, 陈立才, 叶春, 王康军, 黄俊宝. 基于归一化法的双季稻叶面积指数动态预测模型[J]. 中国农学通报, 2017, 33(29): 77-84.
	Li Y D, Shu S F, Chen L C, Ye C, Wang K J, Huang J B. A predict model of dynamic leaf area index of double cropping rice based on normalized method[J]. Chinese Agricultural Science Bulletin, 2017, 33(29): 77-84. (in Chinese with English abstract)
[8]	Campos-Taberner M, García-Haro F J, Camps-Valls G, Grau-Muedra G, Nutini F, Crema A, Boschetti M. Multitemporal and multiresolution leaf area index retrieval for operational local rice crop monitoring[J]. Remote Sensing of Environment, 2016, 187: 102-118.
[9]	张漫, 苗艳龙, 仇瑞承, 季宇寒, 李寒, 李民赞. 基于车载三维激光雷达的玉米叶面积指数测量[J]. 农业机械学报, 2019, 50(6): 12-21.
	Zhang M, Miao Y L, Qiu R C, Ji Y H, Li H, Li M Z. Maize leaf area index measurement based on vehicle 3D LiDAR[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(6): 12-21. (in Chinese with English abstract)
[10]	牛小桃, 樊军, 王胜, 王秋铭. 利用鱼眼摄像机测量植被叶面积指数动态变化[J]. 应用生态学报, 2018, 29(10): 3183-3190.
	Niu X T, Fan J, Wang S, Wang Q M. measuring the dynamic of leaf area index of vegetation using fisheye camera[J]. Chinese Journal of Applied Ecology, 2018, 29(10): 3183-3190. (in Chinese with English abstract)
[11]	刘杨, 冯海宽, 黄珏, 孙乾, 杨福芹, 杨贵军. 基于无人机高光谱影像的马铃薯株高和地上生物量估算[J]. 农业机械学报, 2021, 52(2): 188-198.
	Liu Y, Feng H K, Huang J, Sun Q, Yang F Q, Yang G J. Estimation of potato plant height and above-ground biomass based on UAV hyperspectral images[J]. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(2): 188-198. (in Chinese with English abstract)
[12]	宋勇, 陈兵, 王琼, 苏维, 孙乐鑫, 赵静, 韩焕勇, 王方永. 无人机遥感监测作物病虫害研究进展[J]. 棉花学报, 2021, 33(3): 291-306.
	Song Y, Chen B, Wang Q, Su W, Sun L X, Zhao J, Han H Y, Wang F Y. Research advances of crop diseases and insect pests monitoring by unmanned aerial vehicle remote sensing[J]. Cotton Science, 2021, 33(3): 291-306. (in Chinese with English abstract)
[13]	Zhang L Y, Zhang H H, Niu Y X, Han W T. Mapping maize water stress based on UAV multispectral remote sensing[J]. Remote Sensing, 2019, 11(6): 605.
[14]	江杰, 张泽宇, 曹强, 田永超, 朱艳, 曹卫星, 刘小军. 基于消费级无人机搭载数码相机监测小麦长势状况研究[J]. 南京农业大学学报, 2019, 42(4): 622-631.
	Jiang J, Zhang Z Y, Cao Q, Tian Y C, Zhu Y, Cao W X, Liu X J. Use of a digital camera mounted on a consumer-grade unmanned aerial vehicle to monitor the growth status of wheat[J]. Journal of Nanjing Agricultural University, 2019, 42(4): 622-631. (in Chinese with English abstract)
[15]	高林, 杨贵军, 李红军, 李振海, 冯海宽, 王磊, 董锦绘, 贺鹏. 基于无人机数码影像的冬小麦叶面积指数探测研究[J]. 中国生态农业学报, 2016, 24(9): 1254-1264.
	Gao L, Yang G J, Li H J, Li Z H, Feng H K, Wang L, Dong J H, He P. Winter wheat LAI estimation using unmanned aerial vehicle RGB-imaging[J]. Chinese Journal of Eco-Agriculture, 2016, 24(9): 1254-1264. (inChinese with English abstract)
[16]	Hunt E R, Cavigelli M, Daughtry C, Mcmurtrey J E, Walthall C L. Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status[J]. Precision Agriculture, 2005, 6(4): 359-378.
[17]	万亮, 岑海燕, 朱姜蓬, 张佳菲, 杜晓月, 何勇. 基于纹理特征与植被指数融合的水稻含水量无人机遥感监测[J]. 智慧农业(中英文), 2020, 2(1): 58-67.
	Wan L, Cen H Y, Zhu J P, Zhang J F, Du X Y, He Y. Using fusion of texture features and vegetation indices from water concentration in rice crop to UAV remote sensing monitor[J]. Smart Agriculture, 2020, 2(1): 58-67. (in Chinese with English abstract)
[18]	陈鹏飞, 梁飞. 基于低空无人机影像光谱和纹理特征的棉花氮素营养诊断研究[J]. 中国农业科学, 2019, 52(13): 2220-2229.
	Chen P F, Liang F. Cotton nitrogen nutrition diagnosis based on spectrum and texture feature of images from low altitude unmanned aerial vehicle[J]. Scientia Agricultura Sinica, 2019, 52(13): 2220-2229. (in Chinese with English abstract)
[19]	Konôpka B, Pajtík J, Marušák R, Boše’a M, Lukac M. Specific leaf area and leaf area index in developing stands of Fagus sylvatica L. and Piceaabies karst[J]. Forest Ecology and Management, 2016, 364: 52-59.
[20]	Shibayama M C, Sakamoto T H, Takada E J, Inoue A H, Morita K H, Yamaguchi T K Y, Takahashi W T R, Kimura A H. Estimating rice leaf greenness (SPAD) using fixed-point continuous observations of visible red and near infrared narrow-band digital images[J]. Plant Production Science, 2012, 15(4): 293-309.
[21]	Meyer G E, Hindman T W, Laksmi K. Machine vision detection parameters for plant species identification[J]. Proceedings of SPIE: The International Society for Optical Engineering, 1999, 3543: 327-335.
[22]	Meyer G E, Neto J C. Verification of color vegetation indices for automated crop imaging applications[J]. Computers and Electronics in Agriculture, 2008, 63(2): 282-293.
[23]	Gitelson A A, Kaufman Y J, Stark R, Rundquist D. Novel algorithms for remote estimation of vegetation fraction[J]. Remote Sensing of Environment, 2002, 80(1): 76-87.
[24]	Louhaichi M, Borman M M, Johnson D E. Spatially located platform and aerial photography for documentation of grazing impacts on wheat[J]. Geocarto International, 2001, 16(1): 65-70.
[25]	Haralick R M, Shanmugam K, Dinstein I. Textural features for image classification[J]. Studies in Media and Communication, 1973, SMC-3(6): 610-621.
[26]	Cao Z S, Cheng T, Ma X, Tian Y C, Zhu Y, Yao X, Chen Q, Liu S Y, Guo Z Y, Zhen Q M, Li X. A new three-band spectral index for mitigating the saturation in the estimation of leaf area index in wheat[J]. International Journal of Remote Sensing, 2017, 38(13): 3865-3885.
[27]	Bendig J, Yu K, Aasen H, Bolten A, Bennertz S, Broscheit J, Gnyp M L, Bareth G. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley[J]. International Journal of Applied Earth Observation and Geoinformation, 2015, 39: 79-87.
[28]	郑恒彪. 水稻生育期及生长参数的近地面遥感监测研究[D]. 南京: 南京农业大学, 2018.
	Zheng H B. Monitoring rice phenology and growth parameters using near-ground remote sensing platforms[D]. Nanjing: Nanjing agricultural university, 2018, Nanjing. (in Chinese with English abstract)
[29]	Sun B, Wang C F, Yang C H, Xu B D, Zhou G S, Li X Y, Xie J, Xu S J, Liu B, Xie T J, Kuai J, Zhang J. Retrieval of rapeseed leaf area index using the PROSAIL model with canopy coverage derived from UAV images as a correction parameter[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 102: 102373.
[30]	Ciganda V S, Gitelson A A, Schepers J. How deep does a remote sensor sense? Expression of chlorophyll content in a maize canopy[J]. Remote Sensing of Environment, 2012, 126: 240-247.
[31]	Mishra P, Nordon A, Mohd A, Mohd S, Lian, G P, Redfern S. Fusing spectral and textural information in near-infrared hyperspectral imaging to improve green tea classification modelling[J]. Journal of Food Engineering, 2019, 249: 40-47.
[32]	Duan M Q, Zhang X G. Using remote sensing to identify soil types based on multiscale image texture features[J]. Computers and Electronics in Agriculture, 2021, 187: 106272.

参数 Parameter	数值 Value
传感器 Sensor	1/2.3 英寸CMOS
有效像素 Effective pixels	1 235 万
最大分辨率 Maximum resolution	4000 × 3000
质量 Quality	734 g
续航时间 Battery terms	21 min

参数 Parameter	数值 Value
传感器 Sensor	1/2.3 英寸CMOS
有效像素 Effective pixels	1 235 万
最大分辨率 Maximum resolution	4000 × 3000
质量 Quality	734 g
续航时间 Battery terms	21 min

序号 Number	颜色指数 Color index	计算公式 Equation	参考文献 Reference
1	红光标准化值NRI Normalized redness intensity	r/(r+g+b)	[20]
2	绿光标准化值NGI Normalized greenness intensity	g/(r+g+b)	[20]
3	蓝光标准化值NBI Normalized blueness intensity	b/(r+g+b)	[20]
4	归一化绿红差值指数NGRDI Normalized green minus red difference index	(g-r)/(g+r)	[20]
5	超绿植被指数ExG Excess green vegetation index	2g-r-b	[20]
6	超红植被指数ExR Excess red vegetation index	1.4r-b	[21]
7	超绿超红差分植被指数ExGR Excess green minus excess red vegetation index	ExG-ExR	[22]
8	可见光大气阻抗植被指数VARI Visible light atmospheric resistant vegetation index	(g-r)/(g+r-b)	[23]
9	绿叶植被指数GLI Green leaf vegetation index	(2g-b-r)/(2g+b+r)	[24]

序号 Number	颜色指数 Color index	计算公式 Equation	参考文献 Reference
1	红光标准化值NRI Normalized redness intensity	r/(r+g+b)	[20]
2	绿光标准化值NGI Normalized greenness intensity	g/(r+g+b)	[20]
3	蓝光标准化值NBI Normalized blueness intensity	b/(r+g+b)	[20]
4	归一化绿红差值指数NGRDI Normalized green minus red difference index	(g-r)/(g+r)	[20]
5	超绿植被指数ExG Excess green vegetation index	2g-r-b	[20]
6	超红植被指数ExR Excess red vegetation index	1.4r-b	[21]
7	超绿超红差分植被指数ExGR Excess green minus excess red vegetation index	ExG-ExR	[22]
8	可见光大气阻抗植被指数VARI Visible light atmospheric resistant vegetation index	(g-r)/(g+r-b)	[23]
9	绿叶植被指数GLI Green leaf vegetation index	(2g-b-r)/(2g+b+r)	[24]

序号 Number	纹理特征 Texture feature	简写 Abbreviation	说明 Description
1	均值 Mean	MEA	反映纹理的平均值
2	方差 Variance	VAR	反映纹理的变化情况
3	均一性 Homogeneity	HOM	反映纹理的同质性
4	对比度 Contrast	CON	反映纹理的清晰度
5	异质性 Dissimilarity	DIS	反映纹理的相似性
6	熵 Entropy	ENT	反映纹理的复杂程度
7	角二阶矩 Second moment	SEC	反映纹理粗细度
8	相关性 Correlation	COR	反映纹理的一致性

基于无人机数码影像的水稻叶面积指数监测

Monitoring Rice Leaf Area Index Based on Unmanned Aerial Vehicle (UAV) Digital Images

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Metrics

颜色指数和纹理特征 Color index and texture feature	相关系数 Correlation coefficient
颜色指数和纹理特征 Color index and texture feature	分蘖期 Tillering	拔节期 Jointing	孕穗期 Booting	抽穗期 Heading	灌浆期 Filling	分蘖期+拔节期 Tillering+Jointing	孕穗期+抽穗期+灌浆期 Booting+Heading+Filling	全生育期 All
R	-0.476^**	-0.642^**	-0.429^**	-0.277	0.009	0.050	-0.372^**	0.098
G	-0.327	-0.478^*	-0.263	-0.160	-0.043	0.074	-0.340^**	-0.021
B	-0.327	-0.472^*	-0.083	0.116	-0.104	0.248	-0.035	0.159
NGRDI	-0.072	0.221	-0.108	0.002	-0.066	0.145	0.117	-0.292^**
ExG	0.182	0.392	-0.057	-0.247	-0.006	-0.316^*	-0.338^**	-0.396^**
ExR	-0.736^**	-0.712^**	-0.559^**	-0.402^**	0.109	0.004	-0.380^**	-0.301^**
ExGR	-0.176	0.154	-0.252	-0.116	0.042	-0.146	-0.222	0.172
VARI	0.258	0.586^**	-0.292	-0.038	0.076	0.808^**	0.247^*	0.211^**
GLI	0.366^*	0.080	0.080	0.121	0.076	-0.336^*	0.116	-0.346^**
MEA	-0.644^**	-0.377	-0.463^*	-0.582^**	0.088	0.018	-0.349^**	0.124
VAR	0.768^**	0.759^**	0.628^**	0.483^**	-0.096	0.910^**	0.400^**	0.513^**
HOM	-0.735^**	-0.448^*	-0.478^**	-0.481^**	0.047	0.786^**	-0.348^**	-0.670^**
CON	0.772^**	0.735^**	0.577^**	0.513^**	-0.067	0.907^**	0.395^**	0.509^**
DIS	0.777^**	0.723^**	0.552^**	0.521^**	-0.063	0.894^**	0.389^**	0.607^**
ENT	0.545^**	-0.047^**	0.321	0.114	-0.037	0.597^**	0.161	0.506^**
SEC	-0.466^*	0.156	-0.259	-0.006^**	0.064	-0.484^**	-0.036	-0.506^**
COR	0.528^**	0.669^**	0.298	-0.376	-0.054	0.213	0.061	0.455^**

颜色指数和纹理特征 Color index and texture feature	生育期 Growth stage	建模 Calibration		检验 Validation
颜色指数和纹理特征 Color index and texture feature	生育期 Growth stage	监测模型 Model	决定系数 R²	相对均方根误差 RRMSE	偏差 θ
ExR	分蘖 Tillering	LAI = 5.9395×exp^{(-0.0150×ExR)}	0.5169	0.5394	1.8288
	拔节 Jointing	LAI = 6.8568×exp^{(-0.0131×ExR)}	0.4712	0.3099	0.9632
	分蘖+拔节 Tillering+ Jointing	LAI = 6.0103×exp^{(-0.010×ExR)}	0.0003	0.2487	0.8406
VAR	分蘖 Tillering	LAI = 1.1333×exp^{(0.0175×VAR)}	0.5511	0.1607	0.1476
	拔节 Jointing	LAI = 1.7101×exp^{(0.0121×VAR)}	0.5481	0.1998	0.3505
	分蘖+拔节 Tillering+ Jointing	LAI = 1.1656×exp^{(0.0174×VAR)}	0.7980	0.1658	0.1306
CON	分蘖 Tillering	LAI = 1.1001×exp^{(0.0093×VAR)}	0.5557	0.5576	0.9824
	拔节 Jointing	LAI = 1.6875×exp^{(0.0063×VAR)}	0.5157	0.2433	0.2776
	分蘖+拔节 Tillering+ Jointing	LAI = 1.1375×exp^{(0.0091×VAR)}	0.7944	0.4407	0.5352
DIS	分蘖 Tillering	LAI = 0.6599×exp^{(0.1963×VAR)}	0.5848	0.3614	0.6211
	拔节 Jointing	LAI = 0.8549×exp^{(0.1812×VAR)}	0.5099	0.2371	0.1936
	分蘖+拔节 Tillering+ Jointing	LAI = 0.5791×exp^{(0.2242×VAR)}	0.8064	0.3781	0.4446

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