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作物学报 ›› 2021, Vol. 47 ›› Issue (10): 2028-2035.doi: 10.3724/SP.J.1006.2021.02077

• 耕作栽培·生理生化 • 上一篇    下一篇

基于作物生长监测诊断仪的双季稻叶干重监测模型

李艳大1,*(), 曹中盛1, 舒时富1, 孙滨峰1, 叶春1, 黄俊宝1, 朱艳2, 田永超2   

  1. 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, 江西南昌 330200
    2南京农业大学 / 国家信息农业工程技术中心, 江苏南京 210095
  • 收稿日期:2020-11-16 接受日期:2021-01-13 出版日期:2021-10-12 网络出版日期:2021-02-19
  • 通讯作者: 李艳大
  • 基金资助:
    国家重点研发计划项目(2016YFD0300608);“万人计划”青年拔尖人才项目;国家自然科学基金项目(31260293);江西省科技计划项目(20182BCB22015);江西省科技计划项目(20202BBFL63044);江西省科技计划项目(20192BBF60052);江西省“双千计划”项目;江西省“远航工程”项目

Model for monitoring leaf dry weight of double cropping rice based on crop growth monitoring and diagnosis apparatus

LI Yan-Da1,*(), CAO Zhong-Sheng1, SHU Shi-Fu1, SUN Bin-Feng1, YE Chun1, HUANG Jun-Bao1, ZHU Yan2, TIAN Yong-Chao2   

  1. 1Institute of Agricultural Engineering, Jiangxi Academy of Agricultural Sciences / Jiangxi Province Engineering Research Center of Intelligent Agricultural Machinery Equipment / Jiangxi Province Engineering Research Center of Information Technology in Agriculture, Nanchang 330200, Jiangxi, China
    2Nanjing Agricultural University / National Engineering and Technology Center for Information Agriculture, Nanjing 210095, Jiangsu, China
  • Received:2020-11-16 Accepted:2021-01-13 Published:2021-10-12 Published online:2021-02-19
  • Contact: LI Yan-Da
  • Supported by:
    National Key Research and Development Program of China(2016YFD0300608);National Program for Support of Top-notch Young Professionals;National Natural Science Foundation of China(31260293);Jiangxi Science and Technology Program(20182BCB22015);Jiangxi Science and Technology Program(20202BBFL63044);Jiangxi Science and Technology Program(20192BBF60052);Jiangxi Province “Double Thousand Plan” Program;Jiangxi Voyage Project

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摘要：

本文旨在验证作物生长监测诊断仪(crop growth monitoring and diagnosis apparatus, CGMD)监测双季稻长势指标的准确性, 建立基于CGMD的双季稻叶干重监测模型。通过实施8个不同早、晚稻品种和4个施氮水平的小区试验, 采用CGMD获取从分蘖期至灌浆期的冠层归一化植被指数(normalized difference vegetation index, NDVI)、差值植被指数(differential vegetation index, DVI)和比值植被指数(ratio vegetation index, RVI), 同步采用高光谱仪(analytical spectral devices field-spec handheld 2, ASD FH2)获取冠层光谱反射率计算NDVI、DVI和RVI; 分析2种光谱仪获取的植被指数间的相关关系, 验证CGMD的测量精度, 建立基于CGMD的叶干重监测模型, 并用独立试验数据对模型进行检验。结果表明: 早、晚稻叶干重随施氮水平的增加而增大, 随生育进程的推进呈“低—高—低”动态变化趋势; CGMD与ASD FH2获取的NDVI、DVI和RVI呈极显著相关, 相关系数(correlation coefficient, r)分别为0.9535~0.9972、0.9099~0.9948和0.9298~0.9926, 表明2种光谱仪获取的植被指数具有高度的一致性, CGMD可替代价格昂贵的ASD FH2获取NDVI、DVI和RVI。CGMD获取的3个植被指数相比, RVICGMD与叶干重的相关性最高; 基于RVICGMD的幂函数模型可准确地监测叶干重, 模型建立的决定系数(determination coefficient, R2)为0.8604~0.9216, 模型检验的均方根误差(root mean square error, RMSE)、相对均方根误差(relative root mean square error, RRMSE)和r分别为12.97~17.87 g m-2、4.88%~16.79%和0.9951~0.9992。与人工采样测定叶干重相比, 利用CGMD可实时准确地获取双季稻叶干重动态变化, 在双季稻长势精确诊断和丰产高效栽培中具有应用价值。

关键词: 双季稻, 叶干重, 作物生长监测诊断仪, 植被指数, 监测模型

Abstract:

The quantitative, convenient and non-destructive monitoring of leaf dry weight (LDW) is critical for precise management in double cropping rice production. The objective of this study is to verify the accuracy of crop growth monitoring and diagnosis apparatus (CGMD, a passive multi-spectral sensor containing 810 nm and 720 nm wavelengths) in monitoring growth index of double cropping rice, and establish the model for monitoring LDW of double cropping rice based on CGMD. Plot experiments were conducted in Jiangxi province in 2016 and 2017, including eight early and late rice cultivars and four nitrogen application rates. The normalized difference vegetation index (NDVI), differential vegetation index (DVI), and ratio vegetation index (RVI) were measured at tillering, jointing, booting, heading and filling stages with two spectrometers, CGMD and analytical spectral devices field-spec handheld 2 (ASD FH2, a passive hyper-spectral sensor containing 325 nm to 1075 nm wavelengths). In order to verify the measurement precision of CGMD, the correlation relationship of vegetation indices between CGMD and ASD FH2 was analyzed. The LDW monitoring models of double cropping rice were established based on CGMD from an experimental dataset and then validated using an independent dataset involving different early and late rice cultivars and nitrogen application rates. The results indicated that the LDW of early and late rice were increased with the increase of nitrogen application rate at different growth stages, and exhibited “low-high-low” dynamic variation trend with early and late rice development progress. The NDVI, DVI, and RVI from CGMD and ASD FH2 were significantly correlation. The correlation coefficient (r) of NDVI, DVI, and RVI from CGMD and ASD FH2 were 0.9535-0.9972, 0.9099-0.9948, and 0.9298-0.9926, respectively. This result indicated that there was highly consistent of vegetation indices from CGMD and ASD FH2, and the CGMD could replace expensive ASD FH2 to measure NDVI, DVI and RVI. Compared with the three vegetation indices based on CGMD, the correlation between RVICGMD and LDW was the highest. The power function model based on RVICGMD could accurate monitoring LDW with a determination coefficient (R2) in the range of 0.8604-0.9216, the root mean square error (RMSE), relative root mean square error (RRMSE), and r of model validation in the range of 12.97-17.87 g m-2, 4.88%-16.79%, and 0.9951-0.9992, respectively. Compared with the manual sampling measure LDW, CGMD method can timely and accurately measure the LDW dynamic variation of double cropping rice, which had a potential to be widely applied for growth precision diagnosis and high yield and high efficiency cultivation in double cropping rice production.

Key words: double cropping rice, leaf dry weight, crop growth monitoring and diagnosis apparatus, vegetation index, monitoring model

图1

不同生育期、施氮水平下的早、晚稻叶干重变化特征 TS: 分蘖期; JS: 拔节期; BS: 孕穗期; HS: 抽穗期; FS: 灌浆期。C1: 中嘉早17; C2: 潭两优83; C3: 天优华占; C4: 岳优9113。N0: 0 kg hm-2; N1: 早稻75 kg hm-2, 晚稻90 kg hm-2; N2: 早稻150 kg hm-2, 晚稻180 kg hm-2; N3: 早稻225 kg hm-2, 晚稻270 kg hm-2。"

表1

不同生育期早、晚稻CGMD植被指数与ASD FH2植被指数间的差异显著性概率P和相关关系"

作物
Crop		 生育期
Growth stages		 归一化植被指数 NDVI 差值植被指数 DVI 比值植被指数 RVI
差异显著性概率P 相关系数
r		 差异显著性概率P 相关系数
r		 差异显著性概率P 相关系数
r
早稻 分蘖期 Tillering stage 0.60 0.9675** 0.49 0.9099** 0.34 0.9298**
Early rice 拔节期 Jointing stage 0.55 0.9960** 0.61 0.9870** 0.42 0.9320**
孕穗期 Booting stage 0.46 0.9613** 0.87 0.9948** 0.54 0.9894**
抽穗期 Heading stage 0.54 0.9820** 0.69 0.9799** 0.38 0.9926**
灌浆期 Filling stage 0.48 0.9654** 0.50 0.9779** 0.58 0.9543**
晚稻 分蘖期 Tillering stage 0.91 0.9550** 0.30 0.9387** 0.93 0.9807**
Late rice 拔节期 Jointing stage 0.82 0.9972** 0.22 0.9750** 0.89 0.9703**
孕穗期 Booting stage 0.83 0.9535** 0.81 0.9772** 0.94 0.9899**
抽穗期 Heading stage 0.94 0.9711** 0.78 0.9751** 0.86 0.9438**
灌浆期 Filling stage 0.79 0.9880** 0.84 0.9732** 0.61 0.9823**

表2

基于CGMD植被指数的不同生育期早、晚稻叶干重监测模型"

植被指数
Vegetation index		 生育期
Growth stages		 早稻 Early rice 晚稻 Late rice
监测模型 Monitoring model 决定系数 R2 监测模型 Monitoring model 决定系数 R2
NDVICGMD 分蘖期 Tillering stage $\text{LDW}=\text{32}\text{.173}\times {{\text{e}}^{\text{5}\text{.8687}\times \text{NDV}{{\text{I}}_{\text{CGMD}}}}}$ 0.8304 $\text{LDW}=\text{54}\text{.595}\times {{\text{e}}^{\text{4}\text{.3443}\times \text{NDV}{{\text{I}}_{\text{CGMD}}}}}$ 0.8932
拔节期 Jointing stage $\text{LDW}=\text{46}\text{.779}\times {{\text{e}}^{\text{4}\text{.9717}\times \text{NDV}{{\text{I}}_{\text{CGMD}}}}}$ 0.8413 $\text{LDW}=\text{71}\text{.407}\times {{\text{e}}^{\text{4}\text{.4888}\times \text{NDV}{{\text{I}}_{\text{CGMD}}}}}$ 0.8451
孕穗期 Booting stage $\text{LDW}=\text{68}\text{.835}\times {{\text{e}}^{\text{3}\text{.9738}\times \text{NDV}{{\text{I}}_{\text{CGMD}}}}}$ 0.8291 $\text{LDW}=\text{69}\text{.912}\times {{\text{e}}^{\text{5}\text{.0304}\times \text{NDV}{{\text{I}}_{\text{CGMD}}}}}$ 0.8782
抽穗期 Heading stage $\text{LDW}=\text{65}\text{.956}\times {{\text{e}}^{\text{3}\text{.0992}\times \text{NDV}{{\text{I}}_{\text{CGMD}}}}}$ 0.8235 $\text{LDW}=\text{56}\text{.463}\times {{\text{e}}^{\text{5}\text{.7332}\times \text{NDV}{{\text{I}}_{\text{CGMD}}}}}$ 0.8775
灌浆期 Filling stage $\text{LDW}=\text{47}\text{.316}\times {{\text{e}}^{\text{4}\text{.5720}\times \text{NDV}{{\text{I}}_{\text{CGMD}}}}}$ 0.8465 $\text{LDW}=\text{61}\text{.509}\times {{\text{e}}^{\text{5}\text{.0302}\times \text{NDV}{{\text{I}}_{\text{CGMD}}}}}$ 0.8303
DVICGMD 分蘖期 Tillering stage LDW=1512.80×DVICGMD +38.511 0.8312 LDW=785.22×DVICGMD +61.521 0.8197
拔节期 Jointing stage LDW=867.63×DVICGMD +54.730 0.8178 LDW=912.36×DVICGMD +91.027 0.8162
孕穗期 Booting stage LDW=712.08×DVICGMD +94.664 0.8267 LDW=717.89×DVICGMD +114.750 0.8379
抽穗期 Heading stage LDW=546.71×DVICGMD +74.901 0.8190 LDW=755.20×DVICGMD +92.275 0.8230
灌浆期 Filling stage LDW=956.46×DVICGMD +37.730 0.8445 LDW=770.92×DVICGMD +77.178 0.8244
RVICGMD 分蘖期 Tillering stage LDW =39.217×RVICGMD1.9585 0.8697 LDW =50.006×RVICGMD2.1792 0.9216
拔节期 Jointing stage LDW =52.865×RVICGMD1.8850 0.8892 LDW =64.510×RVICGMD2.3807 0.9045
孕穗期 Booting stage LDW =70.446×RVICGMD1.4743 0.8758 LDW =66.149×RVICGMD2.2845 0.9118
抽穗期 Heading stage LDW =51.792×RVICGMD1.6805 0.8604 LDW =59.700×RVICGMD2.3213 0.8851
灌浆期 Filling stage LDW =41.993×RVICGMD2.4764 0.8793 LDW =56.741×RVICGMD2.6011 0.8854

图2

基于RVICGMD的早、晚稻叶干重观测值与预测值的比较 ER: 早稻; LR: 晚稻; 缩略词同图1。"

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