基于作物生长监测诊断仪的双季稻叶片氮含量和氮积累量监测

doi:10.13287/j.1001-9332.202009.012

应用生态学报 ›› 2020, Vol. 31 ›› Issue (9): 3040-3050.doi: 10.13287/j.1001-9332.202009.012

基于作物生长监测诊断仪的双季稻叶片氮含量和氮积累量监测

李艳大^1*, 叶春¹, 曹中盛¹, 孙滨峰¹, 舒时富¹, 黄俊宝¹, 田永超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:2020-03-17 接受日期:2020-06-29 出版日期:2020-09-15 发布日期:2021-03-15
通讯作者: * E-mail: liyanda2008@126.com
作者简介:李艳大, 男, 1980年生, 研究员。主要从事信息农学与农机化技术研究。E-mail: liyanda2008@126.com
基金资助:
国家重点研发计划项目(2016YFD0300608)、江西省科技计划项目(20182BCB22015,20161BBI90012,20181BCD40011)、国家青年拔尖人才支持计划项目、江西省“双千计划”项目和江西省“远航工程”项目资助

Monitoring leaf nitrogen concentration and nitrogen accumulation of double cropping rice based on crop growth monitoring and diagnosis apparatus

LI Yan-da^1*, YE Chun¹, CAO Zhong-sheng¹, SUN Bin-feng¹, SHU Shi-fu¹, HUANG Jun-bao¹, TIAN Yong-chao², HE Yong³

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, 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:2020-03-17 Accepted:2020-06-29 Online:2020-09-15 Published:2021-03-15
Contact: * E-mail: liyanda2008@126.com
Supported by:
the National Key R&D Program of China (2016YFD0300608), the Jiangxi Science and Technology Program (20182BCB22015, 20161BBI90012, 20181BCD40011), the National Program for Top-notch Young Professionals, the Jiangxi Double-Thousand Plan and the Jiangxi Voyage Project.

摘要/Abstract

摘要： 为了验证作物生长监测诊断仪(CGMD)监测双季稻氮素营养指标的准确性和适用性,构建基于CGMD的双季稻叶片氮含量(LNC)和氮积累量(LNA)的监测模型。选用8个不同早、晚稻品种,设置4个不同施氮水平,利用CGMD采集冠层差值植被指数(DVI)、归一化植被指数(NDVI)和比值植被指数(RVI),同步利用ASD FH2高光谱仪采集冠层光谱反射率,并计算DVI、NDVI和RVI;通过比较CGMD和ASD FH2采集的冠层植被指数变化特征,验证CGMD的测量精度,构建基于CGMD的LNC和LNA监测模型,并利用独立试验数据对模型进行检验。结果表明: 早、晚稻LNC、LNA、DVI、NDVI和RVI随施氮水平的增加而增大,随生育进程的推进呈先升后降的趋势;CGMD与ASD FH2采集的DVI、NDVI和RVI间拟合的决定系数(R²)分别为0.9350、0.9436和0.9433,表明CGMD的测量精度较高,可替代ASD FH2采集冠层植被指数。基于CGMD的3个冠层植被指数相比,NDVI_CGMD与LNC的相关性最高,RVI_CGMD与LNA的相关性最高;基于NDVI_CGMD的指数模型可较准确地预测LNC,模型R²为0.8581～0.9318,模型检验的均方根误差(RMSE)、相对均方根误差(RRMSE)和相关系数(r)分别为0.1%～0.2%、4.0%～8.5%和0.9041～0.9854;基于RVI_CGMD的幂函数模型可较准确地预测LNA,模型R²为0.8684～0.9577,模型检验的RMSE、RRMSE和r分别为0.37～0.89 g·m^-2、6.7%～20.4%和0.9191～0.9851。与化学分析方法相比,利用CGMD可便捷准确地获取早、晚稻的LNC和LNA,在双季稻丰产高效栽培和氮肥精确管理中具有应用价值。

关键词: 作物生长监测诊断仪, 双季稻, 叶片氮含量, 叶片氮积累量, 监测模型

Abstract: To verify the accuracy and adaptability of crop growth monitoring and diagnosis apparatus (CGMD) in monitoring nitrogen nutrition index of double cropping rice, we established a monitoring model of leaf nitrogen concentration (LNC) and leaf nitrogen accumulation (LNA) for double cropping rice based on CGMD. Eight early and late rice cultivars were selected and four nitrogen application rates were set up. The differential vegetation index (DVI), normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) were collected using CGMD. Meanwhile, ASD FH2 high spectrometer was used to collect canopy spectral reflectance and calculated DVI, NDVI, and RVI. To verify the accuracy of CGMD, we compared the canopy vegetation indices change characteristics collected by CGMD and ASD FH2. The CGMD-based monitoring models of LNC and LNA were established, which was tested with independent field data. The results showed that LNC, LNA, DVI, NDVI and RVI of early and late rice increased with increasing nitrogen application rate, and increased first and then decreased with the advance of growth progress. The determination coefficient (R²) of fitting for DVI, NDVI and RVI from CGMD and ASD FH2 were 0.9350, 0.9436 and 0.9433, respectively. This result indicated that the measurement accuracy of CGMD was high, and that the CGMD could be used to replace ASD FH2 to measure canopy vegetation indices of early and late rice. Compared with the three canopy vegetation indices based on CGMD, the correlation between NDVI_CGMD and LNC and that between RVI_CGMD and LNA was the highest. The exponential model based on NDVI_CGMD could be used to accurate estimate LNC with the R² in the range of 0.8581-0.9318, and the root mean square error (RMSE), relation root mean square error (RRMSE) and correlation coefficient (r) of model validation in the range of 0.1%-0.2%, 4.0%-8.5%, and 0.9041-0.9854, respectively. The power function model based on RVI_CGMD could be used to estimate LNA with the R² in the range of 0.8684-0.9577, and the RMSE, RRMSE and r of model validation in the range of 0.37-0.89 g·m^-2, 6.7%-20.4% and 0.9191-0.9851, respectively. Compared with the chemical testing method, using the CGMD could conveniently and accurately measure LNC and LNA of early and late rice, which had a potential to be widely applied for high yield and high efficiency cultivation and precise management of nitrogen fertilizer in double cropping rice production.

Key words: crop growth monitoring and diagnosis apparatus, double cropping rice, leaf nitrogen concentration, leaf nitrogen accumulation, monitoring model

李艳大, 叶春, 曹中盛, 孙滨峰, 舒时富, 黄俊宝, 田永超, 何勇. 基于作物生长监测诊断仪的双季稻叶片氮含量和氮积累量监测[J]. 应用生态学报, 2020, 31(9): 3040-3050.

LI Yan-da, YE Chun, CAO Zhong-sheng, SUN Bin-feng, SHU Shi-fu, HUANG Jun-bao, TIAN Yong-chao, HE Yong. Monitoring leaf nitrogen concentration and nitrogen accumulation of double cropping rice based on crop growth monitoring and diagnosis apparatus[J]. Chinese Journal of Applied Ecology, 2020, 31(9): 3040-3050.

参考文献

[1] Zhou ZJ, Plauborg F, Liu FL, et al. Yield and crop growth of table potato affected by different split-N fertigation regimes in sandy soil. European Journal of Agronomy, 2018, 92: 41-50
[2] Lemaire G, Jeuffroy MH, Gastal F. Diagnosis tool for plant and crop N status in vegetative stage: Theory and practices for crop N management. European Journal of Agronomy, 2008, 28: 614-624
[3] Tian YC, Gu KJ, Chu X, et al. Comparison of different hyperspectral vegetation indices for canopy leaf nitrogen concentration estimation in rice. Plant and Soil, 2014, 376: 193-209
[4] Zhou ZJ, Plauborg F, Anton GT, et al. A RVI/LAI-reference curve to detect N stress and guide N fertigation using combined information from spectral reflectance and leaf area measurements in potato. European Journal of Agronomy, 2017, 87: 1-7
[5] 李艳大, 舒时富, 陈立才, 等. 基于便携式作物生长监测诊断仪的江西双季稻氮肥调控研究. 农业工程学报, 2019, 35(2): 100-106 [Li Y-D, Shu S-F, Chen L-C, et al. Regulation of nitrogen fertilizer based on portable apparatus for crop growth monitoring and diagnosis in Jiangxi double cropping rice. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(2): 100-106]
[6] Cheng T, Yang ZW, Inoue Y, et al. Preface: Recent advances in remote sensing for crop growth monitoring. Remote Sensing, 2016, 8: 116-118
[7] Zhou ZJ, Jabloun M, Plauborg F, et al. Using ground-based spectral reflectance sensors and photography to estimate shoot N concentration and dry matter of potato. Computers and Electronics in Agriculture, 2018, 144: 154-163
[8] Shi Y, Huang WJ, Luo JH, et al. Detection and discrimination of pests and diseases in winter wheat based on spectral indices and kernel discriminant analysis. Computers and Electronics in Agriculture, 2017, 141: 171-180
[9] He JY, Zhang XB, Guo WT, et al. Estimation of vertical leaf nitrogen distribution within a rice canopy based on hyperspectral data. Frontiers in Plant Science, 2020, 10: 1-15
[10] Tian YC, Yao X, Yang J, et al. Assessing newly develo-ped and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance. Field Crops Research, 2011, 120: 299-310
[11] 王树文, 赵越, 王丽凤, 等. 基于高光谱的寒地水稻叶片氮素含量预测. 农业工程学报, 2016, 32(20): 187-194 [Wang S-W, Zhao Y, Wang L-F, et al. Prediction for nitrogen content of rice leaves in cold region based on hyperspectrum. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(20): 187-194]
[12] 束美艳, 顾晓鹤, 孙林, 等. 基于新型植被指数的冬小麦LAI 高光谱反演. 中国农业科学, 2018, 51(18): 3486-3496 [Shu M-Y, Gu X-H, Sun L, et al. High spectral inversion of winter wheat LAI based on new vegetation index. Scientia Agricultura Sinica, 2018, 51(18): 3486-3496]
[13] Liu TJ, Xu T, Yao J, et al. Quantitative relationship between leaf area index and canopy reflectance spectra of rice under different nitrogen levels. Agricultural Science & Technology, 2016, 17: 2446-2448
[14] Zou XB, Shi JY, Hao LM, et al. In vivo noninvasive detection of chlorophyll distribution in cucumber (Cucumis sativus) leaves by indices based on hyperspectral imaging. Analytica Chimica Acta, 2011, 706: 105-112
[15] 郑文刚, 孙刚, 申长军, 等. 可见-近红外作物氮素光电测量仪开发. 农业工程学报, 2010, 26(3): 178-182 [Zheng W-G, Sun G, Shen C-J, et al. Development of a visible-infrared photoelectric instrument for measuring crop nitrogen. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(3): 178-182]
[16] 张猛, 孙红, 李民赞, 等. 基于4波段作物光谱测量仪的小麦分蘖数预测. 农业机械学报, 2016, 47(9): 341-347 [Zhang M, Sun H, Li M-Z, et al. Prediction of winter wheat tiller number based on 4-waveband crop monitor with spectral reflectance. Transactions of the Chinese Society for Agricultural Machinery, 2016, 47(9): 341-347]
[17] 倪军, 姚霞, 田永超, 等. 便携式作物生长监测诊断仪的设计与试验. 农业工程学报, 2013, 29(6): 150-156 [Ni J, Yao X, Tian Y-C, et al. Design and experiments of portable apparatus for plant growth monitoring and diagnosis. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(6): 150-156]
[18] Kipp S, Mistele B, Schmidhalter U. The performance of active spectral reflectance sensors as influenced by measuring distance, device temperature and light intensity. Computers and Electronics in Agriculture, 2014, 100: 24-33
[19] 邹应斌. 长江流域双季稻栽培技术发展. 中国农业科学, 2011, 44(2): 254-262 [Zou Y-B. Development of cultivation technology for double cropping rice along the Changjiang river valley. Scientia Agricultura Sinica, 2011, 44(2): 254-262]
[20] 邵华, 石庆华, 郭熙, 等. 基于冠层高光谱的南方丘陵地区晚稻氮素营养诊断. 江西农业大学学报, 2015, 37(6): 975-981 [Shao H, Shi Q-H, Guo X, et al. Diagnosis of nitrogen nutrition of late rice based on canopy hyper-spectrum in hilly areas of Jiangxi province. Acta Agriculturae Universitis Jiangxiensis, 2015, 37(6): 975-981]
[21] 周冬琴, 田永超, 姚霞, 等. 水稻叶片全氮浓度与冠层反射光谱的定量关系. 应用生态学报, 2008, 19(2): 337-344 [Zhou D-Q, Tian Y-C, Yao X, et al. Quantitative relationships between leaf total nitrogen concentration and canopy reflectance spectra of rice. Chinese Journal of Applied Ecology, 2008, 19(2): 337-344]
[22] 周冬琴, 朱艳, 田永超, 等. 以冠层反射光谱监测水稻叶片氮积累量的研究. 作物学报, 2006, 32(9): 1316-1322 [Zhou D-Q, Zhu Y, Tian Y-C, et al. Monitoring leaf nitrogen accumulation with canopy spectral reflectance in rice. Acta Agronomica Sinica, 2006, 32(9): 1316-1322]
[23] 郭建彪, 马新明, 时雷. 冬小麦叶面积指数的品种差异性与高光谱估算研究. 麦类作物学报, 2018, 38(3): 340-347 [Guo J-B, Ma X-M, Shi L, et al. Variety variation and hyperspectral estimate model of leaf area index of winter wheat. Journal of Triticeae Crops, 2018, 38(3): 340-347]
[24] Guo BB, Zhu YJ , Feng W, et al. Remotely estimating aerial N uptake in winter wheat using red-edge area index from multi-angular hyperspectral data. Frontiers in Plant Science, 2018, 9: 1-14
[25] 贺佳, 郭燕, 王利军, 等. 基于作物生长监测诊断仪的玉米LAI监测模型研究. 农业机械学报, 2019, 50(12): 187-194 [He J, Guo Y, Wang L-J, et al. Monitor model of corn leaf area index based on CGMD-402. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(12): 187-194]
[26] 朱艳, 李映雪, 周冬琴, 等. 稻麦叶片氮含量与冠层反射光谱的定量关系. 生态学报, 2006, 26(10): 3463-3469 [Zhu Y, Li Y-X, Zhou D-Q, et al. Quantitative relationship between leaf nitrogen concentration and canopy reflectance spectra in rice and wheat. Acta Ecologica Sinica, 2006, 26(10): 3463-3469]
[27] Zhu Y, Yao X, Tian YC, et al. Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice. International Journal of Applied Earth Observation and Geoinformation, 2008, 10: 1-10

基于作物生长监测诊断仪的双季稻叶片氮含量和氮积累量监测

Monitoring leaf nitrogen concentration and nitrogen accumulation of double cropping rice based on crop growth monitoring and diagnosis apparatus

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	石丽红, 唐海明, 文丽, 程凯凯, 李超, 李微艳, 肖小平. 长期不同施肥模式对南方双季稻田生态系统净碳汇效应和经济收益的影响 [J]. 应用生态学报, 2022, 33(9): 2450-2456.
[2]	林志敏, 李洲, 翁佩莹, 吴冬青, 邹京南, 庞孜钦, 林文雄. 再生稻田温室气体排放特征及碳足迹 [J]. 应用生态学报, 2022, 33(5): 1340-1351.
[3]	王利民, 黄东风, 张秉涯, 潘住财. 不同水肥耦合下双季稻氮磷吸收、利用与流失差异 [J]. 应用生态学报, 2022, 33(4): 1037-1044.
[4]	石丽红, 唐海明, 孙耿, 孙梅, 龙泽东, 文丽, 程凯凯, 罗尊长. 长期不同施肥模式对双季稻田土壤酸解有机氮组分的影响 [J]. 应用生态学报, 2022, 33(12): 3345-3351.
[5]	卜容燕, 李敏, 韩上, 程文龙, 王慧, 孙志祥, 唐杉, 武际. 有机无机肥配施对双季稻轮作系统产量、温室气体排放和土壤养分的综合效应 [J]. 应用生态学报, 2021, 32(1): 145-153.
[6]	周文涛, 龙文飞, 毛燕, 王勃然, 龙攀, 徐莹, 傅志强. 节水轻简栽培模式下增密减氮对双季稻田温室气体排放的影响 [J]. 应用生态学报, 2020, 31(8): 2604-2612.
[7]	马娉, 李如楠, 王斌, 李玉娥, 万运帆, 秦晓波, 刘硕, 高清竹. 双季稻不同生育期净同化速率对大气CO₂浓度和温度升高的响应 [J]. 应用生态学报, 2020, 31(3): 872-882.
[8]	李艳大, 曹中盛, 孙滨峰, 叶春, 舒时富, 黄俊宝, 王康军, 田永超. 江西双季稻氮素监测诊断模型的建立与应用 [J]. 应用生态学报, 2020, 31(2): 433-440.
[9]	龙攀, 苏姗, 黄亚男, 李超, 肖志祥, 祝志娟, 刘莉, 傅志强. 双季稻田冬季种植模式对土壤有机碳和碳库管理指数的影响 [J]. 应用生态学报, 2019, 30(4): 1135-1142.
[10]	柳开楼,韩天富,黄庆海,余喜初,李大明,马常宝,薛彦东,张会民. 鄱阳湖流域长期施肥下双季稻田的土壤基础地力 [J]. 应用生态学报, 2019, 30(1): 209-216.
[11]	杜尧东,沈平,王华,唐湘如,赵华. 气候变化对广东省双季稻种植气候区划的影响 [J]. 应用生态学报, 2018, 29(12): 4013-4021.
[12]	陈中督, 徐春春, 纪龙, 方福平, 陈阜. 2004—2014年南方稻区双季稻生产碳足迹动态及其构成 [J]. 应用生态学报, 2018, 29(11): 3669-3676.
[13]	田昌, 周旋, 刘强, 谢桂先, 荣湘民, 张玉平, 黄思怡, 彭建伟. 控释尿素减施对双季稻田氮素渗漏淋失的影响 [J]. 应用生态学报, 2018, 29(10): 3267-3274.
[14]	李艳大, 舒时富, 陈立才, 叶春, 万鹏, 王康军, 黄俊宝. 双季稻光合生产模型的建立与应用 [J]. 应用生态学报, 2017, 28(4): 1227-1236.
[15]	张剑, 胡亮亮, 任伟征, 郭梁, 吴敏芳, 唐建军, 陈欣. 稻鱼系统中田鱼对资源的利用及对水稻生长的影响 [J]. 应用生态学报, 2017, 28(1): 299-307.