Prediction of Leaf Nitrogen Content of Rice in Cold Region Based on Spectral Reflectance

被引:1
作者
Li, Hong-yu [1 ,2 ,3 ]
Gao, Zheng-wu [1 ,2 ,3 ]
Wang, Zhi-jun [4 ]
Lin, Tian [1 ,2 ]
Zhao, Hai-cheng [1 ,2 ,3 ]
Fan, Ming-yu [1 ,2 ,3 ]
机构
[1] Heilongjiang Bayi Agr Univ, Coll Agr, Crop Dept, Daqing 163319, Peoples R China
[2] Minist Agr & Rural Affairs, Key Lab Low carbon Green Agr Northeastern China, Daqing 163319, Peoples R China
[3] Heilongjiang Bayi Agr Univ, Heilongjiang Prov Key Lab Modern Agr Cultivat & Cr, Daqing 163319, Peoples R China
[4] Heilongjiang Acad Agr Sci, Qiqihar Branch, Qiqihar 161006, Peoples R China
关键词
Rice in cold region; Reflectance; Leaf nitrogen content; Spectroscopic indices; Prediction model; HYPERSPECTRAL VEGETATION INDEXES;
D O I
10.3964/j.issn.1000-0593(2024)09-2582-12
中图分类号
O433 [光谱学];
学科分类号
0703 ; 070302 ;
摘要
In order to realize the real-time prediction of nitrogen content in the rice leaf population by using the rice leaf spectral index, the spectral reflectance of the top three fully expanded leaves (upper 1, upper 2 and upper 3 leaves were recorded as La. 1 and L respectively) at the main growth stages of rice in cold region (T, mid-spike differentiation stage. T, jointing stage. Ta booting stage. T. full heading stage and T. wax ripening stage) under different nitrogen and variety differences in different years were collected. The change rule and the relationship between spectral index and leaf nitrogen content were explored. P-k, Root Mean Square Error (RMSE). Symmetric Mean Absolute Percentage Error (SMAPE). Root Mean Square Error of Calibration (RMSEC), Root Mean Square Error of Interactive Verification (RMSECV) and Residual Prediction Deviation (RPD) were used to verify the accuracy of the model. The results showed that with the increase of nitrogen fertilizer input, the leaf reflectance decreased in the visible region, while the leaf reflectance increased in the near-infrared platform, With the advance of the growth period, in the visible light region, the reflectance of L., leaves of different varieties decreased first and then increased, and the reflectance of 14 and 1 leaves increased all the time. The sensitive hands of leaf nitrogen percentage were 500-550 and 650-700 nm. The correlation analysis of the spectral index and leaf nitrogen percentage content showed that the correlation coefficient of the spectral index of the following leaves was high in the carly stage of growth, but it was the opposite in the later stage of growth. The L. leaf index FD- NDNI in the T, period, Le leaf index GM2 in the Te period, Le leaf index Licz in the T, period. L., leaf index MRESRI in the T, period, and L, leaf index Ctrl in the T, period were selected as the best indexes. to predict leaf nitrogen content in different periods. The regression equations R for predicting leaf nitrogen content were 0.54 0.60 0.66 0.62 and 0.51, respectively, which reached extremely significant levels, The P-k values of the validation indexes were 0.00, 0.04, 0.06, 0.01 and 0.04, respectively. RMSE were 0.39, 0.58. 0.22.0.54, 2.56, SMAPE were 1.11. 1.41. 1.03. 1.64.3.89. RMSEC were 0.17.0.15. 0.13, 0.13, 0.13. RMSECV were 0. 18. 0.14. 0. 12. 0. 12. 0. 14. the RPD were 2. 46. 2. 19. 3. 15. 1.74 and 3. 01. respectively. Among them, the prediction effect of the 14 leaf index Lic? at the T, stage was the best. In summary, with the help of the selected spectral indicators, the nitrogen nutrition status of rice at different growth stages can be predicted quickly, non-destructively, and in real-time, and the sustainable development of high-yield and high-quality cold rice can be promoted.
引用
收藏
页码:2582 / 2593
页数:12
相关论文
共 33 条
  • [1] Development of critical nitrogen dilution curve of Japonica rice in Yangtze River Reaches
    Ata-Ul-Karim, Syed Tahir
    Yao, Xia
    Liu, Xiaojun
    Cao, Weixing
    Zhu, Yan
    [J]. FIELD CROPS RESEARCH, 2013, 149 : 149 - 158
  • [2] auan Fu, 2021, European Journal of Agronomy, V124
  • [3] chao TIAN Yong, 2010, Acta Agronomica Sinica, V36, P1529
  • [4] Chen Chun-Ling, 2020, Journal of Shenyang Agricultural University, V51, P218
  • [5] chen REN Wei, 2022, Chinese Journal of Kice Science, V36, P586
  • [6] Comparison of Different Hyperspectral Vegetation Indices for Estimating Canopy Leaf Nitrogen Accumulation in Rice
    Chu, Xu
    Guo, YongJiu
    He, JiaoYang
    Yao, Xia
    Zhu, Yan
    Cao, WeiXing
    Cheng, Tao
    Tian, YongChao
    [J]. AGRONOMY JOURNAL, 2014, 106 (05) : 1911 - 1920
  • [7] Estimation of Potato Plant Nitrogen Content Based on UAV Hyperspectral Imaging
    Fan Yi-guang
    Feng Hai-kuan
    Liu Yang
    Long Hui-ling
    Yang Gui-jun
    Qian Jian-guo
    [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43 (05) : 1524 - 1531
  • [8] GUO Jian-hua, 2008, Soil and Fertilizer Sciences in China, V216, P10
  • [9] heng ZHANG Jin, 2004, Journal of Zhejiang University, V30, P340
  • [10] hua YU Feng, 2023, Journal of Shenyang Agricultural University, P44