Prediction of Leaf Nitrogen Content of Rice in Cold Region Based on Spectral Reflectance
LI Hong-yu1, 2, 3, GAO Zheng-wu1, 2, 3, WANG Zhi-jun4, LIN Tian1, 2, 3, ZHAO Hai-cheng1, 2, 3, FAN Ming-yu1, 2, 3
1. Crop Department College of Agriculture, Heilongjiang Bayi Agricultural University, Daqing 163319, China
2. Key Laboratory of Low-carbon Green Agriculture in Northeastern China, Ministry of Agriculture and Rural Affairs, Daqing 163319, China
3. Heilongjiang Provincial Key Laboratory of Modern Agricultural Cultivation and Crop Germplasm Improvement, Heilongjiang Bayi Agricultural University, Daqing 163319, China
4. Qiqihar Branch, Heilongjiang Academy of Agricultural Sciences, Qiqihar 161006, China
Abstract: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 L1, L2 and L3, respectively) at the main growth stages of rice in cold region (T1 mid-spike differentiation stage, T2 jointing stage, T3 booting stage, T4 full heading stage and T5 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 L1 leaves of different varieties decreased first and then increased, and the reflectance of L2 and L3 leaves increased all the time. The sensitive bands 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 early stage of growth, but it was the opposite in the later stage of growth. The L2 leaf index FD-NDNI in the T1 period, L2 leaf index GM2 in the T2 period, L2 leaf index Lic2 in the T3 period, L1 leaf index MRESRI in the T4 period, and L1 leaf index Ctr1 in the T5 period were selected as the best indexes to predict leaf nitrogen content in different periods. The regression equations R2 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 L2 leaf index Lic2 at the T3 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.
Key words:Rice in cold region; Reflectance; Leaf nitrogen content; Spectroscopic indices; Prediction model
李红宇,高正武,王志君,林 添,赵海成,范名宇. 基于光谱反射率的寒地水稻叶片氮含量预测[J]. 光谱学与光谱分析, 2024, 44(09): 2582-2593.
LI Hong-yu, GAO Zheng-wu, WANG Zhi-jun, LIN Tian, ZHAO Hai-cheng, FAN Ming-yu. Prediction of Leaf Nitrogen Content of Rice in Cold Region Based on Spectral Reflectance. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(09): 2582-2593.
[1] WANG Shu-wen, ZHAO Yue, WANG Li-feng, et al(王树文, 赵 越, 王丽凤, 等) . Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(20): 187.
[2] Fu Yuanyuan, Yang Guijun, Pu Ruiliang, et al. European Journal of Agronomy, 2021, 124: 126241.
[3] WANG Lei, LU Yan-li, BAI You-lu(王 磊, 卢艳丽, 白由路). Journal of Plant Nutrition and Fertilizers(植物营养与肥料学报), 2022, 28(3): 546.
[4] Lumme J, Karjalainen M, Kaartinen H, et al. International Journal of Remote Sensing, 2008, 26(7): 563.
[5] Mckinion J M, Willers J L, Jenkins J N. Computers and Electronics in Agriculture, 2010, 74(2): 244.
[6] YU Feng-hua, ZHANG Hong-gang, JIN Zhong-yu, et al(于丰华, 张鸿刚, 金忠煜, 等). Journal of Shenyang Agricultural University(沈阳农业大学学报), 2023, 54(2): 248.
[7] Interpreters S. Science, 2013, 118(3077): 13.
[8] Liu J G, Pattey E, Miller J R, et al. Remote Sensing of Environment, 2010, 114(6): 1167.
[9] WANG Zhi-jun, LI Hong-yu, XIA Yu-ying, et al(王志君, 李红宇, 夏玉莹, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2022, 38(21): 147.
[10] ZHANG Xiao, YAN Yan, WANG Wen-hui, et al(张 骁, 闫 岩, 王文辉, 等). Acta Agronomica Sinica(作物学报), 2021, 47(8): 1563.
[11] Zha H, Miao Y, Wang T, et al. Remote Sensing, 2020, 12(2): 215.
[12] Qiu Z, Ma F, Li Z, et al. Computers and Electronics in Agriculture, 2021,189: 106421.
[13] XU Hao-cong, YAO Bo, WANG Quan(徐浩聪,姚 波,王 权). Scientia Agricultura Sinica(中国农业科学), 2021, 54(21): 4525.
[14] Ata-UI-Karim S T, Yao X, Liu X J, et al. Field Crops Research, 2013, 149: 149.
[15] WANG Hai-peng, CHU Xiao-li, CHEN Pu, et al(王海朋, 褚小立, 陈 瀑, 等). Chinese Journal of Analytical Chemistry(分析化学), 2021, 49(8): 1270.
[16] YANG Xi-lai, ZHU Liu-jun, FENG Zhao-zhong(杨熙来,朱榴骏,冯兆忠) . Acta Ecologica Sinica(生态学报), 2023, 43(8): 3213.
[17] LI Fang-liang, KONG Qing-bo, ZHANG Qing, et al(栗方亮, 孔庆波, 张 青, 等). Journal of Fruit Science(果树学报), 2022, 39(5): 882.
[18] Zhao Y, Yan C, Lu S, et al. Ecological Indicators, 2019,42(11):3152.
[19] XIAO Tian-hao, FAN Yuan-yuan, FENG Hai-kuan, et al(肖天豪, 范园园, 冯海宽, 等). Remote Sensing Information(遥感信息), 2022, 37(3): 7.
[20] CHEN Chun-ling, ZHOU Chang-xian, YU Feng-hua, et al(陈春玲, 周长献, 于丰华, 等). Journal of Shenyang Agricultural University(沈阳农业大学学报), 2020,51(2):218.
[21] REN Wei-chen, CHANG Qing-xia, ZHANG Ya-jun, et al(任维晨, 常庆霞, 张亚军, 等). Chinese Journal of Rice Science(中国水稻科学), 2022, 36(6): 586.
[22] GUO Jian-hua, ZHAO Chun-jiang, WANG Xiu, et al(郭建华, 赵春江, 王 秀, 等). Soil and Fertilizer Sciences in China(中国土壤与肥料), 2008, 216(4): 10.
[23] ZHANG Jin-heng, WANG Ke, WANG Ren-chao, et al(张金恒, 王 珂, 王人潮, 等). Journal of Zhejiang University (Agric. &Life Sci.)[浙江大学学报(农业与生命科学版)], 2004, 30(3): 340.
[24] TIAN Yong-chao, YANG Jie, YAO Xia, et al(田永超, 杨 杰, 姚 霞, 等). Chinese Journal of Applied Ecology(应用生态学报),2010, 21(4): 966.
[25] Tan K, Wang S, Song Y, et al. Chemometrics and Intelligent Laboratory Systems, 2017, 172: 68.
[26] Ranjan R, Chopra U K, Sahoo R N, et al. International Journal of Remote Sensing, 2012, 33(20): 6342.
[27] FAN Yi-guang, FENG Hai-kuan, LIU Yang, et al(樊意广, 冯海宽, 刘 杨, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2023, 43(5): 1524.
[28] WANG Yu-na, LI Fen-ling, WANG Wei-dong, et al(王玉娜, 李粉玲, 王伟东, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2020, 36(22): 31.
[29] YU Feng-hua, XING Si-min, GUO Zhong-hui, et al(于丰华, 邢思敏, 郭忠辉, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2022, 38(2): 175.
[30] TIAN Yong-chao, YANG Jie, YAO Xia, et al(田永超, 杨 杰, 姚 霞, 等). Acta Agronomica Sinica(作物学报), 2010, 36(9): 1529.
[31] XU Hao-cong, YAO Bo, WANG Quan, et al(徐浩聪, 姚 波, 王 权, 等). Scientia Agricultura Sinica(中国农业科学), 2021, 54(21): 4525.
[32] Tian Y, Gu K, Chu X, et al. Plant and Soil, 2014, 376(1-2): 193.
[33] Chu X, Guo Y, He J, et al. Agronomy Journal, 2014, 106(5): 1911.