Nitrogen Nutrition Diagnostic Based on Hyperspectral Analysis about Different Layers Leaves in Maize
ZHANG Yin-jie, WANG Lei*，BAI You-lu, YANG Li-ping, LU Yan-li, ZHANG Jing-jing, LI Ge
Key Laboratory of Plant Nutrition and Fertilizer, Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Abstract：In order to clarify the location of nitrogen nutrient diagnosis of maize leaves at different growth stages, and to establish an accurate and robust model to diagnose maize’s nitrogen nutrition, which aims to guide rational fertilization and improve recovery rate, in this experiment, a single factor pot experiment was designed, and maize (Zhengdan 958) was used as the research object to study the distribution and variation of nitrogen content in different layers of leaf under different nitrogen nutrition levels. The distribution and variation of N content and the spectral response characteristics of maize leaves were analyzed. And the correlation relationship between nitrogen content and spectral reflectance of different layers leaves at different growth stages was investigated. Moreover, the regression relationship between the leaf nitrogen content and the ratio spectral index (RSI) which was composed of any two bands between 400～2 000 nm was explored. According to these analyses, leaf layer, optimal RSI and estimation models were initially determined at different stages for nitrogen nutrition diagnosis by spectral technique. The main results are as follows: The results indicate that the maize’s nitrogen content in different layers is as follows: the upper layer>the middle layer>the lower layer; and that as the stages of growth forward, leaves’ nitrogen content in upper layer, under the condition of low-nitrogen, appears to first decrease and then increase (after manuring) and decrease again while keeping the tendency of decrease under the condition of high-nitrogen, with the leaves’ nitrogen content in the levels of middle and low appearing to decrease. At the Six-leaf stage, the lower layer of leaves has a larger sensitivity range and a stronger correlation coefficient. At Nine-leaf and Filling stage, spectral reflectance of the upper layer maize leaves was more sensitive and correlated. At the flowering and silking stage, spectral reflectance of the middle layer leaves was more sensitive and relevant. SO the lower leaves were selected as the diagnosis target at the Six-leaf stage, and the optimal ratio spectral index RSI (1 811, 1 842) was selected to establish the linear estimation model. The upper leaves were selected as the diagnostic target at the Nine-leaf stage and the Filling stage, and the optimal ratio spectral indices were RSI (720, 557), RSI (600, 511) to establish the linear estimation model, respectively. The middle leaves were selected as the diagnostic target during the anthesis-silking stage, and the RSI (688, 644) spectral index was selected to establish the estimation model. The research results could provide a theoretical basis for rapid and accurate nitrogen nutrition spectrum diagnosis method in maize or other crop.
张银杰，王 磊，白由路，杨俐苹，卢艳丽，张静静，李 格. 基于高光谱分析的玉米叶片氮含量分层诊断研究[J]. 光谱学与光谱分析, 2019, 39(09): 2829-2835.
ZHANG Yin-jie, WANG Lei，BAI You-lu, YANG Li-ping, LU Yan-li, ZHANG Jing-jing, LI Ge. Nitrogen Nutrition Diagnostic Based on Hyperspectral Analysis about Different Layers Leaves in Maize. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(09): 2829-2835.
[1] GUO Chao-fan, DUAN Fu-zhou, GUO Xiao-yu, et al(郭超凡, 段福洲, 郭逍宇, 等). Acta Ecologica Sinica(生态学报), 2014, 34(17): 4839.
[2] WANG Ren-hong, SONG Xiao-yu, LI Zhen-hai, et al(王仁红, 宋晓宇, 李振海, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2014, (19): 191.
[3] Inoue Y, Sakaiya E, Zhu Y, et al. Remote Sensing of Environment, 2012, 126(6829): 210.
[4] LIAO Qin-hong, LI Hui-he, ZHANG Qin, et al(廖钦洪, 李会合, 张 琴, 等). Research of Agricultural Modernization(农业现代化研究), 2017, 38(2): 315.
[5] Wang Wei, Yao Xia, Tian Yongchao, et al. Journal of Integrative Agriculture, 2012, 11(12): 2001.
[6] LI Yuan-yuan, CHANG Qing-rui, LIU Xiu-ying, et al(李媛媛, 常庆瑞, 刘秀英, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(16): 135.
[7] ZHANG Xiao-yuan, ZHANG Li-fu, ZHANG Xia, et al(张潇元, 张立福, 张 霞, 等). Scientia Agricultura Sinica(中国农业科学), 2017, 50(3): 474.
[8] Jin Xiuliang, Xu Xingang, Feng Haikun, et al. International Journal of Agriculture and Biology, 2014, 16(3): 498.
[9] JIN Ji-yun, BAI You-lu，YANG Li-ping(金继运, 白由路, 杨俐苹). High Efficient Technology and Equipment for Soil Testing(高效土壤养分测试技术与设备). Beijing: China Agriculture Press(北京：中国农业出版社), 2006. 148.
[10] LIU Xiao-jun, TIAN Yong-chao, YAO Xia, et al(刘小军, 田永超, 姚 霞, 等). Scientia Agricultura Sinica(中国农业科学), 2012, 45(3): 435.
