Abstract：Environmental stress of light and temperature is a major restricting factor that affects the quality and yield of crops. Traditional crop stress monitoring is insufficiently sensitive, time-consuming and laborious, and mostly destructive testing. In recent years, with the rapid development of information technology, hyperspectral technology can quickly and non-destructively obtain crop physiological information, and dynamically monitor the response to adversity, providing digital support for the precision production and intelligent decision-making of modern agriculture, and is of great significance for realizing the transformation of traditional agriculture to precision and modern digital agriculture. This paper takes the corn seedling stage as the research object, obtains the hyperspectral data and physiological parameters of leaves under different light and temperature environments, explores the response law of corn leaves to different light and temperature environments, conducts hyperspectral difference analysis, and construct physiological parameters Hyperspectral inversion model. The correlation analysis method is used to screen the spectral sensitive band. The preprocessing method combining Multivariate Scattering Correction (MSC), Standard Normal Variable transformation (SNV), and Savitzky-Golay (SG) smoothing is used, respectively. Partial Least Square regression (PLS), Principal Component Regression (PCR), Stepwise Multiple Linear Regression (SMLR) three modeling methods combination, the model correlation coefficient and root mean square error are used as model effect evaluation indicators to explore the optimal method of hyperspectral inversion of leaf physiological parameter models. The results show that the hyperspectral characteristics of corn under different light and temperature environments have the same changing trend as a whole, but there are still differences. The reflectance of the spectrum in the 500～700 nm band gradually increases with the increase of light intensity, the reflectivity of the spectrum in the 760～900 nm band gradually increases with the increase of temperature, and the changes of the light and temperature stress environment can be reflected in the hyperspectral characteristics. The spectral reflectance in the 760～900 nm band is relatively high in a high temperature stress environment, the spectral reflectance is low in a low light stress environment, and the reflectance is significantly reduced in a low temperature stress environment. The optimal combination of SPAD and Fv/Fm inversion model is PLS-MSC-SG, the correlation coefficients of the model validation set are 0.958 and 0.976, and the correlation coefficients of the training set are 0.979 and 0.995, respectively. The model’s predictive accuracy is high, which indicates that the use of hyperspectral technology can realize quantitative monitoring of maize plants under light and temperature environmental stresses, improve the level of refined management in the field, and provide a reference for the intelligent management of high-quality and high-yield maize.
陈美辰，于海业，李晓凯，王洪健，刘 爽，孔丽娟，张 蕾，党敬民，隋媛媛. 不同光温环境下玉米苗期叶片的高光谱特性响应分析[J]. 光谱学与光谱分析, 2021, 41(11): 3545-3551.
CHEN Mei-chen, YU Hai-ye, LI Xiao-kai, WANG Hong-jian, LIU Shuang, KONG Li-juan, ZHANG Lei, DANG Jing-min, SUI Yuan-yuan. Response Analysis of Hyperspectral Characteristics of Maize Seedling Leaves Under Different Light and Temperature Environment. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3545-3551.
[1] WANG Ji-zhang, HE Tong, ZHOU Jing, et al(王纪章，贺 通，周 静，等). Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(S1): 347.
[2] Dusenge M E, Duarte A G, Way D A. The New Phytologist, 2019, 221(1): 32.
[3] Mo S, Qian Y, Zhang W, et al. Protoplasma, 2021, 258(3): 477.
[4] Batava Y V, Kaznina N M, Titov A F. Biology Bulletin of the Russian Academy of Sciences, 2021, 48(2): 156.
[5] WANG Qun, ZHAO Xiang-yang, LIU Dong-yao, et al(王 群, 赵向阳, 刘东尧, 等). Chinese Agricultural Sciences(中国农业科学), 2020, 53(17): 3479.
[6] FANG Fang, HE Xu-chen, ZHANG Zhi-hao, et al(方 芳, 何序晨, 张志豪, 等). Journal of Zhejiang Agriculture(浙江农业学报), 2019, 31(7): 1045.
[7] LIN Song-ming, MENG Wei-wei, NAN Zhen-wu, et al(林松明, 孟维伟, 南镇武, 等). Chinese Journal of Eco-Agriculture(中国生态农业学报), 2020, 28(1): 31.
[8] Wang Y, Wang C, Zhang Q. Theoretical and Applied Climatology, 2021, 143(3): 1017.
[9] YE Man-zhu, LIAO Shi-fang(叶满珠, 廖世芳). Agricultural Engineering(农业工程), 2018, 8(10): 38.
[10] FENG Wei, WANG Xiao-yu, SONG Xiao, et al(冯 伟，王晓宇，宋 晓，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2013，29(13)：114.
[11] LIU Shuang, YU Hai-ye, CHEN Mei-chen, et al(刘 爽, 于海业, 陈美辰, 等). Spectros copy and Spectral Analysis(光谱学与光谱分析), 2020, 40(5): 1575.
[12] Liu C, Hu Z, Kong R, et al. Iternational Journal of Remote Sensing, 2021, 42(3): 1035.
[13] XU Lin-yu, LIU Shou-zan, BAI Yan, et al(徐琳煜，刘守赞，白 岩，等). Chinese Journal of Eco-Agriculture(中国生态农业学报)，2018，26(5)：2222.
[14] HE Jia, LIU Bing-feng, GUO Yan, et al(贺 佳，刘冰峰，郭 燕，等). Journal of Plant Nutrition and Fertilizer(植物营养与肥料学报)，2017，23(2)：313.
[15] Brestic M, Zivcak M, Kunderlikova K, et al. Photosynthesis Research, 2016, 130(1): 251.
[16] MENG Qing-long, ZHANG Yan, SHANG Jing(孟庆龙, 张 艳, 尚 静). Optoelectronics·Laser(光电子·激光), 2019, 30(3): 266.
[17] CHEN Bing, WANG Ke-ru, LI Shao-kun, et al(陈 兵, 王克如, 李少昆, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2011, 1(9): 86.
