A Model for Predicting Early Spot Disease of Maize Based on Fluorescence Spectral Analysis
WANG Hong-jian1, YU Hai-ye1, GAO Shan-yun1, LI Jin-quan1, LIU Guo-hong1, YU Yue1, LI Xiao-kai1, ZHANG Lei1, ZHANG Xin1, LU Ri-feng2, SUI Yuan-yuan1*
1. College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
2. School of Public Health, Jilin University, Changchun 130021, China
Abstract：Spot disease is a common foliar disease with outbreaks in maize production areas worldwide, seriously affecting maize yield and quality. Fluorescence spectroscopy can reflect the physiological information of crops quickly and accurately without loss, and dynamically detect its response pattern to adversity. In this study, we investigated the response patterns of maize physiological parameters to different degrees of spot diseases based on the fusion analysis of fluorescence spectra and physiological parameters (SPAD and Fv/Fm) and constructed a fluorescence spectral inversion model. Firstly, the sensitive bands of fluorescence spectra were screened by correlation analysis and peak analysis, and multivariate scattering correction (MSC), standard normal variable transformation (SNV), polynomial smoothing (Smoothing), and the inversion model were used. Savitzky-Golaay (S-G), FD spectral first-order derivative, SD spectral second-order derivative, and four modeling combinations such as MSC-SG-FD, MSC-FD-SG, SNV-SG-FD, SNV-SG-SD, etc. The correlation coefficient R2 and the root mean square error RMSE were used as the evaluation indexes to determine the optimal method for fluorescence spectral inversion. The results showed that modeling the different spot disease levels was not as effective as modeling the physiological parameters. The results showed that the overall trend of fluorescence spectral properties under different spot disease degrees was consistent, but the intensity varied significantly, and the spectral reflectance would show an obvious peak center and reach the extreme value in the band 600.000～800.000 nm. After the band 900.000 nm, the reflectance leveled off and the features decreased significantly. For latent phase leaves, the modeling optimal method for both SPAD and Fv/Fm is SNV-SG-FD with Rc of 0.985 2 and 0.976 8 and RMSEP of 1.59 and 0.015 0. For early onset leaves, the modeling optimal method for SPAD is SNV-SG-FD with Rc of 0.949 7 and RMSEP of 3.79, and the Fv/Fm The modeling optimal method was SNV-SG-SD with Rc of 0.943 8 and RMSEP of 0.011 7. The high predictive accuracy of the model indicates that accurate prediction of SPAD and Fv/Fm for early spot diseased maize leaves can be achieved, providing a reference basis for monitoring physiological information during the latent and early disease stages of maize spot disease. The results of this paper can be applied to field operations, which improves the level of fine and intelligent management in the field and provides the theoretical basis and technical support for high yield, high quality and eugenics of maize.
王洪健，于海业，高山云，李金权，刘国鸿，于 跃，李晓凯，张 蕾，张 昕，卢日峰，隋媛媛. 基于荧光光谱分析的玉米早期斑病害预测模型[J]. 光谱学与光谱分析, 2023, 43(12): 3710-3718.
WANG Hong-jian, YU Hai-ye, GAO Shan-yun, LI Jin-quan, LIU Guo-hong, YU Yue, LI Xiao-kai, ZHANG Lei, ZHANG Xin, LU Ri-feng, SUI Yuan-yuan. A Model for Predicting Early Spot Disease of Maize Based on Fluorescence Spectral Analysis. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3710-3718.
[1] Strange R N, Scott P R. Annual Review of Phytopathology, 2005, 43(1): 83.
[2] Mubeen S, Rafique M, Munis M F H, et al. Journal of the Saudi Society of Agricultural Sciences, 2017, 16(3): 210.
[3] Haboudane D, Miller J R, Pattey E, et al. Remote Sensing of Environment, 2004, 90(3): 337.
[4] Lee W-H, Kim M S, Lee H, et al. Journal of Food Engineering, 2014, 130: 1.
[5] Sánchez J F, Quiles M J. Journal of Biological Education, 2006, 41(1): 34.
[6] Graeff S, Link J, Claupein W. Central European Journal of Biology, 2006, 1(2): 275.
[7] Ashourloo D, Mobasheri M R, Huete A. Remote Sensing (Basel, Switzerland), 2014, 6(6): 4723.
[8] Golhani K, Balasundram S K, Vadamalai G, et al. Journal of the Indian Society of Remote Sensing, 2019, 47(4): 639.
[9] Izzuddin M A, Seman Idris A, Nisfariza M N, et al. International Journal of Remote Sensing, 2017, 38(23): 6505.
[10] YANG Hao-yu, YU Hai-ye, ZHANG Lei, et al(杨昊谕, 于海业, 张 蕾, 等). Transactions of the Chinese Society of Agricultural Machinery(农业机械学报), 2009, 40(10): 169.
[11] YANG Yan-yang, CHEN Bin, CAI Gui-min, et al(杨艳阳, 陈 斌, 蔡贵民, 等). Jiangsu Agricultural Sciences (江苏农业科学), 2012, 40(5): 270.
[12] Bassanezi R B, Amorim L, Filho A B, et al. Journal of Phytopathology, 2002, 150(1): 37.
[13] Cherif J, Derbel N, Nakkach M, et al. Journal of Photochemistry and Photobiology B: Biology, 2010, 101(3): 332.
[14] Mandai K, Saravanan R, Mait S, et al. Journal of Plant Diseases and Protection (2006), 2009, 116(4): 164.
[15] CHEN Mei-chen, YU Hai-ye, LI Xiao-kai, et al(陈美辰, 于海业, 李晓凯, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2021, 41(11): 3545.
[16] Björkman O, Demmig B. Planta, 1987, 170(4): 489.
[17] Brestic M, Zivcak M, Kunderlikova K, et al. Photosynthesis Research, 2016, 130(1-3): 251.
