Chen B.,Shihezi University |
Chen B.,Xinjing Academy of Agricultural and Reclamation science |
Li S.,Shihezi University |
Li S.,Chinese Academy of Agricultural Sciences |
And 7 more authors.
International Journal of Remote Sensing | Year: 2012
High spatial or spectral resolution remote sensing might be an efficient method for estimating Verticillium wilt incidence in cotton. The objectives of this study were to characterize leaf spectra and the physiological and biochemical parameters of cotton (Gossypium hirsutum) damaged by Verticillium dahliae Kleb. (simply, Verticillium) to determine the wavelengths of those leaves that were most responsive to cotton with Verticillium and to develop a spectral model to predict the severity levels (SLs) of Verticillium through evaluation of the SLs of cotton leaves with Verticillium at different growth stages using reflectance and the first derivative (FD) spectrum. The study revealed that the values of the physiological and biochemical parameters all gradually decreased with increasing SLs in cotton leaves infected with Verticillium. The spectral characteristics of cotton leaves infected with Verticillium were significant compared to healthy ones. The reflectance of cotton leaves increased with increasing SLs of SLs disease in the range of 400-2500 nm (excluding 700-900 nm). The values of FD spectrum changed significantly at the red edge of the chlorophyll absorption feature (680-740 nm). The wavelength position of the red edge shifted towards shorter wavelengths and the red-edge swing decreased with respect to increasing SLs. From this study, the raw spectral bands of 437-724 and 909-2500 nm and the FD spectra bands of 535-603 and 699-750 nm can be selected as sensitive bands for estimating the SLs of disease in cotton leaves. Inversion models have been established to estimate the SLs of cotton leaves infected with Verticillium. Of all models, the model of R 700nm/R 825nm was superior for quantitatively estimating the disease SLs of cotton leaves infected with Verticillium in practice: its root mean square error (RMSE) was 0.866 and relative error (RE) was only 0.012. Thus, both the selected wavelength ranges and the chosen reflectance models were good indicators of damage caused by Verticillium to cotton leaves. The results provide theoretical support for large-scale monitoring of cotton infected with Verticillium by air- and spaceborne remote sensing. © 2012 Taylor and Francis Group, LLC.
Sui X.,Shandong Academy of Sciences |
Li S.,Key Laboratory of Crop Physiology and Production |
Li S.,Key Laboratory of Oasis Ecology Agriculture of Xinjiang Construction Crops |
Zhang X.,Shandong Academy of Sciences |
And 6 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2010
Changes of leaf thickness can indicate the variations of plant growth state, in order to carry out real-time, live, non-destructive testing of leaf thickness, the study took cotton leaves as the research object. The correlation between plant leaf spectrum and thickness was studied on 84 couples of data of cotton, with DPS and Origin statistical softwares. Studies showed that the correlation between reflectance and leaf thickness showed significantly positive relationship in two visible light regions of 350-369 nm and 664-689 nm, and significantly negative relationship in two infrared regions of 917-1884 nm and 2 048-2 380 nm. In general, the correlation degree between reflectance and leaf thickness in infrared light was higher than that in visible light. Red edge indices showed low correlation with leaf thickness, however 24 figure indices had significant correlation with leaf thickness, and the area of absorbtion with the center of 980 nm had the highest correlation degree with correlation coefficient 0.848. Three models about leaf thickness were set and tested with reflectance, plant index and spectral figure index. Among these models, the highest relative error was 7.4%, and the RMSE was 0.051 mm. It is feasible to measure alive leaf thickness untouchably with hyper spectrum.