Spectrum Characteristics of Cotton Canopy Infected with Verticillium Wilt and Applications

Hyper spectrum remote sensing with fine spectrum information is an efficient method to estimate the verticillium wilt of cotton. The research was conducted in Xinjiang, the largest cotton plant region of China, by using the data which were collected both by canopy spectrum infected with verticillium wilt and severity level (SL) in the year 2005-2006. The quantitative correlation was analyzed between SL and canopy of reflectance spectrum or derivative spectrum reflectance. The results indicated that spectrum characteristics of cotton canopy infected with verticillium wilt changed regularly with the increase of SL in different periods and varieties. Spectrum reflectance increased in the visible light region (620-700 nm) with the increase of the SL, which inverted in near-infrared region and was extremely significant in the region of (780-1 300 nm). When SL attained b2 (DI = 25), cotton canopy infected with verticillium wilt was used as a watershed and diagnosed index in the beginning stages of the disease. The results also indicated that there were marked different characteristics of the first derivative spectrum in these SL, it changed significantly in the red edge ranges (680-760 nm) with different SL, i.e., red edge swing decreased, and red edge position equally moved to the blue. In this study 1 001-1 110 nm and 1 205-1 320 nm were selected out as sensitive bands for SL of canopy. Inversion models established for estimating cotton canopy infected with verticillium wilt reached the most significant level. Finally, the different spectrum characteristics of cotton canopy infected with verticillium wilt were marked, some inversion models were established, which could estimate SL of canopy infected with verticillium wilt. The best recognized model was the first derivative spectra at (FD 731 nm - FD 1 317 nm), and it might be used to forecast the position of cotton canopy infected with verticillium wilt quantitatively.

[1]  Guo Ni,et al.  Vegetation Index and Its Advances , 2003 .

[2]  H. Ramon,et al.  Automatic detection of ‘yellow rust’ in wheat using reflectance measurements and neural networks , 2004 .

[3]  H. Ramon,et al.  Early Disease Detection in Wheat Fields using Spectral Reflectance , 2003 .

[4]  N. Elliott,et al.  Reflectance characteristics of Russian wheat aphid (Hemiptera: Aphididae) stress and abundance in winter wheat , 2007 .

[5]  Scot E. Smith,et al.  Vegetation indices as indicators of damage by the sunn pest (Hemiptera: Scutelleridae) to field grown wheat , 2008 .

[6]  Z. Niu,et al.  Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging , 2007, Precision Agriculture.

[7]  Sándor Lenk,et al.  Multicolor fluorescence imaging for early detection of the hypersensitive reaction to tobacco mosaic virus. , 2007, Journal of plant physiology.

[8]  Bai Cai-yun Studies of Remote Sensing on Monitoring Crop Diseases and Pests , 2007 .

[9]  Jagadeesh Mosali,et al.  Identification of Optical Spectral Signatures for Detecting Cheat and Ryegrass in Winter Wheat , 2005 .

[10]  K. R. Reddy,et al.  Narrow-waveband reflectance ratios for remote estimation of nitrogen status in cotton. , 2002, Journal of environmental quality.

[11]  Hamed Hamid Muhammed,et al.  Hyperspectral Crop Reflectance Data for characterising and estimating Fungal Disease Severity in Wheat , 2005 .

[12]  Gregory A. Carter,et al.  Airborne Detection of Southern Pine Beetle Damage Using Key Spectral Bands , 1998 .

[13]  Stephan J. Maas,et al.  Spider Mite Detection and Canopy Component Mapping in Cotton Using Hyperspectral Imagery and Spectral Mixture Analysis , 2004, Precision Agriculture.

[14]  H. Prasanna,et al.  Detection and frequency of recombination in tomato-infecting begomoviruses of South and Southeast Asia , 2007, Virology Journal.

[15]  Jingfeng Huang,et al.  Characterizing and estimating rice brown spot disease severity using stepwise regression, principal component regression and partial least-square regression , 2007, Journal of Zhejiang University SCIENCE B.

[16]  Minghua Zhang,et al.  Remote Sensed Spectral Imagery to Detect Late Blight in Field Tomatoes , 2005, Precision Agriculture.