Differentiation of Cotton from Other Crops at Different Growth Stages Using Spectral Properties and Discriminant Analysis

The spectral reflectance properties of cotton (Gossypium hirsutum L.), corn (Zea mays L.), soybean [Glycine max (L.)], and sorghum [Sorghum bicolor (L.)] crops during their different growth stages were examined, and spectral data were used to distinguish cotton from other crops. Two field blocks with two different soil types, Belk clay (BaA) and Ships clay (ShA), were set up with cotton, corn, soybean, and sorghum in each block and grown using conventional production practices for the area. Spectral information was collected from all crops at different growth stages from May to July 2009. Reflectance spectra and the first derivative of the spectra were analyzed to characterize the spectral properties of crop types and compare the crops grown in different soil types. The red-edge points of cotton, soybean, and sorghum shifted with the growth stage. Principal component analyses were successful in reducing the dimensionality of the hyperspectral data and identifying significant features from the original data. Most significant wavelengths selected were in the 548-556 nm, 679-682 nm, 756-764 nm, and 928-940 nm regions of the spectrum. Discriminant analysis was able to differentiate cotton from other crop types at four critical growth stages with 100% accuracy of classification for all four observation dates.

[1]  D. Horler,et al.  The red edge of plant leaf reflectance , 1983 .

[2]  P. Thenkabail,et al.  Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics , 2000 .

[3]  L. Bruce,et al.  Wavelet analysis of hyperspectral reflectance data for detecting pitted morningglory (Ipomoea lacunosa) in soybean (Glycine max) , 2003 .

[4]  A. Galston Plant Physiology , 1967, Nature.

[5]  A. Skidmore,et al.  Red edge shift and biochemical content in grass canopies , 2007 .

[6]  Benoit Rivard,et al.  Comparison of spectral indices obtained using multiple spectroradiometers , 2006 .

[7]  J. Dungan,et al.  The effect of a red leaf pigment on the relationship between red edge and chlorophyll concentration , 1991 .

[8]  Yubin Lan,et al.  Spatial Analysis of NDVI Readings with Different Sampling Densities , 2011 .

[9]  J. Qi,et al.  Identification of red and NIR spectral regions and vegetative indices for discrimination of cotton nitrogen stress and growth stage , 2005 .

[10]  P. Curran Remote sensing of foliar chemistry , 1989 .

[11]  Vijaya Gopal Kakani,et al.  Nitrogen deficiency effects on plant growth, leaf photosynthesis, and hyperspectral reflectance properties of sorghum , 2005 .

[12]  B. R. Roberts,et al.  RELATIONSHIPS BETWEEN REMOTELY SENSED REFLECTANCE DATA AND COTTON GROWTH AND YIELD , 2000 .

[13]  Michael D. Steven,et al.  High resolution derivative spectra in remote sensing , 1990 .

[14]  F. M. Danson,et al.  RED-EDGE RESPONSE TO FOREST LEAF-AREA INDEX (VOL 16, PG 183, 1995) , 1995 .

[15]  Lori M. Bruce,et al.  Utility of Hyperspectral Reflectance for Differentiating Soybean (Glycine max) and Six Weed Species , 2009, Weed Technology.

[16]  H. Muhammed Characterizing and Estimating Fungal Disease Severity in Wheat , 2004 .

[17]  R. M. Korobov,et al.  Red edge structure of canopy reflectance spectra of Triticale , 1993 .

[18]  J. Peñuelas,et al.  The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. , 1994 .

[19]  Yubin Lan,et al.  Development of an Integration Sensor and Instrumentation System for Measuring Crop Conditions , 2009 .