Discriminating among Cotton Cultivars with Varying Leaf Characteristics Using Hyperspectral Radiometry

There is a rapidly growing interest in methods for automatic plant identification in agricultural research. Cotton (Gossypium spp.) is a crop well-suited to precision agriculture and its inherent goals of increasing yields while minimizing environmental impacts. Ten cotton (G. hirsutum and G. barbadense) cultivars with differing leaf characteristics were evaluated in a greenhouse environment. Hyperspectral data collected with a handheld spectroradiometer were used to distinguish among the cultivars. The features extracted by principal component analysis and stepwise selection approaches were used for discriminant analysis. The best discrimination accuracy by selected wavelengths was 90.4% for G. hirsutum cultivars, 100% for G. barbadense cultivars, and 91.6% for pooled cultivars of the two species. Spectral wavelengths at 550 and 760 nm were most relevant to the discrimination between these two cotton species. Two vegetation indices, NDVI and PRI, were also investigated for any significant differences across cotton cultivars. The results demonstrated that hyperspectral radiometry has good potential for discrimination of G. hirsutum and G. barbadense cotton cultivars in early stages of growth.

[1]  José M. Paruelo,et al.  Grass species differentiation through canopy hyperspectral reflectance , 2009 .

[2]  M. G. Holmes,et al.  Effects of pubescence and waxes on the reflectance of leaves in the ultraviolet and photosynthetic wavebands: a comparison of a range of species , 2002 .

[3]  M. Ashton,et al.  Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications , 2004 .

[4]  H. Gausman,et al.  Visible light reflectance, transmittance and absorptance of differently pigmented cotton leaves , 1983 .

[5]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[6]  John B. Solie,et al.  Detection of nitrogen and phosphorus nutrient status in winter wheat using spectral radiance , 1998 .

[7]  Glen L. Ritchie,et al.  Cotton growth and development , 2007 .

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

[9]  P. North,et al.  Remote sensing of canopy light use efficiency using the photochemical reflectance index , 2001 .

[10]  F. D. Whisler,et al.  Analysis of a precision agriculture approach to cotton production , 2001 .

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

[12]  V. Kakani,et al.  Selection of Optimum Reflectance Ratios for Estimating Leaf Nitrogen and Chlorophyll Concentrations of Field-Grown Cotton , 2005 .

[13]  Michael Nørremark,et al.  Automatic identification of crop and weed species with chlorophyll fluorescence induction curves , 2011, Precision Agriculture.

[14]  S. Prasher,et al.  Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn , 2003 .

[15]  M. Dugan,et al.  Cotton , 2009, Fashion Fibers.

[16]  D. Sims,et al.  Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages , 2002 .

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

[18]  J. Peñuelas,et al.  Assessment of photosynthetic radiation‐use efficiency with spectral reflectance , 1995 .

[19]  William R. Raun,et al.  By‐Plant Prediction of Corn Forage Biomass and Nitrogen Uptake at Various Growth Stages Using Remote Sensing and Plant Height , 2007 .

[20]  S. Ghosh,et al.  Development of an agricultural crops spectral library and classification of crops at cultivar level using hyperspectral data , 2007, Precision Agriculture.

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

[22]  P. Dutilleul,et al.  CLASSIFICATION ACCURACY OF DISCRIMINANT ANALYSIS, ARTIFICIAL NEURAL NETWORKS, AND DECISION TREES FOR WEED AND NITROGEN STRESS DETECTION IN CORN , 2005 .