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ARS Home » Plains Area » College Station, Texas » Southern Plains Agricultural Research Center » Crop Germplasm Research » Research » Publications at this Location » Publication #258690

Title: Discriminating among cotton cultivars with varying leaf characteristics using hyperspectral radiometry

item ZHANG, HUIHUI - Texas A&M University
item Hinze, Lori
item Lan, Yubin
item Westbrook, John
item Hoffmann, Wesley

Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/5/2012
Publication Date: 2/29/2012
Citation: Zhang, H., Hinze, L.L., Lan, Y., Westbrook, J.K., Hoffmann, W.C. 2012. Discriminating among cotton cultivars with varying leaf characteristics using hyperspectral radiometry. Transactions of the ASABE. 55(1):275-280.

Interpretive Summary: Precision agriculture serves a vital role in the improvement of agriculture practices, allowing farmers to produce crops with greater economy and efficiency. The utility of precision agriculture could be further increased if various types of crop varieties could be automatically identified, allowing for the selective application of fertilizers, herbicides, and chemicals. Through analysis of imaging data (including visible and near-infrared wavelengths) obtained from plants grown in greenhouses, we demonstrated that certain cotton genotypes can be characterized using a remote sensing tool, a handheld spectroradiometer. The results of this investigation are important to scientists as they continue to enhance the applications of precision agriculture tools and techniques that will ultimately benefit farmers. Farmers will use these tools to produce environmentally friendly crops with minimum inputs and maximum yields.

Technical Abstract: Precision agriculture applications using remote sensing have been steadily increasing in recent years. 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 cultivars representing different species (G. hirsutum and G. barbadense) with several leaf phenotypes were grown in two greenhouses. Hyperspectral data collected with a handheld spectroradiometer were used to distinguish cotton cultivars. The results for statistical analyses of three datasets showed that cotton cultivars within each greenhouse could be discriminated with principal component analysis. The 550 nm and 700 nm wavelengths have significant information for the discrimination within both cotton cultivar groups. Two vegetation indices, NDVI and PRI were also investigated for the discrimination. Neither NDVI nor PRI could be used to distinguish all the cotton cultivars, PRI could be used to separate cotton cultivar F4 from others in the G. hirsutum group. No consistent result was found for the G. barbadense group.