Estimating Defoliation of Peanuts From Spectral Data¹

Authors: J. C. Harlan , W. D. Rosenthal , D. H. Smith

  • Estimating Defoliation of Peanuts From Spectral Data¹


    Estimating Defoliation of Peanuts From Spectral Data¹

    Authors: , ,


Analysis of spectral reflectance potentially can be used to determine the condition of a crop, e.g., drought stress and nutritional deficiencies. Since there is a paucity of information on the feasibility of quantifying disease severity in Arachis hypogaea (L.) with spectral reflectance measurements of the canopy, the objective of this study was to determine if spectral reflectance values of the peanut canopy could be used to estimate the amount of defoliation attributable to epidemics of Cercospora leafspot. Defoliation data and spectral reflectance values (Exotech 100-A radiometer) were periodically obtained in fungicide evaluation tests with varying amounts of defoliation, depending on the efficacy of the fungicide treatments. Combination parameters were developed from reflectance values and statistically compared with appropriate defoliation data. During this two-year study, defoliation percentages were accurately predicted (R2=0.89) by subjecting the combination parameters to regression analyses. Therefore, these results indicate that there is a definite relationship between canopy reflectance and defoliation resulting from Cercospora leafspot epidemics. Since full sunlight near solar noon is the only requirement for reliable reflectance measurements, this radiometric technique can probably be used to monitor development of leafspot epidemics and quantify yield losses on a large scale with either aerial or satellite measurements.

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Keywords: Cercospora arachidicola, Cercosporidium personatum, fungicides, spectral reflectance, remote sensing

How to Cite:

Harlan, J. & Rosenthal, W. & Smith, D., (1978) “Estimating Defoliation of Peanuts From Spectral Data¹”, Peanut Science 5(1), p.10-12. doi:



Published on
01 Jan 1978
Peer Reviewed

Author Notes

1Contribution of the Remote Sensing Center, Texas A&M University, College Station, Texas 77843.