Determining the prevalence of certain cereal crop diseases by means of aerial photography

Colwell, R

Determining the prevalence of certain cereal crop diseases by means of aerial photography

Author

Robert N. Colwell

Author Affiliations

Robert N. Colwell was Associate Professor of Forestry and Associate Silviculturist in the Experiment Station, Berkeley; Chairman, National Research Council Subcommittee on Crop Geography and Vegetation Analysis.

Publication Information

Hilgardia 26(5):223-286. DOI:10.3733/hilg.v26n05p223. November 1956.

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Abstract

Studies were conducted to determine the possibility of detecting and identifying various healthy and diseased cereal crops by means of aerial photographs, and of estimating disease severity.

The crops studied were wheat, oats, barley, and rye; the diseases were black stem rust and yellow dwarf virus.

Tests were conducted both on test plots artificially inoculated with disease and on open fields where the pathogen had entered by natural means.

Spectrophotometric analyses of light reflectance from healthy and diseased crops were made. These analyses permitted prediction of the photographic tones or colors with which the various crops would register on any film-filter combination. Four film-filter combinations were then selected as offering the best tone or color contrasts for the photographic identifications sought.

Results of the tests indicate that, on aerial photographs taken to proper specifications, and at the proper seasonal state of development of the host plant and pathogen, it is possible for a photo interpreter to recognize: (1) healthy wheat, oats, barley, and rye; (2) diseased wheat infested with black stem rust (Puccinia graminis tritici); (3) diseased oats infested with black stem rust (P. graminis avenae); and (4) diseased oats infested with yellow dwarf virus.

In some instances the photo interpreter, using only smallscale, black-and-white photographs, can detect disease infection centers early enough to permit effective control measures. Frequently, using large-scale, color photographs, he can also estimate disease severity and subsequent yield reduction quite accurately.

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