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AERIAL PHOTOGRAPHY FOR DETECTION AND ASSESSMENT OF GRASSHOPPER (ORTHOPTERA: ACRIDIDAE) DAMAGE TO SMALL GRAIN CROPS IN SASKATCHEWAN1

Published online by Cambridge University Press:  31 May 2012

O. O. Olfert
Affiliation:
Research Station, Agriculture Canada, Saskatoon, Saskatchewan S7N 0X2
S. H. Gage
Affiliation:
Research Station, Agriculture Canada, Saskatoon, Saskatchewan S7N 0X2
M. K. Mukerji
Affiliation:
Research Station, Agriculture Canada, Saskatoon, Saskatchewan S7N 0X2
R. L. Randell
Affiliation:
Biology Department, University of Saskatchewan, Saskatoon, Saskatchewan S7N 0X2

Abstract

Aerial colour infra-red photographs were used to detect grasshopper damage to cereal grains, determine extent of damage, estimate crop yield, and identify crops. The detection and identification of grasshopper damage was facilitated by the development of a dichotomous key which differentiates grasshopper damage from other crop anomalies. Crop types and yield were determined from correlations between the optical density of the photographs and the biomass of standing crops.

Cost of an aerial survey was estimated at $0.02 per hectare as compared with ground survey cost of $0.20 per hectare. Information provided by an aerial survey of damage has the potential to assist grain growers, extension personnel, and researchers in evaluating the influence of grasshoppers on grain production

Type
Articles
Copyright
Copyright © Entomological Society of Canada 1980

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References

Baier, W. and Robertson, G. W.. 1965. Estimation of latent evaporation from simple weather observations. Can. J. Pl. Sci. 45: 276284.CrossRefGoogle Scholar
Brach, E. J. 1976. Recent development on crop variety identification by U.V., visible and infrared remote spectroscopy. Engng Res. Serv. Rep. (Ottawa) 6842–570. 9 pp.Google Scholar
Brach, E. J., Tinker, R. W., and St. Amour, G. T.. 1977. Improvements in spectral reflectance measurements of field crops. Can. Agric. Engng 19: 7883.Google Scholar
Gage, S. H. and Mukerji, M. K.. 1978. Crop losses associated with grasshoppers in relation to economics of crop production. J. econ. Ent. 71: 487498.CrossRefGoogle Scholar
Goodman, M. S. 1959. A technique for identification of farm crops on aerial photographs. Photogramm. Engng 25: 5864.Google Scholar
Idso, S. B., Jackson, R. D., and Reginato, R. J.. 1977. Remote sensing of crop yields. Science 196: 1925.CrossRefGoogle ScholarPubMed
Mayr, E., Linsley, E. G., and Usinger, R. L.. 1953. Methods and Principles of Systematic Zoology. McGraw Hill, New York. 828 pp.Google Scholar
Meyer, M. D. and Chiang, H. C.. 1971. Multiband reconnaissance of simulated defoliation in corn fields. Sci. J. Ser. 7634. Minn. Exp. Stn.Google Scholar
Murtha, P. A. 1972. A guide to air photo interpretation of forest damage in Canada. Can. For. Serv. Publ. 1292. 63 pp.Google Scholar
Philpotts, L. E. and Wallen, V. R.. 1969. IR color for crop disease identification. Photogramm. Eng. 35: 11161125.Google Scholar
Rao, V. R., Brach, E. J., and Mack, A. R.. 1978. Crop discriminability in visible and near infrared regions. Photogramm. Engng 44: 11791184.Google Scholar
Steiner, D. and Salerno, A. E. (Eds.). 1975. Remote sensor data systems, processing, and management. pp. 611803 in Reeves, R. C. (Ed.), Manual of Remote Sensing. Am. Soc. Photogramm., Falls Church, Virginia.Google Scholar
Wallen, V. R., Jackson, H. R., and MacDiarmid, S. W.. 1976. Remote sensing of corn aphid infestation, 1974 (Hemiptera: Aphididae). Can. Ent. 108: 751754.CrossRefGoogle Scholar