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Microcomputer Measurements of Pathogen Injury to Weeds

Published online by Cambridge University Press:  12 June 2017

Steven E. Lindow
Affiliation:
Dep. Plant Pathol., Univ. California, Berkeley, CA 94720
Gary L. Andersen
Affiliation:
Dep. Plant Pathol., Univ. California, Berkeley, CA 94720

Extract

The impact of chemical or biological herbicides in weed control may or may not be absolute. The traditional goal in chemical herbicide evaluation has been the identification of materials and application rates resulting in absolute (100%) kill of susceptible weeds. However, many insects or plant pathogens affecting weeds do not kill their weed hosts but decrease their growth and or reproduction in the field. Obligate plant pathogens dependent upon their weed hosts for their own survival derive no evolutionary advantage from killing their weed hosts and seldom do so under natural conditions. Under field conditions where weeds are subjected to stresses imposed by competition with crop and/or other wild plants, these biological agents can decrease the impact of a weed on crop or wildland productivity to tolerable economic levels. The recognition that the impact of chemical and/or biological herbicides on weeds need not be absolute to be effective has been accompanied by a search for an accurate, efficient, and economical method to measure quantitative changes in weed growth or health. While much of this report will concentrate on measuring impact of plant pathogens on weeds or other plants, all comments and techniques will apply also to other stresses such as herbicide injury.

Type
Research Article
Copyright
Copyright © 1986 by the Weed Science Society of America 

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References

Literature Cited

1. Anonymous. 1947. The measurement of potato blight. Trans. Br. Mycol. Soc. 31:140141.Google Scholar
2. Anonymous. 1970. Crop loss assessment methods; FAO manual on the evaluation and prevention of losses by pests, diseases and weeds. Publ. AGP:CP/22. Rome (Looseleaf).Google Scholar
3. Baird, J. C. and Elliott, N. 1978. Fundamentals of Scaling and Psychophysics. John Wiley & Sons, New York. 287 pp.Google Scholar
4. Blanchette, R. A. 1982. New technique to measure tree defect using an image analyzer. Plant Dis. 66:394397.Google Scholar
5. Brenchley, G. H. 1968. Aerial photography for the study of plant diseases. Annu. Rev. Phytopathol. 6:122.CrossRefGoogle Scholar
6. Broaderick, H. T., Longshaw, T. B., and Lelyveld, L. J. 1973. Disease detection with spectral analysis. Abstr. 073. Proc. 2d Int. Cong. Plant Pathol.Google Scholar
7. Chester, K. S. 1950. Plant disease losses: Their appraisal and interpretation. Plant Dis. Rep. Suppl. 193:189362.Google Scholar
8. Cobb, N. A. 1892. Contribution to an economic knowledge of the Australian rusts (Uredineae) Agric. Gaz. N.S. Wales 3:6068.Google Scholar
9. Coren, S. and Girgus, J. S. 1978. Seeing is Deceiving: The Psychology of Visual Illusion. John Wiley & Sons, New York. 255 pp.Google Scholar
10. Croxal, H. E., Gwynne, D. C., and Jenkins, J. E. E. 1952. The rapid assessment of apple scab on leaves and fruit. Plant Pathol. 1:3941.Google Scholar
11. Dietz, T. E., Davis, L. S., Diller, K. R., and Aggarwal, J. W. 1982. Computer recognition and analysis of freezing cells in noisy, cluttered images. Cryobiology 19:539549.Google Scholar
12. Eyal, Z. and Brown, M. B. 1976. A quanitative method for estimating density of Septoria tritici pycnidia on wheat leaves. Phytopathology 66:1114.Google Scholar
13. Gausman, H. W. 1974. Leaf reflectance of near infrared. Photogramm. Eng. 40:182191.Google Scholar
14. Gausman, H. W., Escobar, D. E., and Bowen, R. L. 1983. A video system to demonstrate interactions of near-infrared radiation with plant leaves. Remote Sensing of Environment 13:363366.Google Scholar
15. Hebert, T. T. 1982. The rationale for the Horsfall-Barratt plant disease assessment scale. Phytopathology 72:1269.Google Scholar
16. Horsfall, J. G. and Barratt, R. W. 1945. An improved grading system for measuring plant disease (Abstr.) Phytopathology 35:655.Google Scholar
17. Horsfall, J. G. and Cowling, E. B. 1978. Pathometry: The measurement of plant disease. Pages 119136 in Plant Disease: An Advanced Treatise. Vol. II. Horsfall, J. G. and Cowling, E. B., eds. Academic Press, New York.Google Scholar
18. Jackson, R. 1964. Detection of plant disease symptoms by infrared photography. J. Biol. Photogr. Assoc. 32:4558.Google Scholar
19. Jackson, H. R., Hodgson, W. A., Wallen, V. R., Philpotts, L. E., and Hunter, J. 1971. Potato late blight intensity levels as determined by microdensitometer studies of false-color-aerial photographs. J. Biol. Photogr. Assoc. 39:101106.Google ScholarPubMed
20. James, W. C. 1971. An illustrated series of assessment keys for plant diseases, their preparations and usage. Can. Plant Dis. Surv. 51:3965.Google Scholar
21. James, W. C. 1974. Assessment of plant diseases and losses. Annu. Rev. Phytopathol. 12:2748.Google Scholar
22. Koch, H. and Hau, B. 1980. Ein psychologischer Aspekt biem Schatzen von Pflanzenkrankheiten. A. Pfalanzenkrankh. Pflanzenschutz 87:587593.Google Scholar
23. Kranz, J. 1970, Schatzklassen fur Krankheitsbefall. Phytopathol. Z. 69:131139.CrossRefGoogle Scholar
24. Large, E. C. 1966. Measuring plant disease. Annu. Rev. Phytopathol. 4:928.Google Scholar
25. Lindow, S. E. and Webb, R. R. 1983. Measurement of foliar plant disease using microcomputer controlled digital video image analysis. Phytopathology 73:520524.Google Scholar
26. Lindow, S. E. 1983. Estimating disease severity of single plants. Phytopathology 73:15761581.Google Scholar
27. Main, C. E. 1977. Crop destruction–the raison d'etre of plant pathology. Pages 5578 in Plant Disease: An Advanced Treatise. Vol. I. Horsfall, J. G. and Cowling, E. B., eds. Academic Press, New York.Google Scholar
28. Manzer, F. E. and Cooper, G. R. 1982. Use of portable videotaping for aerial infrared detection of potato diseases. Plant Dis. 66:665667.Google Scholar
29. Nilsson, H. E. 1980. Application of remote sensing methods and image analysis at macroscopic and microscopic levels in plant pathology. Pages 7684 in Crop Loss Assessment. Minnesota Agric. Exp. Stn. Misc. Publ. 7.Google Scholar
30. Odle, W. C. and Toler, R. W. 1976. Remote sensing of St. Augustine-grass decline disease. Remote Sensing Center TR-77, Texas A&M Univ., College Station. 164 pp.Google Scholar
31. Peterson, R. F., Campbell, A. B., and Hannah, A. E. 1948. A diagrammatic scale for estimating rust intensity on stems and leaves of cereals. Can. J. Res. C. 26:496500.Google Scholar
32. Philip, B. R. 1947. The relationship of speed and exposure time in a perceptual task. J. Exp. Psychol. 37:178186.Google Scholar
33. Sherwood, R. T., Berg, C. C., Hoover, M. R., and Zeiders, K. E. 1983. Illusions in visual assessment of Stagonospora leafspot of orchardgrass. Phytopathology 73:173177.Google Scholar
34. Stevens, S. S. and Galanter, E. H. 1957. Ratio scales and category scales for a dozen perceptual continua. J. Exp. Psychol. 54:377411.Google Scholar
35. Toler, R. W., Smith, B. D., and Harlan, J. C. 1981. Use of aerial color infrared photography to evaluate crop disease. Plant Dis. 65:2431.Google Scholar
36. Ullstrup, A. J., Elliott, C., and Hoppe, P. E. 1945. Report of the committee on methods for reporting corn disease ratings. Wash. Div. Cereal Crops Dis., U.S. Dep. Agric., Washington, DC. 5 pp.Google Scholar
37. Wallen, V. R. and Jackson, H. R. 1971. Aerial photography as a survey technique for the assessment of bacterial blight of field beans. Can. Plant Dis. Surv. 51:163169.Google Scholar