Agriculture Reference
In-Depth Information
overestimations occurred at low (<10%) disease severity, or 30-40% leaf senescence.
The work also showed that such visual assessment errors could alter experimental
conclusions. Parker and Royle (1993) and Lovell
et al.
(1997) therefore developed
and used a new key for assessing foliar disease severity in wheat, in which pictorial
representation of disease levels was presented on a generic cereal leaf divided into a
grid of 1% sectors of leaf lamina (Fig. 2.7) with the following claimed advantages: a
reduced need for interpolation; no reliance on high contrast diagrams; and avoidance
of the need for different keys for assessments of more than one disease. Use of the
new key revealed more comparable assessments were possible between observers,
although precision of assessments did not improve.
Figure 2.7. Prototype of a new wheat leaf disease assessment aid to avoid some of the
disadvantages associated with the use of conventional disease assessment keys. Each sector
of the grid is equal to 1%. (Parker and Royle, 1993).
Nutter and Schultz (1995) concluded that the accuracy and precision of disease
assessments was improved simply by selecting the most appropriate methods and by
training observers to assess disease severity using computerized disease assessment
training programmes such as AREAGRAM, DISTRAIN and Disease.Pro. Although
AREAGRAM (Shane
et al.,
1985) graded user's performance, it generated only
standard area diagrams with fixed disease patterns. DISTRAIN (Tomerlin and
Howell, 1988) was developed as a training programme for disease assessment using
variegated patterns of disease severity for eight common foliar diseases of cereals;
the programme also allowed a comparison of estimated severity with actual severity.
Nutter and Worawitlikit (1989) expanded the computer training concept in their
advanced programme for peanut diseases, Disease.Pro and later in 1998, developed
a more generic disease assessment programme, Severity.Pro, that allowed the user to
select from a menu of leaf shapes (e.g. alfalfa, apple, barley, cucumber, grape,
tomato) and lesion types (e.g. anthracnose, blotch, downy mildew, target spot,
powdery mildew) so mimicking almost any foliar pathosystem (Nutter
et al.,
2006).
There are many variations and modifications of the standardized pictorial disease
assessment key described so far in this chapter. One of the more useful of these is
the Saari-Prescott 0-9 scale (Saari and Prescott, 1975) incorporating a double digit
00-99 scale (Fig. 2.8) for evaluating the intensity (severity and vertical disease
progress) of foliar diseases (except rusts) in wheat, triticale and barley. In this
system, the first digit gives the relative height of the disease using the original 0-9
Saari-Prescott scale as a measure and the second digit shows disease severity but in
terms of 0-9 (0%-90% coverage in equal divisions of 10%). So in a plant with a
disease height of 5 and an average disease coverage on the upper four leaves of
10%, the numerical disease description is 51. A further variation is the Eyal-Brown
diagrammatic scale for estimating pycnidial density of
Septoria tritici
per unit wheat
leaf area (Eyal and Brown, 1976); the scale uses actual observed pycnidial coverage
and a scaled percentage possible pycnidial coverage.
