Data from: Using R-based image analysis to quantify rusts on perennial ryegrass

Crown and stem rust are major diseases of perennial ryegrass (Lolium
perenne L.). Plant breeders and pathologists often rate rust severity in
the field using the modified Cobb scale, but this method is subjective and
labor intensive. A novel, open-source system using ImageJ and R was
developed to quantify pustule number and area using digital images
collected from spaced plants in the field. The computer-processing
pipeline included development of training data for prediction of pixel
identity using random forest and noise reduction spatial processing.
Raters and the computer scored rust severity on plant images of varying
complexity including whole-plant (WP), five-leaf (FL), and single-leaf
(SL) image series. Computer accuracy was determined using the SL, while
the FL series gave insight into the true value of WP severity. Rater
ability was assessed using a panel of nine scientists with varying levels
of disease rating experience. Results showed rater perceptions of crown
rust severity were very consistent across images, but agreement on
severity values for a given image were low. Rater consistency for stem
rust severity was low and FL scores were not strongly correlated with WP
scores (r = 0.36, P = 0.03), indicating low rater accuracy. The
computer-processing pipeline was able to accurately discriminate, count,
and quantify crown and stem rust pustules on leaf samples. Correlations
between computer and the median rater score for crown rust were excellent
(r > 0.90, P < 0.001) for all image series. Similar to
raters, there was a lack of correlation between WP and FL series (r =
0.20, not significant) indicating that this technique is limited to leaf
or stem samples for stem rust and not applicable to WP. However, the
computer-processing pipeline shows promise in replacing visual rating of
WP for crown rust.