Table 1.
Overview of plant disease severity quantification methods.
Fig 1.
Desired output in different situations: The first column represents the initial (artificially created) image, whilst the second one represents the desired output matrix (active pixels in black).
A, B: examples of correct detection when the input is a leaf image with disease symptoms upon pathogen infection (the second example considers a noisy input image). C: example of no detection when the input is a leaf image affected by impulse noise in healthy condition.
Fig 2.
Maximal closed cone containing all accepted vectors in the normalised RGB colour space.
The example shows a closed cone defined by the vector and a tolerance ξ = 0.1.
Fig 3.
(A) Input image representing a grape leaf affected by Black Rot disease. (B) The resulting image from the application of the dynamic algorithm (representation of the pixels modified by the iterative procedure during the transient state until the steady-state condition has been reached). (C) Final result after binarisation.
Fig 4.
(A) A vector x (healthy) converges in norm to (diseased). (B) Distance between the two vectors represented by the norm of their difference.
Fig 5.
Examples of lesions on grape leaf caused by various infectious diseases.
(A) Black rot (Guignardia bidwellii), (B) Leaf blight (Pseudocercospora vitis), (C) Esca (Phaeomoniella spp.), and (D) Downy mildew (Plasmopara viticola).
Table 2.
List of crops and their disease status used in the experiments.
Table 3.
Example of confusion matrix for a dichotomous binary classification problem.
Fig 6.
Example of several curves of the monotonic function ρ using several different parameters.
Fig 7.
Confusion matrices for the proposed disease detection method.
Table 4.
Summary of the experimental results in terms of disease detection and disease severity estimation for each disease dataset.
Fig 8.
Disease severity statistical analysis through the boxplot of data results from each disease dataset.
Fig 9.
System performance analysis through receiver operating characteristic (ROC) curves obtained using the proposed approach for healthy and diseased classification over the apple, grape, and potato culture datasets.
Fig 10.
Example of healthy leaf image affected by impulse noise with a probability p = 15%.
Table 5.
Summary of the noise-rejection experiment results in terms of accuracy and error rate on the healty leaves of the whole dataset.