Red-green-blue (RGB) digital image-based detection is a promising alternative approach to the existing subjectivity-prone and labor-intensive plant disease quantification methods. K-means clustering (KMC) is a widely used algorithm for processing plant disease RGB images. Because the KMC algorithm clusters pixels based only on color similarity, it has limited utility for quantifying diseases that produce necrotic, coalescing, and water-soaked lesions . Here, we present a pixel color- and coordinate-based K-Means clustering (PCC-KMC) algorithm that is capable of RGB image analysis based on color (normalized RGB values) and spatial information (column and row coordinates) of pixels for quantification of rice sheath blight (ShB) disease . The performance of PCC-KMC was tested using RGB images of ShB-infected stems of the rice lines Jasmine 85 , Lemont, and 301194. In contrast to KMC, which clustered randomly scattered pixels of similar color, PCC-KMC successfully segmented diseased regions into biologically-relevant clusters and thereby correctly indicated the pattern of ShB progression. PCC-KMC also enabled the quantification of disease in terms of the percentage of stem area with symptoms and the distance of disease progression from the inoculation site. The results indicate that the PCC-KMC algorithm can be used for the automated quantification of lesions caused by SbB on rice and of lesions caused by other pathogens on other plants .
Rapid and accurate phenotypic screening of rice germplasms is crucial in screening for sources of rice sheath blight resistance. However, visual and/or caliper-based estimations of coalescing, necrotic, ShB disease lesions are time-consuming, labor-intensive and exposed to human rater subjectivity . Here, we propose the use of RGB images and image processing techniques to quantify ShB disease progression in terms of lesion height and diseased area. To be specific, we developed a pixel color- and coordinate-based K-Means Clustering (PCC-KMC) algorithm utilizing Mahalanobis metric aimed at accurate segmentation of symptomatic and non-symptomatic regions within rice stem images . The performance of PCC-KMC was evaluated using Lin's concordance correlation coefficient by comparing its results to visual measurements of ShB lesion height and to lesion/diseased area measured using ImageJ. Low bias and high precision were observed for absolute lesion height (bias=0.93, precision=0.94) and absolute symptomatic area (bias=0.98, precision=0.97) studies. Moreover, we introduced a convolutional neural network (CNN) for the automatic annotation on clusters, termed PCC-KMC-CNN. Our CNN was trained based on 85 \%:15\% of composition for training and testing dataset from total 168 ShB-infected stem sample images, recording 92\% accuracy and 0.21 loss. PCC-KMC-CNN also showed high accuracy and precision for the absolute lesion height (bias=0.86, precision=0.90) and absolute diseased area (bias=0.99, precision=0.97) studies. These results demonstrate that the present methodology has great potential and promise to substitute the traditional visual-based ShB disease severity assessment .
R
Red-green-blue (RGB) digital image-based detection is a promising alternative approach to the existing subjectivity-prone and labor-intensive plant disease quantification methods. K-means clustering (KMC) is a widely used algorithm for processing plant disease RGB images. Because the KMC algorithm clusters pixels based only on color similarity, it has limited utility for quantifying diseases that produce necrotic, coalescing, and water-soaked lesions
Rapid and accurate phenotypic screening of rice germplasms is crucial in screening for sources of rice sheath blight resistance. However, visual and/or caliper-based estimations of coalescing, necrotic, ShB disease lesions are time-consuming, labor-intensive and exposed to human rater subjectivity
R
present
propose the use of RGB images and image processing techniques to quantify ShB disease progression in terms of lesion height and diseased area. To be specific, we developed
R
clustering
Clustering
R
that is capable of RGB image analysis based on color (normalized RGB values) and spatial information (column and row coordinates) of pixels for quantification of rice sheath blight (ShB) disease
utilizing Mahalanobis metric aimed at accurate segmentation of symptomatic and non-symptomatic regions within rice stem images
R
tested using RGB images of ShB-infected stems of the rice lines Jasmine
evaluated using Lin's concordance correlation coefficient by comparing its results to visual measurements of ShB lesion height and to lesion/diseased area measured using ImageJ. Low bias and high precision were observed for absolute lesion height (bias=0.93, precision=0.94) and absolute symptomatic area (bias=0.98, precision=0.97) studies. Moreover, we introduced a convolutional neural network (CNN) for the automatic annotation on clusters, termed PCC-KMC-CNN. Our CNN was trained based on
R
, Lemont, and 301194. In contrast to KMC, which clustered randomly scattered pixels of similar color, PCC-KMC successfully segmented diseased regions into biologically-relevant clusters and thereby correctly indicated the pattern of ShB progression. PCC-KMC also enabled the quantification of disease in terms of the percentage of stem area with symptoms and the distance of disease progression from the inoculation site. The results indicate that the PCC-KMC algorithm can be used for the automated quantification of lesions caused by SbB on rice and of lesions caused by other pathogens on other plants
\%:15\% of composition for training and testing dataset from total 168 ShB-infected stem sample images, recording 92\% accuracy and 0.21 loss. PCC-KMC-CNN also showed high accuracy and precision for the absolute lesion height (bias=0.86, precision=0.90) and absolute diseased area (bias=0.99, precision=0.97) studies. These results demonstrate that the present methodology has great potential and promise to substitute the traditional visual-based ShB disease severity assessment