Before revision:  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 .
After revision:  The performance of PCC-KMC was tested using RGB images of ShB-infected stems of the rice lines Jasmine 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 .