LAUSR

Abstract The fungal diseases in banana cause major yield losses for millions of farmers around the globe. Early detection of these diseases helps the farmers to devise successful management strategies. The characteristic leaf blade discoloration pattern at the earlier stages of infection could be used to understand the onset of each disease. This paper demonstrates a methodology for classification of three important foliar diseases in banana, using local texture features. The disease affected regions are identified using image enhancement and color segmentation. Segmented images are converted to transform domain using three image transforms (DWT, DTCWT and Ranklet transforms). Feature vector is extracted from transform domain images using LBP and its variants (ELBP, MeanELBP and MedianELBP). These texture based features are applied to five popular image classifiers and comparative performance analysis is done using ten-fold cross validation procedure. Experimental results showed best classification performance for ELBP features extracted from DTCWT domain (accuracy 95.4 %, precision 93.2 %, sensitivity 93.0 %, Fscore 93.0 % and specificity 96.4 %). Compared with traditional methods of feature extraction, this novel method of fusing DTCWT with ELBP features has attained high degree of accuracy in precisely detecting and classifying fungal diseases in banana at an early stage.