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Table 1 Comparison of proposed technique with existing methods

From: A probabilistic segmentation and entropy-rank correlation-based feature selection approach for the recognition of fruit diseases

Method Year Segmentation technique Feature type Feature selection Classifier Disease Accuracy
[2] 2016 K-means clustering Color, texture Random Forest Classifier Apple scab, apple blotch, and rot 80%, 80%, 80%
[35] 2012 K-means clustering GCH, CCV, LBP, CLBP M-SVM Apple scab 96.96%
[5] 2016 K-means clustering Color, texture, shape M-SVM Apple blotch, rot, and scab 97.50%, 92.50%, 93.75%
[29] 2017 Region growing algorithm Color, texture, shape GA and correlation-based feature selection SVM Rust 94.28%
[36] 2016 Otsu and watershed SGDM, GLCM NN Grape rot, powdery mildew 95.23%, 92.85%
[37] 2016 K-means clustering Color, texture SVM Powdery mildew 83.33%
Proposed 2017 Adaptive and quartile deviation-based segmentation with correlation coefficient SLBP, texture, color Entropy-rank correlation-based feature selection M-SVM Apple scab, apple rust, grape rot leaves, grape powdery mildew 97.10%, 94.70%, 96.60%, 96.30