## 3 Results
\begin{table}
\begin{tabular}{l c c c c} \hline  & ** SSM1** & ** SSM1** & ** SSM1** & ** SSM2** & ** SSM3** \\  & **Optimal \(T_{h}\) (\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline Melanose (26/8) & 100/86/92 & 92/86/88 & 92/86/88 & 92/100/96 & 100/93/96 \\ Rot (20/6) & 80/100/89 & 70/100/89 & 70/100/89 & 80/96/93 & 60/100/86 \\ Septoria (10/6) & 33/100/87 & 20/100/86 & 20/100/86 & 100/100/**100** & 20/100/86 \\ Fruit Split (10/6) & 40/100/89 & 40/100/89 & 40/100/89 & 75/100/96 & 40/100/89 \\ \hline \end{tabular}
\end{table} 
Table 4: Sensitivity/specificity/accuracy of group C. The parentheses next to the name of the diseases denote (test/training samples). The SSM with the best accuracy for each disease is highlighted with bold-italics. \begin{table}
\begin{tabular}{l c c c c c} \hline  & ** SSM1** & ** SSM1** & ** SSM1** & ** SSM2** & ** SSM3** \\  & **Optimal \(T_{h,opt}\) (\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline Alternaria (44/6) & 86/78/80 & 82/78/79 & 82/78/79 & 41/96/85 & 63/90/85 \\ Anthracnose (24/6) & 75/92/89 & 73/98/95 & 70/86/84 & 20/84/77 & 89/76/77 \\ CCDV (22/8) & 91/100/**99** & 45/100/92 & 91/100/99 & 75/100/98 & 90/99/98 \\ Melanose (36/8) & 86/78/80 & 82/78/79 & 82/78/79 & 41/96/85 & 63/90/85 \\ Nutrient Deficiency (38/8) & 79/96/92 & 74/96/91 & 68/96/89 & 61/94/87 & 18/100/90 \\ \hline \end{tabular}
\end{table} 
Table 2: Sensitivity/specificity/accuracy of group A. The parentheses next to the name of the diseases denote (test/training samples). The SSM with the best accuracy for each disease is highlighted with bold-italics. \begin{table}
\begin{tabular}{l c c c c c} \hline  & ** SSM1** & ** SSM1** & ** SSM1** & **SSM2** & **SSM3** \\  & **Optimal \(T_{h}\) (\%)** & **(\(T_{h,opt}-10\))** & **(\%)** & **(\%)** & **(\%)** \\ \hline Alternaria (30/6) & 73/94/90 & 60/94/87 & 60/94/87 & 50/95/**91** & 70/90/87 \\ Anthracnose (56/6) & 36/87/**79** & 31/86/77 & 33/86/78 & 67/79/76 & 71/76/75 \\ CCDV (18/8) & 89/100/**99** & 78/100/97 & 78/100/97 & 68/100/98 & 75/100/98 \\ Melanose (36/8) & 94/100/**99** & 83/97/94 & 83/97/94 & 80/90/89 & 75/85/84 \\ Nutrient Deficiency (40/8) & 25/100/81 & 25/100/81 & 25/100/81 & 0/99/89 & 25/100/83 \\ \hline \end{tabular}
\end{table} 
Table 3: Sensitivity/specificity/accuracy of group B. The parentheses next to the name of the diseases denote (test/training samples). The SSM with the best accuracy for each disease is highlighted with bold-italics. 9 of them are used for training and the 10 th for testing. All the folders are used for testing in a round robin fashion and the classification results obtained are combined. It is obvious that the employed classification methods are trained exhaustively and much more efficiently than the proposed scheme that uses 10-36% of the test samples for training. The sensitivity, specificity and accuracy results of the classification methods tested in Weka are listed in Tables 5-7. The sensitivity, specificity and accuracy measured when the photographs of the leaves from both group A (captured under direct sunlight) and group B (captured under a canopy) are merged into a single group (AB), are listed in Table 5. The groups A and B have also been tested separately and the sensitivity, specificity and accuracy results are presented in Tables 6 and 7. Color spaces (HSV, HSL, CIE L*a*b) different than RGB have been used with linear DRE (see Equation (3)) for the diagnosis of four pear diseases. An overall set of 200 pear leaf photographs (50 per disease) has been employed and 8 of the 50 photographs from each disease were used for training. The DRE normalization has been applied to the V (value) component of HSV, and the L (lightness) component of HSL or CIE L*a*b. The sensitivity results are listed in Table 8.