## 4 Discussion  
As can be seen from Tables 2 and 3, higher sensitivity is achieved using the photographs of group A. This is explained by the fact that the plant diseases have been defined based on the photographs from this group. A comparable accuracy is achieved in both groups A and B, since higher sensitivity was achieved in group A, for anthracose and nutrient deficiency and higher in group B for _Alternaria_ and melanose. Better specificity is achieved in group B. The highest score for these metrics is achieved using SSM1 with optimal \(T_{h}\), although SSM3 is more efficient in some cases. Some performance degradation is expected when a \(T_{h}\) value different from the optimal one is used. However, it is not likely that the user would select a threshold \(T_{h}\) much different from \(T_{h,opt}\) as he/she would be able to compare interactively the initial photograph (Figure 6a) with the segmented one (Figure 6b). Higher accuracy is achieved in group A. In group B the application fails to recognize diseases like anthracose and nutrient deficiency with acceptable sensitivity since the training photographs of these diseases were not representative of the group B. In group C, the sensitivity achieved is not particularly high for _Septoria_ and fruit split, but the overall accuracy is acceptable in all cases (see Table 4). This is owed mainly to the high specificity that can be achieved in this group. The sensitivity achieved in group C is slightly worse than group A, but much higher than that of group B. From the comparison with other classification methods presented in Tables 5-7 it can be deduced that the highest sensitivity for group A,B is achieved by the proposed method in all diseases but nutrient deficiency. SimpleLogistics is the only compared method that achieves better sensitivity for CCDV (see Table 5). From Table 5 it is also obvious that the proposed method achieves very good accuracy in the case of _Alternaria_ (only random forest is better) and CCDV. However, many of these classification methods achieve higher accuracy than the proposed one in the case of anthracose, nutrient deficiency and melanose. Focusing on group A, the sensitivity achieved by the proposed method is much better than all the other classification methods except from the case of nutrient deficiency as shown by Table 6. Concerning the results presented in Table 7, the proposed method achieves better sensitivity and accuracy than all the other methods in the case of _Alternaria_, CCDV and melanose. The accuracy achieved for the other two diseases (anthracnose, nutrient deficiency) is comparable to the accuracy of the rest of the classification methods. The results achieved by the developed classification technique are generally better in group A and as already mentioned this is owed to the fact that the disease signatures have been defined using photographs from this group. In any case, the accuracy achieved by the proposed method is comparable (if not better) than the rest of the classification algorithms when they operate on the same set of features. It has to be stressed that the results of the compared classification methods are the best that could have been achieved in the Weka tool since cross-validation with 10 folders has been selected. This configuration forces multiple cross checks in order to perform the best possible classification. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification Method** & **RGB** & **HSV** & **HSL** & **CIE L*a*b** \\ \hline Fire Blight & **95.8\%** & 79.2\% & 83.3\% & 83.3\% \\ Pear Scab & 74\% & **98\%** & **98\%** & 91.6\% \\ Mycosphaerella & **100\%** & 96\% & 94\% & 90\% \\ Mildew & **100\%** & 88\% & **100\%** & 98\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Sensitivity of pear disease diagnosis when linear dynamic range expansion (DRE) normalization is employed. The color space with the best sensitivity for each disease is highlighted with bold-italics.