## 4 Discussion
The employed normalization technique that has been originally developed for skin disorder diagnosis [35], improved the achieved sensitivity by up to 150% in some cases (e.g., from 45% to 68% in Papillomas). The effect of other normalization techniques such as linear DRE in various color spaces (HSV, HSL, CIE L*a*b) was measured in pear disease diagnosis and the sensitivity results were listed in Table 8. As can be seen from this table, only one of the four diseases achieved better sensitivity in color spaces different than RGB. For this reason, the normalization technique of [35] was employed in the rest of the experiments conducted in the framework of this paper. The SSM2 and SSM3 achieved worse results compared to the SSM1 with \(T_{h,opt}\). However, it has to be noted that the disease signature definition was based on the analysis performed by SSM1 and \(T_{h,opt}\). Had photographs been analyzed with SSM2 or SSM3 for the disease signature definition, the results would be much better since more representative photographs would have been exploited. Table 9 shows the draft segmentation quality achieved by SSM2 and SSM3. The analysis performed by SSM2 and SSM3 is compared to that of SSM1 with \(T_{h,opt}\). A segmentation precision degradation between 10% and 50% compared to SSM1 is marked as \"Moderately Accurate\" in Table 9. If the degradation is better, the results are marked as \"Accurate\", otherwise as \"Inaccurate\". As can be seen from this table, SSM2 achieves better segmentation precision if leaf spots have to be recognized while SSM3 recognizes more accurately the spots on the fruits. This can be explained by the fact that the spots on the fruits have a more distinctive color than the leaf spots. For example, the citrus fruits are yellow while the spots are brown and thus, a clear local minimum appears as required by the SSM3 in order to separate correctly the spots from the normal plant part. Different plant diseases like the ones that appear on grapevine can be recognized with higher than 88% sensitivity as described in [26] where SSM1 with \(T_{h,opt}\) is used. If \(T_{h,opt}\pm 10\) is used the sensitivity can fall below 70%. The accuracy achieved with the proposed methods may not be as high as the 98% achieved, for example, by [PERSON] et al. [17], but it is comparable to the 91% of the method described by [PERSON] and [PERSON] [18] and to the 93% achieved by the mobile phone application presented in [21]. However, the accuracy presented in these referenced approaches was achieved after training the employed classification methods with thousands of images. Moreover, most of these approaches have not been developed for mobile phones that have limited resources [36]. As a conclusion from the discussion above we can state that the proposed plant disease diagnosis method has achieved very good accuracy compared to standard classification methods and the referenced approaches. The disease signatures defined here, were based on a small number of representative training photographs allowing the extension of the supported set of diseases by an end user that does not have access to the application source code. It is obvious that higher accuracy can be achieved by a larger training set. However, we believe that the size of this training set does not have to be excessively high in order to cover all the possible appearances of a disease with acceptable accuracy. Although the optimal thresholds needed for the image segmentation can be easily selected through the SSM1 interactively, the automatic segmentation performed by SSM2 and SSM3 can also be employed according to the displayed plant part and provided that the employed disease signatures would have been defined using photographs analyzed with these methods.