## 3 Results  
In the experimental results presented in this paper, the information set in Figure 6i was not taken into consideration. The classification process did not also take into account the weather data (\(T_{n}\), \(T_{x}\), \(M_{d}\)) although they are supported by the developed application since the quantitative contribution of these features in the appearance of a disease was difficult to be determined (their weight was set to 0). Three metrics were measured in order to characterize the quality of the classification procedure: sensitivity, specificity and accuracy. After analyzing all the available photographs, each disease was treated separately and four parameters were estimated: (a) the true positives (TP) were the photographs displaying the specific disease and were recognized correctly, (b) the true negatives (TN) were the photographs that did not display the specific disease and were also recognized correctly, (c) the falsely recognized as negative (FN) and (d) the falsely recognized as positive (FP) to the specific disease. Sensitivity measures how many of the sample photographs displaying the disease have been recognized correctly. Specificity measures how many of the samples that do not display the specific disease, have been recognized correctly. Finally, accuracy indicates how many of both categories have been identified successfully. These metrics are defined as:  
\[\text{Sensitivity}=\frac{TP}{TP+FN}, \tag{9}\]  
\[\text{Specificity}=\frac{TN}{TN+FP}, \tag{10}\]  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FN+FP}, \tag{11}\]  
The experimental results for each disease of the group A (see Section 2), are presented in Table 2. In each cell, the sensitivity/specificity/accuracy results are displayed. The number of photographs used for training and for testing are listed in parentheses next to the disease name. Similar experimental results for the leaves of group B are presented in Table 3 and the corresponding for the diseases of the orange fruits are listed in Table 4. As can be seen from these tables the number of test photographs per disease ranges between 10 and 56. In most of the diseases, 30-40 photographs were used. Few of them (6-8), were used for training. The developed application has selected the most likely disease after performing the appropriate analysis. In case of leaves, it had to select between five alternatives while it had to choose the most likely disease between four alternatives in the case of citrus fruits. The columns of the Tables 2-4 correspond to the different SSM used. The first column shows the results achieved using SSM1 with an optimal \(T_{h}\) value (\(T_{h,opt}\)) for accurate image segmentation. The \(T_{h,opt}\) value was selected by trial and error. Then, the T\({}_{h,opt}\)\(\pm\) 10 values are also tested to examine how worse can the results get if the user does not select the exact \(T_{h,opt}\) value but a different one in the range (T\({}_{h,opt}-10\), T\({}_{h,opt}+10\)). If the constant 10 is too large to be added to, or subtracted by the threshold, a \(\pm\)10% is used. The other two columns show the results if the SSM2 and SSM3 are used to segment the input image in a non-interactive way. The features used by the technique that has been described in Section 2.3 are also tested with popular classification methods in the framework of the Weka tool. The specific classification methods tested are: (a) multilayer perceptron neural network, (b) J48 decision tree [30], (c) random forest [31], (d) random tree (e) naive Bayes [32] and two more recent approaches that are based on logistic model trees (LMT) and are supported in the Weka environment: (f) simple logistics [33] and (g) LMT [34]. The training and testing of these algorithms is performed using the cross-validation method with the default 10 folders. This means that for each class the test samples are distributed in 10 folders,