## 3 Materials and Methods
\[\%\text{Diseased Leaf Area}=\frac{\text{Background pixel count}}{\left(\text{Background pixel count}\right)\ +\ \left(\text{Foreground pixel count}\right)}\ \times 100 \tag{8}\]  
According to the fuzzy decision rules that will be explained next, an image of maize common rust disease shown in Figures 2 and 3 was assigned to be Late Stage because a percentage of 65.8% was calculated by means of Equation (8) as follows:  
\[\%\text{Diseased Leaf Area}=\frac{43175}{43175+2232}\ \times 100=65.8\%\]  
Figure 1: The procedure of the proposed approach. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Hardware** & **Software** & **Dataset** \\ \hline \(\bullet\) & ASUS TUF Laptop with & \(\bullet\) & Image Analyzer, an & \(\bullet\) & A standard PlantVillage \\ \(\bullet\) & NVIDIA GeForce GTX & & open-source image & \(\bullet\) & maize data set. \\  & 1650 4 GB Graphics. & & processing tool. & \\ \(\bullet\) & Samsung A-30 16 Mega & \(\bullet\) & Jupyter Notebook IDE. & \\  & Pixels rear camera & \(\bullet\) & Python-Keras library. & \\  & & \(\bullet\) & Python-Tensor flow & \\  & & \(\bullet\) & library. & \\  & & \(\bullet\) & Anaconda package. & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A tabulated summary of the materials that were used in the study. Finally, Step 6 concerns the derivation of fuzzy decision rules for the assignment of maize common rust diseased images to their severity classes. With the help of a human expert in plant pathology, we were able to derive fuzzy decision rules for the compilation of training data sets that were categorized into \"Early Stage\", \"Middle Stage\", and \"Late Stage\". The technique used to derive these fuzzy logic rules was based on the experience of the plant pathologist and classic methods of detecting plant disease severity, such as observing the rate at which the rust is scattered on the leaf. These rules may change depending on the plant species and the type of the disease dealt with. As for the \"Healthy Stage\", the training data were compiled by use of healthy images in the PlantVillage dataset. The compilation of the training data sets was conducted using fuzzy decision rules outlined subsequently. Design Rules for Healthy Prediction. Rule 1: As for the \"Healthy Stage\", the training data were compiled by use of healthy images in the PlantVillage dataset. Design Rules for Common Rust Disease Severity Prediction. Fuzzy decision Rule 1: If %Diseased Leaf Area \(\geq\)50%, then, the image belongs to \"Late-stage training data set\". Fuzzy decision Rule 2: If 45% \(\leq\) %Diseased Leaf Area < 50%, then, the image belongs to \"Middle stage training data set\". Fuzzy decision Rule 3: If %Diseased Leaf Area <45%, then, the image belongs to \"Early-stage training data set\". Figure 4 illustrates the procedure for the assignment of maize common rust diseased images to their severity classes using the fuzzy decision rules. The same procedure was repeated for all the common rust images until enough datasets were compiled to train the fine-tuned VGG-16 network. There were three severity classes of maize common rust  
Figure 3: Procedure explained in Figure 1, continued. Figure 2: Procedure explained in Figure 1.  
disease and one healthy class. The fourth healthy class was developed from the PlantVillage dataset by use of healthy maize images (Supplementary Materials). Figure 5 shows a sample of healthy leaf images from the PlantVillage datasets that were used for training in the fourth class. By performing the above fuzzy decision rules to assign the maize common rust diseased images to their severity classes, and formation of a healthy class with healthy images of the PlantVillage dataset, we ended up with four categories of the training datasets. Figure 6 shows the arrangement of the datasets which were adhered to in order to be able to train the VGG-16 network with the images of our proposed approach. Figure 4: Assignment of maize common rust disease images to their severity classes using fuzzy decision rules.