# Plant Disease Detection Using Deep Convolutional Neural Network
## 3 Materials and Methods
### Dataset Preparation and Preprocessing  
Diseased and healthy leaf images of various plants were collected from different standard open data repositories [9; 35; 36; 37; 38]. Sixteen different plant species were used to create the plant leaf disease dataset. Each plant contains healthy and the most common disease classes in the dataset. There are 58 different classes of plant leaves, and 1 no-leaves class is present in the dataset. The collected original dataset contains 61,459 plant leaves and no-leaves images. The list of plant names with the healthy and disease classes of the proposed dataset is shown in Table 2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**S. No** & **Plant Name** & **Class Names** \\ \hline
1 & & Healthy \\
2 & Aloe Vera & Leaf Rot \\
3 & & Leaf Rust \\ \hline
4 & & Healthy \\
5 & Apple & Leaf Scab \\
6 & & Black Rot \\
7 & & Leaf Rust \\ \hline
8 & & Healthy \\
9 & Banana & Bacterial Wilt \\
10 & & Black Sigatoka \\ \hline
11 & Cherry & Healthy \\
12 & & Powdery Mildew \\ \hline
13 & & Healthy \\
14 & & Black Spot \\
15 & Citrus & Canker \\
16 & & Greening \\
17 & & Melanose \\ \hline
18 & & Healthy \\
19 & & Common Rust \\
20 & & Leaf Spot \\
21 & & Northern Leaf Blight \\ \hline
22 & & Healthy \\
23 & & Cercospora Leaf Spot \\
24 & & Leaf Rust \\
25 & & Red Spider Mite \\ \hline
26 & & Healthy \\
27 & & Black Measles \\
28 & & Black Rot \\
29 & & Leaf Blight \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of classes in the proposed dataset. To create an even number of images in each class, data augmentation techniques were introduced. Additionally, the data augmentation techniques can increase the dataset size and reduce the overfitting during the training process of the model by adding some augmented images to the training dataset. The BIM, DCGAN and NST augmentation techniques were used to produce the augmented images in the dataset. The BIM augmentation techniques consist of image cropping, flipping, PCA color augmentation, rotation and scaling. The PCA color augmentation technique alters the intensity of the color channels using the principal component of the pixels [11]. Additionally, the image cropping, flipping, rotation and scaling techniques create augmented images by changing the color and position of the input images. There are 36,541 augmented images created by the BIM augmentation technique in the dataset. DCGANs create augmented images that resemble the training data. The DCGAN consists of two DCNN networks, such as generator DCNN and discriminator DCNN. The generator DCNN network takes a vector of random noise and up-samples it to the training data. On the other hand, the discriminator DCNN learns to classify the real and generated images [33]. The DCGAN network was trained in the graphics processing units with training epochs of 10,000 and a mini-batch size of 64. There are 32,000 augmented images created by the DCGAN augmentation technique in the dataset. NST is another image generation technique using deep learning techniques. A modified VGG19 network was used to develop the NST augmentation model in this research. The NST model was trained with 5000 epochs on the deep learning server system. The NST models require two