# Plant Disease Detection Using Deep Convolutional Neural Network
## 2 Related Works  
The early detection of plant diseases is a significant step in the disease prevention and treatment process [6]. Accurate disease detection techniques can be used by the farmers for applying the prevention and treatment procedures [10; 16]. The most recent plant disease detection techniques are reviewed in this section. In Ref. [4], the authors proposed a support vector machine (SVM) model for detecting sugar crop diseases. They used the hyperspectral images as an input of the SVM model for disease detection. The average classification accuracy of the hyperspectral image-based SVM technique was 78% in testing data. In Ref. [17], the authors proposed two huanglongbing disease identification models for citrus plants. The SVM and artificial neural network (ANN) were used to design the huanglongbing detection techniques. The classification accuracies of the SVM and ANN models in the test data were 92.8% and 92.2% respectively. Identification of tomato yellow leaf curl disease was achieved using the SVM technique with the quadratic kernel function proposed by the author in [18]. The overall classification accuracy of this algorithm was 92% in tomato yellow leaf curl disease detection.  
Additionally, the authors in [19] discussed and compared numerous image processing and feature extraction techniques to identify the various plant diseases using their leaf images. The authors in [20] developed a DCNN model to detect legume plant species using vein morphological patterns. In Ref. [21], the authors proposed a region-based and single-shot multi-box detector CNN model for designing the plant disease and pest identification model. The VGGNet and ResNet were used to improve the classification performance of the model. Table 1 compares the various state-of-the-art DCNN models for plant disease detection proposed by different articles.  
The data augmentation technique improves the diversity of training data without collecting new data. The authors in [11] compared the advantages of various data augmentation techniques in the DCNNs training process. The augmentation techniques are GAN, flipping, cropping, shifting, principal component analysis (PCA), color, noise and rotation. The result shows that the training performance of the cropping, flipping, GAN and rotation are higher than the other augmentation techniques. Additionally, the result proves that the combination of different augmentation techniques can give better performance than individuals. In Ref. [12], the authors introduced the BIM, GAN and NST augmentation techniques for plant leaf disease classification and compared the performance of each technique. The experimental result shows that the performance of the combined augmentation technique was better than the individual techniques. The authors in [32], discussed the advantages of the existing data augmentation techniques in deep learning applications. In Ref. [33], the authors proposed a DCNN for pest detection using GAN based image augmented dataset. The testing result shows that the classification performance of the GAN-based image augmented dataset was better than the non-augmented dataset. The authors in [13] discussed the advantages of the GAN augmentation technique in DCNN development. Moreover, the authors in [34] discussed the importance of hyperparameter tuning to achieve a better performance of DCNNs. The detailed survey indicates the importance of dataset size, augmentation techniques and selection of hyperparameter values in the plant leaf disease detection model. The following section presents the information about the proposed dataset and DCNN model for detecting various plant diseases from leaf images.