# Plant Disease Detection Using Deep Convolutional Neural Network
## 1 Introduction  
The diagnosis and treatment of disease are essential to improving the growth and yield of agricultural plants. For instance, an average estimated yield loss by corn crop diseases in the United States and Ontario from 2012 to 2015 was USD 76.51 per acre [1]. Manual monitoring of plant diseases will not give accurate outcomes regularly [2]. Additionally, finding domain experts for monitoring plant diseases is highly difficult and expensive for farmers. For that reason, an intelligent plant disease diagnosis system was essential to monitor the agricultural fields regularly [3]. At present, several plant disease detection methods are proposed for automatic plant disease detection using artificial intelligence techniques with fewer human efforts [4]. Deep convolutional neural network (DCNN) is a most successful image classification technique [5]. The DCNN comprises various layers, such as convolutional, pooling and fully connected layers for learning features from the training data [6]. Transfer learning uses the pre-trained neural network from one task to a similar new task. Transfer learning techniques can minimize the time for model design and training. The standard transfer learning techniques in image classification are AlexNet, DenseNet, VGG16, Inception-v3, MobileNet and ResNet [7]. The DCNN requires more data for anefficient training process [8]. Data augmentation techniques are producing new images for the existing dataset using several data transformation techniques [9; 10]. Basic image manipulation (BIM), deep convolutional generative adversarial network (DCGAN) and neural style transfer (NST) are popular data augmentation techniques [11]. The most common BIM techniques are affine transformation, scaling, cropping, flipping, padding, rotation, translation, brightness, contrast, saturation and hue. The DCGAN is an unsupervised neural network to create a new set of realistic images from the training data [12]. The GAN comprises two DCNNs, such as generator DCNN and discriminator DCNN. The generator DCNN creates new images similar to the training data; also, the discriminator network classifies the original and newly created images by the generator DCNN. DCGAN is one of the most successful image augmentation techniques in medical image processing applications [13]. The NST is an image transformation technique to produce new images using content and style reference images [12]. Additionally, the DCNNs need suitable hyperparameter values to improve the classification performance. Hyperparameters are the most significant training parameters that can influence the performance of deep learning techniques. Activation function, dropout value, epochs, filter size, learning rate, loss function and mini-batch size are the most common hyperparameters in DCNNs. The selection of the suitable value of hyperparameters is a challenging task in solving a deep learning problem. Hyperparameter tuning techniques are used to discover the most suitable hyperparameter values for the DCNNs [14]. Grid search and random search are the most popular hyperparameter tuning approaches in deep learning. High-performance computing power is needed to train the deep learning algorithm with better efficiency and less training time [15]. This article proposed a novel DCNN model for diagnosing 42 leaf diseases in 16 different plant species. This research used data augmentation and hyperparameter optimization techniques to improve the performance of the disease detection model. To produce augmented leaf images, BIM, DCGAN and NST techniques were used. There are 58 diseased and healthy plant leaf classes that were used to train the DCNN model. Random searching with the coarse-to-fine technique was used to optimize the value of the most common hyperparameters. Finally, the performance of the proposed DCNN was compared with the standard transfer learning approaches, such as AlexNet, Inception-v3-Net, ResNet-50 and VGG16 Net.