# Automatic Identification of Peanut-Leaf Diseases Based on Stack Ensemble
###### Abstract
The results showed that the PCA neural-network-based image recognition was the best method to detect and diagnose plant-leaf diseases [14]. In recent years, deep-learning methods have led a revolution in many fields such as speech recognition [15; 16], object detection [17; 18], and pose estimation [19; 20]. Deep learning is a promising method for the automatic identification of plant diseases. By taking advantage of the sparse interaction, parameter sharing, and variable representation of a convolution network, it can effectively reduce network parameters, save model storage requirements, and improve the statistical efficiency of the network. Deep-learning methods have made some progress in recent years in identifying plant diseases and pests. [PERSON] et al. proposed a nine-layer deep convolutional neural network plant-disease-recognition model. This model recognized 39 different diseases of different plants on the open source dataset PlantVillage, and reached a 96.46% classification accuracy [21]. [PERSON] et al. used the VGG16 model, Inception module, SE module, and the model of the pool average to identify a total of 10 diseases of cherry, apple, and corn, with an accuracy rate of 91.7%, while greatly reducing training time and model parameters [22]. [PERSON] et al. proposed a global pooled dilated convolutional neural network. By combining dilated convolution and global pooling, cucumber-leaf diseases were identified. The results showed that the model could effectively identify cucumber-leaf diseases [23]. In order to train a deep-learning model with good performance, a large number of data are often needed as the training set. However, datasets are very expensive, and the annotation of large datasets is a difficult problem. In this context, semisupervised learning methods aim at discovering and propagating labels to unsupervised samples, such that their correct labeling can improve the classification performance. Using the images of soybean leaves and herbivorous pests taken by Unmanned Aerial Vehicle, [PERSON] et al. classified the unlabeled training set with a semisupervised method. The training set was used to train a convolutional neural network using different training strategies. The experimental resultsshowed that the accuracy reached 98.90% when using the InceptionV3 structure and a semisupervised method to train the soybean-leaf dataset [24]. The above methods have a high accuracy in plant-disease identification, indicating that traditional machine learning and deep learning are effective methods for identifying plant diseases. However, the above deep-learning methods are only aimed at identifying a certain plant or a variety of plant leaves for a certain disease. In actual field production, plant leaves are often infected by multiple pathogens. This experiment mainly focused on the identification of mixed diseases and single diseases on a single leaf of peanut. The types of identification were healthy leaves, rust disease, scorch disease, leaf-spot disease, and both rust disease and scorch disease at the same time. In addition, the above methods do not have a good combination of deep-learning methods and traditional machine-learning methods. To obtain better classification performance, our study used the deep-learning model as the base-model and the traditional machine-learning model as the meta-model, and then used the predicted data of the base-model as the input features of the meta-model. In this experiment, five convolutional neural network structures, AlexNet, VGG16, residual network with 50 layers (ResNet50), dense convolutional network with 121 layers (DenseNet121), and InceptionV3, were selected as the base-model according to the depth and width of convolutional networks. Three traditional machine-learning methods, random forest (RF), logistic regression (LR), and support vector machine (SVM) were selected as the meta-model according to different classification thoughts.