# Automatic Identification of Peanut-Leaf Diseases Based on Stack Ensemble
###### Abstract  
Peanut is an important food crop, and diseases of its leaves can directly reduce its yield and quality. In order to solve the problem of automatic identification of peanut-leaf diseases, this paper uses a traditional machine-learning method to ensemble the output of a deep learning model to identify diseases of peanut leaves. The identification of peanut-leaf diseases included healthy leaves, rust disease on a single leaf, leaf-spot disease on a single leaf, scorch disease on a single leaf, and both rust disease and scorch disease on a single leaf. Three types of data-augmentation methods were used: image flipping, rotation, and scaling. In this experiment, the deep-learning model had a higher accuracy than the traditional machine-learning methods. Moreover, the deep-learning model achieved better performance when using data augmentation and a stacking ensemble. After ensemble by logistic regression, the accuracy of residual network with 50 layers (ResNet50) was as high as 97.59%, and the \(F1\) score of dense convolutional network with 121 layers (DenseNet121) was as high as 90.50. The deep-learning model used in this experiment had the greatest improvement in \(F1\) score after the logistic regression ensemble. Deep-learning networks with deeper network layers like ResNet50 and DenseNet121 performed better in this experiment. This study can provide a reference for the identification of peanut-leaf diseases. peanut-leaf diseases; deep learning; convolutional neural network; identification +
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For fire sensitivity prediction and mapping, [PERSON] evaluated four machine-learning methods. The models were evaluated using the area under the receiver operating characteristic curve and seven other performance metrics. The results showed that these models were sufficiently robust in responding to the training and verification dataset changes [5]. In terms of urban planning, [PERSON] successfully used a machine-learning method to classify the green infrastructure of satellite images [6]. In recent years, random forest, k-means, and support vector machine [7; 8; 9] algorithms based on traditional machine learning have been applied to plant-disease diagnosis. [PERSON] et al. used a vector median filter to remove noise from collected corn-disease color pictures, then extracted the texture and color features of the corn-disease image as feature vectors, and then mapped the input space samples to high-dimensional space features for K-means clustering and plant-disease identification, and the final identification rate of their four corn diseases was 82.5% [10]. [PERSON] et al. proposed a technology based on the serialization of two support vector machine classifiers, with one classifier extracting the color features of the picture, and the other classifier distinguishing the classes of similar colors according to the shape and texture features, and the accuracy rate reached 87.80% [11]. [PERSON] et al. used hyperspectral data to identify wheat leaf rust. Four machine-learning methods, including v-support vector regression, enhanced regression tree, random forest regression and Gaussian process regression, were established to estimate the severity of rust at the canopy scale. The experiment showed that hyperspectral signatures could be used in the identification of wheat leaf rust at different leaf area index levels [12]. [PERSON] et al. performed background cutting on soybean leaf pictures, determined the chromaticity value of the leaves, and established several layers of back-propagation neural networks. The accuracy of the soybean disease spot-recognition rate was 92.1% [13]. [PERSON] et al. proposed a method of plant-disease recognition that extracted 21 color features, 25 texture features and 4 shape features from the images of diseased leaves of wheat and grape, processed the features with principal component analysis (PCA), and classified wheat and grape diseases with multiple neural-network classifiers.