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.
Highlights
Published: 23 February 2021Peanut is a crop with multiple uses and a very rich nutritional value
In the traditional machine-learning method, support vector machine (SVM) achieved an accuracy of 73.20%, which is 11.77% higher than the lowest logistic regression (LR) of 61.43%
In the image-classification problem, a higher classification accuracy meant that recall and precision of the model to give an overall assessment of the performance of the harder to improve the accuracy any further
Summary
Peanut is a crop with multiple uses and a very rich nutritional value. The peanut itself and the complex field environment make the leaves easy to be infected by pathogens. The pathogen can spread rapidly through natural factors and has a high reproductive capacity. The main factor affecting its reproduction is the humidity of peanuts during the seedling stage [2]. Artificial identification of peanut-leaf diseases requires professional knowledge, and it is easy to misdiagnose them only by artificial visual observation. In this way, peanut diseases cannot be diagnosed and treated in time. The key to control peanut disease is to diagnose the disease type quickly and accurately, and take corresponding control measures in time
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