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 Starters 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 LED Visor for the identification of peanut-leaf diseases.