Abstract
Mosaic, Rust, Brown spot, and Alternaria leaf spot are the four common types of apple leaf diseases. Early diagnosis and accurate identification of apple leaf diseases can control the spread of infection and ensure the healthy development of the apple industry. The existing research uses complex image preprocessing and cannot guarantee high recognition rates for apple leaf diseases. This paper proposes an accurate identifying approach for apple leaf diseases based on deep convolutional neural networks. It includes generating sufficient pathological images and designing a novel architecture of a deep convolutional neural network based on AlexNet to detect apple leaf diseases. Using a dataset of 13,689 images of diseased apple leaves, the proposed deep convolutional neural network model is trained to identify the four common apple leaf diseases. Under the hold-out test set, the experimental results show that the proposed disease identification approach based on the convolutional neural network achieves an overall accuracy of 97.62%, the model parameters are reduced by 51,206,928 compared with those in the standard AlexNet model, and the accuracy of the proposed model with generated pathological images obtains an improvement of 10.83%. This research indicates that the proposed deep learning model provides a better solution in disease control for apple leaf diseases with high accuracy and a faster convergence rate, and that the image generation technique proposed in this paper can enhance the robustness of the convolutional neural network model.
Highlights
China is a modern agricultural country supplying fruit products, wherein the fruit planting area is relatively large
We present a novel identifying approach for apple leaf diseases based on a deep convolutional neural network
In order to solve the problem of insufficient apple pathological images, this paper proposes a training image generation technology based on image processing techniques, which can enhance the robustness and prevent overfitting of the convolutional neural networks (CNNs)-based model in the training process
Summary
China is a modern agricultural country supplying fruit products, wherein the fruit planting area is relatively large. We present a novel identifying approach for apple leaf diseases based on a deep convolutional neural network. In order to solve the problem of insufficient apple pathological images, this paper proposes a training image generation technology based on image processing techniques, which can enhance the robustness and prevent overfitting of the CNN-based model in the training process. By analyzing the characteristics of apple leaf diseases, a novel deep convolutional neural network model based on AlexNet is proposed; the convolution kernel size is adjusted, fully-connected layers are replaced by a convolutional layer, and GoogLeNet’s Inception is applied to improve the feature extraction ability.
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