Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

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.

Recognition pest by image‐based transfer learning

“…Brahimi et al , used CNN to recognize tomato leaves infected with nine diseases, the results reached 99.18% accuracy. Liu et al , designed a novel architecture of a deep CNN based on AlexNet to detect apple leaf diseases, the model achieved an accuracy of 97.62%. DeChant et al , used CNN model to recognize Northern leaf blight (NLB) in maize, the model achieved a 96.7% accuracy on test set images.…”

Diseased Portion Cassification & Recognition of Cotton Plants using Convolution Neural Networks

“…Deep convolution neural network has been used for apple leaf diseases. This technique used for generating diseased images and designing a architecture of deep CNN [5]. Deep learning has become an emerging topic along with CNN.…”

A Practical Plant Diagnosis System for Field Leaf Images and Feature Visualization

“…In such circumstances, methodologies for automated plant diagnosis characterized by accuracy, speed and low costs have been requested by the agricultural industry. Several studies have been carried out in response to such requests [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. In [4] used support vector machines (SVM) to classify rice plant diseases and attained 92.7% accuracy.…”

“…Due to that, they not only significantly reduced the need for the complicated hand-made processes mentioned previously but also achieved high classification performance. Recently, several applications for automated plant diagnosis relying on deep learning have also been proposed [11,15,17,[20][21][22][23]. In [15] used a total of 54,306 plant leaf images consisting of 14 crop species and 26 diseases for a total of 38 classes of crop-disease pairs from PlantVillage [24] and built CNNs classifiers.…”