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Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks

1,2,*,†, 1,†, 2,3 and 4
1
College of Information Engineering, NorthWest A&F University, No. 22, Xinong Road, Yangling 712100, China
2
Key Laboratory of Agricultural Internet of Things (NorthWest A&F University), Ministry of Agriculture, Yangling 712100, China
3
College of Mechanical and Electronic Engineering, NorthWest A&F University, No. 22, Xinong Road, Yangling 712100, China
4
School of Information Technology, Henan University of Science and Technology, No. 263, Kaiyuan Avenue, Luoyang 471023, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work and should be considered co-first authors.
Symmetry 2018, 10(1), 11; https://doi.org/10.3390/sym10010011
Received: 5 November 2017 / Revised: 26 December 2017 / Accepted: 27 December 2017 / Published: 29 December 2017
(This article belongs to the Special Issue Information Technology and Its Applications)
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PDF [3567 KB, uploaded 29 December 2017]
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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. View Full-Text
Keywords: apple leaf diseases; deep learning; convolutional neural networks; image processing apple leaf diseases; deep learning; convolutional neural networks; image processing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Liu, B.; Zhang, Y.; He, D.; Li, Y. Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks. Symmetry 2018, 10, 11.

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