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Open AccessArticle

Identification of Apple Tree Leaf Diseases Based on Deep Learning Models

by Xiaofei Chao 1,2, Guoying Sun 2, Hongke Zhao 2, Min Li 2 and Dongjian He 1,3,4,*
1
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China
2
College of Information Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China
3
Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling 712100, Shaanxi, China
4
Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling 712100, Shaanxi, China
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(7), 1065; https://doi.org/10.3390/sym12071065
Received: 31 May 2020 / Revised: 18 June 2020 / Accepted: 24 June 2020 / Published: 28 June 2020
(This article belongs to the Section Computer and Engineer Science and Symmetry)
Early diagnosis and accurate identification of apple tree leaf diseases (ATLDs) can control the spread of infection, to reduce the use of chemical fertilizers and pesticides, improve the yield and quality of apple, and maintain the healthy development of apple cultivars. In order to improve the detection accuracy and efficiency, an early diagnosis method for ATLDs based on deep convolutional neural network (DCNN) is proposed. We first collect the images of apple tree leaves with and without diseases from both laboratories and cultivation fields, and establish dataset containing five common ATLDs and healthy leaves. The DCNN model proposed in this paper for ATLDs recognition combines DenseNet and Xception, using global average pooling instead of fully connected layers. We extract features by the proposed convolutional neural network then use a support vector machine to classify the apple leaf diseases. Including the proposed DCNN, several DCNNs are trained for ATLDs recognition. The proposed network achieves an overall accuracy of 98.82% in identifying the ATLDs, which is higher than Inception-v3, MobileNet, VGG-16, DenseNet-201, Xception, VGG-INCEP. Moreover, the proposed model has the fastest convergence rate, and a relatively small number of parameters and high robustness compared with the mentioned models. This research indicates that the proposed deep learning model provides a better solution for ATLDs control. It could be also integrated into smart apple cultivation systems. View Full-Text
Keywords: apple tree leaf diseases; deep convolutional neural network; transfer learning; model fusion apple tree leaf diseases; deep convolutional neural network; transfer learning; model fusion
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Chao, X.; Sun, G.; Zhao, H.; Li, M.; He, D. Identification of Apple Tree Leaf Diseases Based on Deep Learning Models. Symmetry 2020, 12, 1065.

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