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Identification of Cherry Leaf Disease Infected by Podosphaera Pannosa via Convolutional Neural Network

Identification of Cherry Leaf Disease Infected by Podosphaera Pannosa via Convolutional Neural Network

Keke Zhang, Lei Zhang, Qiufeng Wu
Copyright: © 2019 |Volume: 10 |Issue: 2 |Pages: 13
ISSN: 1947-3192|EISSN: 1947-3206|EISBN13: 9781522566731|DOI: 10.4018/IJAEIS.2019040105
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MLA

Zhang, Keke, et al. "Identification of Cherry Leaf Disease Infected by Podosphaera Pannosa via Convolutional Neural Network." IJAEIS vol.10, no.2 2019: pp.98-110. http://doi.org/10.4018/IJAEIS.2019040105

APA

Zhang, K., Zhang, L., & Wu, Q. (2019). Identification of Cherry Leaf Disease Infected by Podosphaera Pannosa via Convolutional Neural Network. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 10(2), 98-110. http://doi.org/10.4018/IJAEIS.2019040105

Chicago

Zhang, Keke, Lei Zhang, and Qiufeng Wu. "Identification of Cherry Leaf Disease Infected by Podosphaera Pannosa via Convolutional Neural Network," International Journal of Agricultural and Environmental Information Systems (IJAEIS) 10, no.2: 98-110. http://doi.org/10.4018/IJAEIS.2019040105

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Abstract

The cherry leaves infected by Podosphaera pannosa will suffer powdery mildew, which is a serious disease threatening the cherry production industry. In order to identify the diseased cherry leaves in early stage, the authors formulate the cherry leaf disease infected identification as a classification problem and propose a fully automatic identification method based on convolutional neural network (CNN). The GoogLeNet is used as backbone of the CNN. Then, transferred learning techniques are applied to fine-tune the CNN from pre-trained GoogLeNet on ImageNet dataset. This article compares the proposed method against three traditional machine learning methods i.e., support vector machine (SVM), k-nearest neighbor (KNN) and back propagation (BP) neural network. Quantitative evaluations conducted on a data set of 1,200 images collected by smart phones, demonstrates that the CNN achieves best precise performance in identifying diseased cherry leaves, with the testing accuracy of 99.6%. Thus, a CNN can be used effectively in identifying the diseased cherry leaves.