Identification of Cherry Leaf Disease Infected by Podosphaera Pannosa via Convolutional Neural Network
Lei Zhang and
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Keke Zhang: College of Engineering, Northeast Agricultural University, Harbin, China
Lei Zhang: Department of Radiology, University of Pittsburgh, Pittsburgh, USA
Qiufeng Wu: College of Science, Northeast Agricultural University, Harbin, China
International Journal of Agricultural and Environmental Information Systems (IJAEIS), 2019, vol. 10, issue 2, 98-110
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.
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