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

Author:

Zhang Keke1,Zhang Lei2,Wu Qiufeng3ORCID

Affiliation:

1. College of Engineering, Northeast Agricultural University, Harbin, China

2. Department of Radiology, University of Pittsburgh, Pittsburgh, USA

3. College of Science, Northeast Agricultural University, Harbin, China

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.

Publisher

IGI Global

Subject

Information Systems

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3. Bacterial Disease Detection of Cherry Plant Using Deep Features;Sakarya University Journal of Computer and Information Sciences;2024-04-30

4. Synergistic Detection: A Hybrid CNN-XGBoost Model for Cherry Leaf Spot Magnitude Differentiation;2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM);2024-02-21

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