Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques
Joint Authors
Sun, Minghe
Wang, Qimei
Qi, Feng
Qu, Jianhua
Xue, Jie
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-12-16
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
This study develops tomato disease detection methods based on deep convolutional neural networks and object detection models.
Two different models, Faster R-CNN and Mask R-CNN, are used in these methods, where Faster R-CNN is used to identify the types of tomato diseases and Mask R-CNN is used to detect and segment the locations and shapes of the infected areas.
To select the model that best fits the tomato disease detection task, four different deep convolutional neural networks are combined with the two object detection models.
Data are collected from the Internet and the dataset is divided into a training set, a validation set, and a test set used in the experiments.
The experimental results show that the proposed models can accurately and quickly identify the eleven tomato disease types and segment the locations and shapes of the infected areas.
American Psychological Association (APA)
Wang, Qimei& Qi, Feng& Sun, Minghe& Qu, Jianhua& Xue, Jie. 2019. Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-15.
https://search.emarefa.net/detail/BIM-1129654
Modern Language Association (MLA)
Wang, Qimei…[et al.]. Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-15.
https://search.emarefa.net/detail/BIM-1129654
American Medical Association (AMA)
Wang, Qimei& Qi, Feng& Sun, Minghe& Qu, Jianhua& Xue, Jie. Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-15.
https://search.emarefa.net/detail/BIM-1129654
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1129654