Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques

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dc.contributor.authorWang, Qimei
dc.contributor.authorQi, Feng
dc.contributor.authorSun, Minghe
dc.contributor.authorQu, Jianhua
dc.contributor.authorXue, Jie
dc.creator.orcidhttps://orcid.org/0000-0001-8503-9761en_US
dc.date.accessioned2023-04-03T16:18:56Z
dc.date.available2023-04-03T16:18:56Z
dc.date.issued2019-12-16
dc.description.abstractThis 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.en_US
dc.description.departmentManagement Science and Statisticsen_US
dc.description.sponsorshipNational Natural Science Foundation of Chinaen_US
dc.identifier.citationWang, Q., Qi, F., Sun, M., Qu, J., & Xue, J. (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, 2019, 9142753. doi:10.1155/2019/9142753en_US
dc.identifier.issn1687-5273
dc.identifier.otherhttps://doi.org/10.1155/2019/9142753
dc.identifier.urihttps://hdl.handle.net/20.500.12588/1805
dc.language.isoen_USen_US
dc.publisherHindawien_US
dc.relation.ispartofseriesComputational Intelligence and Neuroscience, 2019,;
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.titleIdentification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniquesen_US
dc.typeArticleen_US

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