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Sensors 2017, 17(9), 2022; https://doi.org/10.3390/s17092022

A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition

1
Department of Electronics Engineering, Chonbuk National University, Jeonbuk 54896, Korea
2
Research Institute of Realistic Media and Technology, Mokpo National University, Jeonnam 534-729, Korea
3
Department of Computer Engineering, Mokpo National University, Jeonnam 534-729, Korea
4
National Institute of Agricultural Sciences, Suwon 441-707, Korea
5
IT Convergence Research Center, Chonbuk National University, Jeonbuk 54896, Korea
*
Author to whom correspondence should be addressed.
Received: 10 July 2017 / Revised: 24 August 2017 / Accepted: 28 August 2017 / Published: 4 September 2017
(This article belongs to the Special Issue Sensors in Agriculture)
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Abstract

Plant Diseases and Pests are a major challenge in the agriculture sector. An accurate and a faster detection of diseases and pests in plants could help to develop an early treatment technique while substantially reducing economic losses. Recent developments in Deep Neural Networks have allowed researchers to drastically improve the accuracy of object detection and recognition systems. In this paper, we present a deep-learning-based approach to detect diseases and pests in tomato plants using images captured in-place by camera devices with various resolutions. Our goal is to find the more suitable deep-learning architecture for our task. Therefore, we consider three main families of detectors: Faster Region-based Convolutional Neural Network (Faster R-CNN), Region-based Fully Convolutional Network (R-FCN), and Single Shot Multibox Detector (SSD), which for the purpose of this work are called “deep learning meta-architectures”. We combine each of these meta-architectures with “deep feature extractors” such as VGG net and Residual Network (ResNet). We demonstrate the performance of deep meta-architectures and feature extractors, and additionally propose a method for local and global class annotation and data augmentation to increase the accuracy and reduce the number of false positives during training. We train and test our systems end-to-end on our large Tomato Diseases and Pests Dataset, which contains challenging images with diseases and pests, including several inter- and extra-class variations, such as infection status and location in the plant. Experimental results show that our proposed system can effectively recognize nine different types of diseases and pests, with the ability to deal with complex scenarios from a plant’s surrounding area. View Full-Text
Keywords: plant disease; pest; deep convolutional neural networks; real-time processing; detection plant disease; pest; deep convolutional neural networks; real-time processing; detection
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Fuentes, A.; Yoon, S.; Kim, S.C.; Park, D.S. A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition. Sensors 2017, 17, 2022.

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