Next Article in Journal
Secure Communications in CIoT Networks with a Wireless Energy Harvesting Untrusted Relay
Next Article in Special Issue
Hyperspectral Imaging Analysis for the Classification of Soil Types and the Determination of Soil Total Nitrogen
Previous Article in Journal
Experimental and Numerical Investigations on the Mechanical Characteristics of Carbon Fiber Sensors
Previous Article in Special Issue
Novelty Detection Classifiers in Weed Mapping: Silybum marianum Detection on UAV Multispectral Images
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle

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

Department of Electronics Engineering, Chonbuk National University, Jeonbuk 54896, Korea
Research Institute of Realistic Media and Technology, Mokpo National University, Jeonnam 534-729, Korea
Department of Computer Engineering, Mokpo National University, Jeonnam 534-729, Korea
National Institute of Agricultural Sciences, Suwon 441-707, Korea
IT Convergence Research Center, Chonbuk National University, Jeonbuk 54896, Korea
Author to whom correspondence should be addressed.
Sensors 2017, 17(9), 2022;
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)
PDF [13540 KB, uploaded 6 September 2017]


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

Figure 1

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).
Printed Edition Available!
A printed edition of this Special Issue is available here.

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top