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

Open access
Fuentes, Alvaro, Yoon, Sook, Kim, Sang, Park, Dong·2017·Sensors
DOI 10.3390/s17092022
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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.

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Metadata

Authors
Fuentes, A., Yoon, S., Kim, S., & Park, D.
Venue
Sensors
Publisher
MDPI AG
Published
2017-09-04
Volume
17
Issue
9
Pages
2022
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s17092022
OpenAlex
W2753403518
Cited by
1,572
References
12
FWCI
70.54
Type
journal article
Language
en
Open access
gold
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
  • The “Research Base Construction Fund Support Program” funded by Chonbuk National University in 2016
  • This work was carried out with the support of "Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ0120642016)" Rural Development Administration, Republic of Korea.

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CoChat. (2026). Citation check: A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition. https://app.cochat.ai/papers/10.3390/s17092022
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Fuentes, A., Yoon, S., Kim, S., & Park, D. (2017). A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition. Sensors. https://doi.org/10.3390/s17092022

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