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

By Alvaro Fuentes, Sook Yoon, Sang Cheol Kim, Dong Sun Park. Italian National Conference on Sensors, 2017

1,358 citations recorded by the source corpus.

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.

Subfields: Analytical Chemistry, Plant Science

View Paper: https://www.semanticscholar.org/paper/886674a5abae3c9bfd262006c67d4ba078e1462b

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Citations in the corpus

Automatic and Reliable Leaf Disease Detection Using Deep Learning Techniques (2021, background): “Fuentes et al. [22] used three types of detectors to identify 10 diseases from tomato leaf images: Faster Region-based Convolutional Neural Network (Faster R-CNN), Region-based Completely Convolutional Network (R-FCN), and Single Shot Multibox Detector (SSD).”
Plant Disease Detection and Classification by Deep Learning (2019, methodology): “DL models with some detectors were implemented in [68], in which the diseases in plants were marked along with their prediction percentage.”
Identification and recognition of rice diseases and pests using convolutional neural networks (2018, methodology): “In recent years, these techniques have been used to analyse diseases of tea [14], apple [21], tomato [13], grapevine, peach, and pear [19].”
Hybrid SVM-LR Classifier for Powdery Mildew Disease Prediction in Tomato Plant (2020, methodology): “In 2017, Fuentes et al. [5] have also contributed in this research by using different deep learning approaches namely Single Shot Multibox Detector (SSD), Faster Region-based CNN , and Region-based Fully CNN on a large tomato disease and pest dataset.”
PlantDoc: A Dataset for Visual Plant Disease Detection (2019, background): “A host of other recent works have looked at convolutional neural network variants for disease detection using plant leaf images [7, 21].”
Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification (2021, background): “The precision would be lower in practical application due to the insufﬁcient number of samples [72].”
Plant Disease Detection and Classification by Deep Learning—A Review (2021, methodology): “[74] used Faster R-CNN, R-FCN, and SSD architectures to locate lesion areas of 9 kinds of tomato leaf diseases and insect pests, and classified them according to the bounding box.”