Plant Leaves Classification: A Few-Shot Learning Method Based on Siamese Network

Wang, Bin; Dian, Wang

doi:10.1109/access.2019.2947510

Cited by 78 publications

(40 citation statements)

References 29 publications

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“…(iii) It is difficult to collect the leaves in actual works. Therefore, the problem of few-shot learning is urgent to be solved (i.e., Wang et al proposed a method based on Siamese network for plant leaves classification [45]). All in all, by defining new methods to solve the problem of tomato leaf disease identification, we strive to achieve continuous improvement in performance.…”

Section: Discussionmentioning

confidence: 99%

DCGAN-Based Data Augmentation for Tomato Leaf Disease Identification

2020

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Tomato leaf disease seriously affects the yield of tomato. It is extremely vital for agricultural economy to identify agricultural diseases. The traditional data augmentation methods, such as rotation, flip and translation, are severely limited, which cannot achieve good generalization results. To improve the recognition accuracy of tomato leaf diseases, a new method of data augmentation by generative adversarial networks (GANs) is proposed for leaf disease recognition in this work. Generated images augmented by deep convolutional generative adversarial networks (DCGAN) and original images as the input of GoogLeNet, this model can achieve a top-1 average identification accuracy of 94.33%. By adjusting the hyper-parameters, modifying the architecture of the convolutional neural networks, and selecting different generative adversarial networks, an improved model for training and testing 5 classes of tomato leaf images was obtained. Meanwhile, images generated by DCGAN not only enlarge the size of the data set, but also have the characteristics of diversity, which makes the model have a good generalization effect. We have also visually confirmed that the images generated by DCGAN have much better quality and are more convincing through the t-Distributed Stochastic Neighbor Embedding (t-SNE) and Visual Turing Test. Experiments with tomato leaf disease identification show that DCGAN can generate data that approximate to real images, which can be used to (1) provide a larger data set for the training of large neural networks, and improve the performance of the recognition model through highly discriminating image generation technology; (2) reduce the cost of data collection; (3) enhance the diversity of data and the generalization ability of the recognition models.

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Section: Discussionmentioning

confidence: 99%

DCGAN-Based Data Augmentation for Tomato Leaf Disease Identification

2020

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“…The proposed model for PRPD fault diagnosis is based on the architecture of one-shot learning [ 33 ]. Figure 7 shows the architecture of the proposed one-shot learning model, where the dataset is divided into three parts, namely, a training set

, test set

, and support set

.…”

Section: Proposed Methodsmentioning

confidence: 99%

One-Shot Learning for Partial Discharge Diagnosis Using Ultra-High-Frequency Sensor in Gas-Insulated Switchgear

Tuyet-Doan

Tran-Thi

et al. 2020

Sensors

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In recent years, deep learning has been successfully used in order to classify partial discharges (PDs) for assessing the condition of insulation systems in different electrical equipment. However, fault diagnosis using deep learning is still challenging, as it requires a large amount of training data, which is difficult and expensive to obtain in the real world. This paper proposes a novel one-shot learning method for fault diagnosis using a small dataset of phase-resolved PDs (PRPDs) in a gas-insulated switchgear (GIS). The proposed method is based on a Siamese network framework, which employs a distance metric function for predicting sample pairs from the same PRPD class or different PRPD classes. Experimental results over the small PRPD dataset that was obtained from an ultra-high-frequency sensor in the GIS show that the proposed method achieves outstanding performance for PRPD fault diagnosis as compared with the previous methods.

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“…At present, siamese network is widely used to solve the problem of insufficient training samples. For example, Wang and Wang [20] proposed an improved siamese network to solve the problem of leaf classification in the case of insufficient samples. With limited training samples, Sun et al [21] realized the efficient identification of voltage sag sources by designing siamese networks.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network-Based Radar Jamming Signal Classification With Sufficient and Limited Samples

2020

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Jamming is a big threat to radar system survival and anti-jamming is a part of the solution. The classification of radar jamming signal is the first step toward to anti-jamming. Recently, as an important part of deep learning, convolutional neural network (CNN) based methods have shown their capability in discriminant feature extraction and accurate classification. In this study, in order to harness the powerfulness of deep learning, CNN based methods are proposed to classify radar jamming signal acting on pulse compression radar. Specifically, a 1D-CNN is designed for radar jamming signal classification under the condition of sufficient training samples. Furthermore, due to the fact that the collection of sufficient training samples is time-consuming and expensive, a CNN-based siamese network is proposed for radar jamming signal classification to deal with the issue of limited training samples. The experimental results with sufficient and limited training samples show that the CNN-based classification methods obtain good classification performance in terms of classification accuracy and show a huge potential for radar jamming signal classification. INDEX TERMS Radar jamming signal, convolutional neural network (CNN), sufficient and limited training samples, siamese network.

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Plant Leaves Classification: A Few-Shot Learning Method Based on Siamese Network

Cited by 78 publications

References 29 publications

DCGAN-Based Data Augmentation for Tomato Leaf Disease Identification

DCGAN-Based Data Augmentation for Tomato Leaf Disease Identification

One-Shot Learning for Partial Discharge Diagnosis Using Ultra-High-Frequency Sensor in Gas-Insulated Switchgear

Convolutional Neural Network-Based Radar Jamming Signal Classification With Sufficient and Limited Samples

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