Sisi Fang’s research while affiliated with Chinese Academy of Sciences and other places

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Publications (3)


Crop Disease Image Classification Based on Transfer Learning with DCNNs: First Chinese Conference, PRCV 2018, Guangzhou, China, November 23-26, 2018, Proceedings, Part II
  • Chapter

November 2018

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170 Reads

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16 Citations

Lecture Notes in Computer Science

Yuan Yuan

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Sisi Fang

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Machine learning has been widely used in the crop disease image classification. Traditional methods relying on the extraction of hand-crafted low-level image features are difficulty to get satisfactory results. Deep convolutional neural network can deal with this problem because of automatically learning the feature representations from raw image data, but require enough labeled data to obtain a good generalization performance. However, in the field of agriculture, the available labeled data in target task is limited. In order to solve this problem, this paper proposes a method which combines transfer learning with two popular deep learning architectures (i.e., AlexNet and VGGNet) to classify eight kinds of crop diseases images. First, during the training procedure, the batch normalization and DisturbLabel techniques are introduced into these two networks to reduce the number of training iterations and over-fitting. Then, after training the pre-trained model by using the open source dataset PlantVillage. Finally, we fine-tune this model with our relatively small dataset preprocessed by a proposed strategy. The experimental results reveal that our approach can achieve an average accuracy of 95.93% compared to state-of-the-art method for our relatively small dataset, demonstrating the feasibility and robustness of this approach.


Figure 1: Visual examples of our introduced new dataset: AES-CD9214, which contains various natural scenes, such as different object sizes, shooting angles, poses, and backgrounds.
The disease name and number of corresponding images in PlantVillage dataset [11]
Comparison of Recognition Accuracy on PlantVillage Dataset when Adding Gaussian Noise
Comparison of Recognition Accuracy on PlantVillage Dataset when Adding Salt& Pepper Noise
Comparison of Recognition Accuracy on PlantVillage Dataset when Adding Gaussian and Salt& Pepper Noise
High-Order Residual Convolutional Neural Network for Robust Crop Disease Recognition
  • Conference Paper
  • Full-text available

October 2018

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565 Reads

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29 Citations

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Miao Li

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Jian Zhang

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[...]

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Jingxian Wang

Fast1 and robust recognition of crop diseases is the basis for crop disease prevention and control. It is also an important guarantee for crop yield and quality. Most crop disease recognition methods focus on improving the recognition accuracy on public datasets, but ignoring the anti-interference ability of the methods, which result in poor recognition accuracy when the real scene is applied. In this paper, we propose a high-order residual convolutional neural network (HOResNet) for accurate and robust recognizing crop diseases. Our HOResNet is capable of exploiting low-level features with object details and high-level features with abstract representation simultaneously in order to improve the anti-interference ability. Furthermore, in order to better verify the anti-interference ability of our approach, we introduce a new dataset, which contains 9,214 images of six diseases of Rice and Cucumber. This dataset is collected in the natural environment. The images in the dataset have different sizes, shooting angles, poses, backgrounds and illuminations. Extensive experimental results demonstrate that our approach achieves the highest accuracy on the datasets tested. In addition, when the input images are added to different levels of noise interference, our approach still obtains higher recognition accuracy than other methods.

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Crop Disease Image Recognition Based on Transfer Learning

December 2017

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74 Reads

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13 Citations

Lecture Notes in Computer Science

Machine learning has been widely applied to the crop disease image recognition. Traditional machine learning needs to satisfy two basic assumptions: (1) The training and test data should be under the same distribution; (2) A large scale of labeled training samples is required to learn a reliable classification model. However, in many cases, these two assumptions cannot be satisfied. In the field of agriculture, there are not enough labeled crop disease images. In order to solve this problem, the paper proposed a method which introduced transfer learning to the crop disease image recognition. Firstly, the double Otsu method was applied to obtain the spot images of five kinds of cucumber and rice diseases. Then, color feature, texture feature and shape feature of spot images were extracted. Next, the TrAdaBoost-based method and other baseline methods were used to identify diseases. And experimental results indicate that the TrAdaBoost-based method can implement samples transfer between the auxiliary and target domain and achieve the better results than the other baseline methods. Meanwhile, the results show that transfer learning is helpful in the crop disease image recognition while the training sample is not enough.

Citations (3)


... Leaf diseases are the primary issue that reduces agricultural productivity [1]. According to the studies, 50% of crop losses are caused by plant diseases and pets [2]. Managing and controlling diseases is essential to increasing crop productivity. ...

Reference:

A Review of Leaf Diseases Detection and Classification by Deep Learning
Crop Disease Image Classification Based on Transfer Learning with DCNNs: First Chinese Conference, PRCV 2018, Guangzhou, China, November 23-26, 2018, Proceedings, Part II
  • Citing Chapter
  • November 2018

Lecture Notes in Computer Science

... 18,160 images from the PlantVillage dataset, achieving 94.85% accuracy in just 30 iterations. Additionally, in [49], the high-order residual CNN (HOResNet) architecture was proposed to detect diseases in six categories with 10,478 PlantVillage images, reaching an accuracy of 91.79% after 100 iterations. Furthermore, in [50], SqueezeNet and AlexNet networks were tested on Nvidia Jetson Tx1 with PlantVillage images, where AlexNet achieved 95.65% accuracy. ...

High-Order Residual Convolutional Neural Network for Robust Crop Disease Recognition

... In recent years, a transfer learning method based on CNN (convolutional neural network) parameters has been invented. Some researchers have proposed fine-tuning the training parameters of ImageNet datasets based on network models such as ResNet to achieve recognition of other target datasets [32]. ...

Crop Disease Image Recognition Based on Transfer Learning
  • Citing Chapter
  • December 2017

Lecture Notes in Computer Science