Hybrid approches using for plant classification

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There are the links of papers

using SLIC and CNN for plant classisfication and leaves classification.

I must read it on the current week. 24Jun to 29Jun.

  1. https://www.mdpi.com/2073-8994/9/3/31
  2. http://eprints.nottingham.ac.uk/34197/1/MVAP-D-15-00134_Revised_manuscript.pdf
  3. https://www.researchgate.net/publication/221826758_SLIC_A_Method_for_Sequence-_and_Ligation-Independent_Cloning
  4. https://ieeexplore.ieee.org/document/7966067/metrics#metrics
  5. https://peerj.com/articles/5036/
  6. https://ieeexplore.ieee.org/document/7966067
  7. https://www.semanticscholar.org/paper/A-hybrid-machine-learning-approach-to-automatic-for-Yahata-Onishi/f0be59cebe9c67353f5e84fe3abdca4cc360f03b

Using AutoEncoder(AE) and convolutional neural network (CNN) and SVM approaches

hybrid deep learning is introduced, which included AutoEncoder(AE) and convolutional neural network (CNN). This neural network is applied for extracting the features of the plant leaves. In this paper, we proved that hybrid deep learning can extract better features for classification task. We apply the hybrid deep learning to extract features of leaf pictures, and then we classify leaves using those features with SVM, the result suggests that this method is not only better than pure SVM, but also better than pure AE and pure CNN.

  1. https://www.researchgate.net/publication/283862136_Hybrid_Deep_Learning_for_Plant_Leaves_Classification
  2. https://link.springer.com/chapter/10.1007/978-3-319-22186-1_11
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