Leaf Classification
Paper #1 - Plant identification using deep neural networks via optimization of transfer learning parameters
Key Notes
- 1.2 million labeled images of 1,000 different categories from the ImageNet = one thousand two hundred per class
- LifeCLEF 2015 - 91,758 labeled images of different plant organs (e.g. flowers, fruits, leaves, and stems), from 1,000 - 91 per class
Parts of Plant
- Branch
- Entire
- Flower
- Fruit
- Leaf
- LeafScan
- Stem
- Overall
- Increasing the batch size from 20 to 60 improves the overall accuracy
- 80 patches for data augmentation
Paper #2 - Multi-Organ Plant Classification Based on Convolutional and Recurrent Neural Networks
Key Notes
- Feature engineering approaches such as Scale-invariant
- feature transform (SIFT), Bag of Word (Bow), Speeded-Up
- Robust Features (SURF), Gabor, Local Binary Pattern (LBP).
- Most generally used features to distinguish leaves of different species
- Hybrid generic-organ convolutional neural network, abbreviated HGO-CNN
- Three different sizes: 256, 384 and 512
- Crop 256 × 256 center pixels
- Multi-Scale Plant Images Generation
- During network training, 224 × 224 pixels are randomly cropped from the rescaled images and fed into the network
Keep Exploring!!!
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