Leaf Classification

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

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