Crop Lodging Prediction From UAV-Acquired Images of Wheat and Canola Using a DCNN Augmented With Handcrafted Texture Features

@article{Mardanisamani2019CropLP,
  title={Crop Lodging Prediction From UAV-Acquired Images of Wheat and Canola Using a DCNN Augmented With Handcrafted Texture Features},
  author={Sara Mardanisamani and Farhad Maleki and Sara Hosseinzadeh Kassani and Sajith Rajapaksa and Hema Sudhakar Duddu and Menglu Wang and Steven J. Shirtliffe and Seungbum Ryu and Anique Josuttes and Ti Zhang and Sally Vail and Curtis J. Pozniak and Isobel A. P. Parkin and Ian Stavness and Mark G. Eramian},
  journal={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
  year={2019},
  pages={2657-2664}
}
Lodging, the permanent bending over of food crops, leads to poor plant growth and development. [...] Key Method Also, using transfer learning, we trained 10 lodging detection models using well-established deep convolutional neural network architectures. Our proposed model outperforms the state-of-the-art lodging detection methods in the literature that use only handcrafted features. In comparison to 10 DCNN lodging detection models, our proposed model achieves comparable results while having a substantially…Expand
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