  Lodging, the permanent bending over of food crops, leads to poor plant growth
and development. Consequently, lodging results in reduced crop quality, lowers
crop yield, and makes harvesting difficult. Plant breeders routinely evaluate
several thousand breeding lines, and therefore, automatic lodging detection and
prediction is of great value aid in selection. In this paper, we propose a deep
convolutional neural network (DCNN) architecture for lodging classification
using five spectral channel orthomosaic images from canola and wheat breeding
trials. 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 lower number of parameters. This makes the proposed
model suitable for applications such as real-time classification using
inexpensive hardware for high-throughput phenotyping pipelines. The GitHub
repository at https://github.com/FarhadMaleki/LodgedNet contains code and
models.
