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Lodging, the permanent bending over of food crops, leads to poor plant growthand development. Consequently, lodging results in reduced crop quality, lowerscrop yield, and makes harvesting difficult. Plant breeders routinely evaluateseveral thousand breeding lines, and therefore, automatic lodging detection andprediction is of great value aid in selection. In this paper, we propose a deepconvolutional neural network (DCNN) architecture for lodging classificationusing five spectral channel orthomosaic images from canola and wheat breedingtrials. Also, using transfer learning, we trained 10 lodging detection modelsusing well-established deep convolutional neural network architectures. Ourproposed model outperforms the state-of-the-art lodging detection methods inthe literature that use only handcrafted features. In comparison to 10 DCNNlodging detection models, our proposed model achieves comparable results whilehaving a substantially lower number of parameters. This makes the proposedmodel suitable for applications such as real-time classification usinginexpensive hardware for high-throughput phenotyping pipelines. The GitHubrepository at https://github.com/FarhadMaleki/LodgedNet contains code andmodels.

Crop Lodging Prediction from UAV-Acquired Images of Wheat and Canola using a DCNN Augmented with Handcrafted Texture Features