@article{SchirrmannLandwehrGiebeletal.2021,
  author    = {Schirrmann, Michael and Landwehr, Niels and Giebel, Antje and Garz, Andreas and Dammer, Karl-Heinz},
  title     = {Early detection of stripe rust in winter wheat using deep residual neural networks},
  series = {Frontiers in plant science : FPLS},
  volume    = {12},
  journal   = {Frontiers in plant science : FPLS},
  publisher = {Frontiers Media},
  address   = {Lausanne},
  issn      = {1664-462X},
  doi       = {10.3389/fpls.2021.469689},
  pages     = {14},
  year      = {2021},
  abstract  = {Stripe rust (Pst) is a major disease of wheat crops leading untreated to severe yield losses. The use of fungicides is often essential to control Pst when sudden outbreaks are imminent. Sensors capable of detecting Pst in wheat crops could optimize the use of fungicides and improve disease monitoring in high-throughput field phenotyping. Now, deep learning provides new tools for image recognition and may pave the way for new camera based sensors that can identify symptoms in early stages of a disease outbreak within the field. The aim of this study was to teach an image classifier to detect Pst symptoms in winter wheat canopies based on a deep residual neural network (ResNet). For this purpose, a large annotation database was created from images taken by a standard RGB camera that was mounted on a platform at a height of 2 m. Images were acquired while the platform was moved over a randomized field experiment with Pst-inoculated and Pst-free plots of winter wheat. The image classifier was trained with 224 x 224 px patches tiled from the original, unprocessed camera images. The image classifier was tested on different stages of the disease outbreak. At patch level the image classifier reached a total accuracy of 90\%. To test the image classifier on image level, the image classifier was evaluated with a sliding window using a large striding length of 224 px allowing for fast test performance. At image level, the image classifier reached a total accuracy of 77\%. Even in a stage with very low disease spreading (0.5\%) at the very beginning of the Pst outbreak, a detection accuracy of 57\% was obtained. Still in the initial phase of the Pst outbreak with 2 to 4\% of Pst disease spreading, detection accuracy with 76\% could be attained. With further optimizations, the image classifier could be implemented in embedded systems and deployed on drones, vehicles or scanning systems for fast mapping of Pst outbreaks.},
  language  = {en}
}
@article{CamargoSchirrmannLandwehretal.2021,
  author    = {Camargo, Tibor de and Schirrmann, Michael and Landwehr, Niels and Dammer, Karl-Heinz and Pflanz, Michael},
  title     = {Optimized deep learning model as a basis for fast UAV mapping of weed species in winter wheat crops},
  series = {Remote sensing / Molecular Diversity Preservation International (MDPI)},
  volume    = {13},
  journal   = {Remote sensing / Molecular Diversity Preservation International (MDPI)},
  number    = {9},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2072-4292},
  doi       = {10.3390/rs13091704},
  pages     = {19},
  year      = {2021},
  abstract  = {Weed maps should be available quickly, reliably, and with high detail to be useful for site-specific management in crop protection and to promote more sustainable agriculture by reducing pesticide use. Here, the optimization of a deep residual convolutional neural network (ResNet-18) for the classification of weed and crop plants in UAV imagery is proposed. The target was to reach sufficient performance on an embedded system by maintaining the same features of the ResNet-18 model as a basis for fast UAV mapping. This would enable online recognition and subsequent mapping of weeds during UAV flying operation. Optimization was achieved mainly by avoiding redundant computations that arise when a classification model is applied on overlapping tiles in a larger input image. The model was trained and tested with imagery obtained from a UAV flight campaign at low altitude over a winter wheat field, and classification was performed on species level with the weed species Matricaria chamomilla L., Papaver rhoeas L., Veronica hederifolia L., and Viola arvensis ssp. arvensis observed in that field. The ResNet-18 model with the optimized image-level prediction pipeline reached a performance of 2.2 frames per second with an NVIDIA Jetson AGX Xavier on the full resolution UAV image, which would amount to about 1.78 ha h(-1) area output for continuous field mapping. The overall accuracy for determining crop, soil, and weed species was 94\%. There were some limitations in the detection of species unknown to the model. When shifting from 16-bit to 32-bit model precision, no improvement in classification accuracy was observed, but a strong decline in speed performance, especially when a higher number of filters was used in the ResNet-18 model. Future work should be directed towards the integration of the mapping process on UAV platforms, guiding UAVs autonomously for mapping purpose, and ensuring the transferability of the models to other crop fields.},
  language  = {en}
}