Commit c23c23ce authored by Henrik Skov Midtiby's avatar Henrik Skov Midtiby

Added a bibtex file.

parent 5bf1af44
@article{Midtiby2012psep,
abstract = {Successful intra-row mechanical weed control of sugar beet (beta vulgaris) in early growth stages requires precise knowledge about location of crop plants. A computer vision system for locating plant stem emerging point (PSEP) of sugar beet in early growth stages was developed and tested. The system is based on detection of individual leaves; each leaf location is then described by centre of mass and petiole location. After leaf detection were the true PSEP locations annotated manually and a multivariate normal distribution model of the PSEP relative to the located leaf was built. From testing the system, PSEP estimates based on a single leaf have an average error of {\^{a}}ˆ¼3{\&}{\#}xa0;mm. When several leaves are detected the average error decreases to less than 2{\&}{\#}xa0;mm.},
author = {Midtiby, Henrik S and Giselsson, Thomas M and J{\o}rgensen, Rasmus N},
doi = {10.1016/j.biosystemseng.2011.10.011},
issn = {15375110},
journal = {Biosystems Engineering},
month = {1},
number = {1},
pages = {83--90},
title = {{Estimating the plant stem emerging points (PSEPs) of sugar beets at early growth stages}},
url = {http://www.sciencedirect.com/science/article/pii/S1537511011001954 http://linkinghub.elsevier.com/retrieve/pii/S1537511011001954},
volume = {111},
year = {2012}
}
@article{Dyrmann2016PlantSpeciesClassification,
abstract = {Information on which weed species are present within agricultural fields is important for site specific weed management. This paper presents a method that is capable of recognising plant species in colour images by using a convolutional neural network. The network is built from scratch trained and tested on a total of 10,413 images containing 22 weed and crop species at early growth stages. These images originate from six different data sets, which have variations with respect to lighting, resolution, and soil type. This includes images taken under controlled conditions with regard to camera stabilisation and illumination, and images shot with hand-held mobile phones in fields with changing lighting conditions and different soil types. For these 22 species, the network is able to achieve a classification accuracy of 86.2{\%}.},
author = {Dyrmann, Mads and Karstoft, Henrik and Midtiby, Henrik Skov},
doi = {10.1016/j.biosystemseng.2016.08.024},
file = {:home/henrik/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Dyrmann, Karstoft, Midtiby - 2016 - Plant species classification using deep convolutional neural network.pdf:pdf},
issn = {15375110},
journal = {Biosystems Engineering},
keywords = {Deep learning,Networks,Pl,[Convolutional Neural},
month = {11},
pages = {72--80},
title = {{Plant species classification using deep convolutional neural network}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S1537511016301465},
volume = {151},
year = {2016}
}
@article{Dyrmann2018EstimationOfPlantSpecies,
abstract = {Information on which weed species are present within agricultural fields is a prerequisite when using robots for site-specific weed management. This study proposes a method of improving robustness in shape-based classifying of seedlings toward natural shape variations within each plant species. To do so, leaves are separated from plants and classified individually together with the classification of the whole plant. The classification is based on common, rotation-invariant features. Based on previous classifications of leaves and plants, confidence in correct assignment is created for the plants and leaves, and this confidence is used to determine the species of the plant. By using this approach, the classification accuracy of eight plants species at early growth stages is increased from 93.9{\%} to 96.3{\%}.},
author = {Dyrmann, Mads and Christiansen, Peter and Midtiby, Henrik Skov},
doi = {10.1002/rob.21734},
file = {:home/henrik/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Dyrmann, Christiansen, Midtiby - 2018 - Estimation of plant species by classifying plants and leaves in combination.pdf:pdf},
issn = {15564959},
journal = {Journal of Field Robotics},
keywords = {Bayes belief integration,automated weed control,classifier fusion,computer vision,excessive green,phenotyping,plant classification},
month = {3},
number = {2},
pages = {202--212},
title = {{Estimation of plant species by classifying plants and leaves in combination}},
url = {http://doi.wiley.com/10.1002/rob.21734},
volume = {35},
year = {2018}
}
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