Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Uwe Knauer"'
Detection of Apple Proliferation Disease Using Hyperspectral Imaging and Machine Learning Techniques
Autor:
Uwe Knauer, Sebastian Warnemünde, Patrick Menz, Bonito Thielert, Lauritz Klein, Katharina Holstein, Miriam Runne, Wolfgang Jarausch
Publikováno v:
Sensors, Vol 24, Iss 23, p 7774 (2024)
Apple proliferation is among the most important diseases in European fruit production. Early and reliable detection enables farmers to respond appropriately and to prevent further spreading of the disease. Traditional phenotyping approaches by human
Externí odkaz:
https://doaj.org/article/9d41a9e49dce43b5bc936eb2bcec5402
Publikováno v:
Agronomy, Vol 14, Iss 2, p 376 (2024)
Apple proliferation (AP) is an economically important disease in many apple-growing regions caused by ‘Candidatus Phytoplasma mali’ which is spread by migrating psyllid vectors on a regional scale. As infected trees in orchards are the only inocu
Externí odkaz:
https://doaj.org/article/377c7cc420b747879cb0cabf2ce58fa4
Autor:
Paul Herzig, Peter Borrmann, Uwe Knauer, Hans-Christian Klück, David Kilias, Udo Seiffert, Klaus Pillen, Andreas Maurer
Publikováno v:
Remote Sensing, Vol 13, Iss 14, p 2670 (2021)
With advances in plant genomics, plant phenotyping has become a new bottleneck in plant breeding and the need for reliable high-throughput plant phenotyping techniques has emerged. In the face of future climatic challenges, it does not seem appropria
Externí odkaz:
https://doaj.org/article/ec9afffcfdeb412b94eed90d0ec6031b
Publikováno v:
Plant Methods, Vol 13, Iss 1, Pp 1-15 (2017)
Abstract Background Hyperspectral imaging is an emerging means of assessing plant vitality, stress parameters, nutrition status, and diseases. Extraction of target values from the high-dimensional datasets either relies on pixel-wise processing of th
Externí odkaz:
https://doaj.org/article/8ab72e8a09e048408942cd81d3968824
Autor:
Wolfgang Jarausch, Patrick Menz, Ali Al Masri, Miriam Runne, Bonito Thielert, Katrin Kohler, Sebastian Warnemunde, David Kilias, Barbara Jarausch, Uwe Knauer
Publikováno v:
Phytopathogenic Mollicutes. 13:135-136
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::58dd88024df2cec25e66893a85c09d80
https://doi.org/10.5958/2249-4677.2023.00068.3
https://doi.org/10.5958/2249-4677.2023.00068.3
Autor:
Klaus Pillen, Uwe Knauer, Hans-Christian Klück, Peter Borrmann, David Kilias, Andreas Maurer, Udo Seiffert, Paul Herzig
Publikováno v:
Remote Sensing; Volume 13; Issue 14; Pages: 2670
Remote Sensing, Vol 13, Iss 2670, p 2670 (2021)
Remote Sensing, Vol 13, Iss 2670, p 2670 (2021)
With advances in plant genomics, plant phenotyping has become a new bottleneck in plant breeding and the need for reliable high-throughput plant phenotyping techniques has emerged. In the face of future climatic challenges, it does not seem appropria
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a666f701f15e67b3b7d7365e42544f58
https://publica.fraunhofer.de/handle/publica/270241
https://publica.fraunhofer.de/handle/publica/270241
Autor:
Seiffert, Uwe Knauer, Cornelius Styp von Rekowski, Marianne Stecklina, Tilman Krokotsch, Tuan Pham Minh, Viola Hauffe, David Kilias, Ina Ehrhardt, Herbert Sagischewski, Sergej Chmara, Udo
Publikováno v:
Remote Sensing; Volume 11; Issue 23; Pages: 2788
In this paper, we evaluate different popular voting strategies for fusion of classifier results. A convolutional neural network (CNN) and different variants of random forest (RF) classifiers were trained to discriminate between 15 tree species based
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=multidiscipl::8985cb34cedc971aa9d1454b2e2f655b
Publikováno v:
Plant Methods
Plant Methods, Vol 13, Iss 1, Pp 1-15 (2017)
Plant Methods, Vol 13, Iss 1, Pp 1-15 (2017)
Background Hyperspectral imaging is an emerging means of assessing plant vitality, stress parameters, nutrition status, and diseases. Extraction of target values from the high-dimensional datasets either relies on pixel-wise processing of the full sp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7203ba3cf45f26e74e99c3ae3b7f2ada
https://doi.org/10.1186/s13007-017-0198-y
https://doi.org/10.1186/s13007-017-0198-y
Publikováno v:
Neural Computing and Applications. 26:253-262
Ensembles of RBF networks trained with $$\gamma$$ ? -divergence-based similarity measures can improve classification accuracy of hyperspectral imaging data significantly compared to any single RBF network as well as to RBF ensembles based on the Eucl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::db69baaf82d1e27dd271c5c78da61775
https://doi.org/10.1007/s00521-014-1634-9
https://doi.org/10.1007/s00521-014-1634-9
Publikováno v:
SSCI
A significant increase in the accuracy of hyper spectral image classification has been achieved by using ensembles of radial basis function networks trained with different number of neurons and different distance metrics. Best results have been obtai
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::33a30e1592382172cabf6218217e048a
https://doi.org/10.1109/ssci.2015.55
https://doi.org/10.1109/ssci.2015.55