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2020 | OriginalPaper | Buchkapitel

6. Phenotyping and Genotyping of Plants

Phenotyping of Crop Plants Using Spectral Sensors and Artificial Intelligence

verfasst von : Udo Seiffert, Prof., Andreas Herzog, Dr.

Erschienen in: Biological Transformation

Verlag: Springer Berlin Heidelberg

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Summary

The digitization of the economy and society naturally does not fail to include agriculture. Having a plant‐based bioeconomy as primary production for many downstream industries is a key component of digital transformation. The biological transformation of the economy, or rather of the world, derived from the much‐vaunted trend of digitization, is thereby extended by another very interesting component. This is ultimately the optimization—at least at specific points—of biology by biology. In other words, the optimization of plants as biological systems is carried out using technological processes and methods, the approach and design of which have been modeled on or inspired by various biological principles. On the technological side, bio‐inspired systems include spectral sensors and, in particular, artificial intelligence as the central component required for the phenotyping needed to achieve the aforementioned optimization.

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Literatur
3.
Zurück zum Zitat Peukert M, Lim WL, Seiffert U, Matros A (2016) Mass Spectrometry Imaging of Metabolites in Barley Grain Tissues. In: Current Protocols in Plant Biology. Wiley, Hoboken, p 574–591 Peukert M, Lim WL, Seiffert U, Matros A (2016) Mass Spectrometry Imaging of Metabolites in Barley Grain Tissues. In: Current Protocols in Plant Biology. Wiley, Hoboken, p 574–591
4.
Zurück zum Zitat Arens N, Backhaus A, Döll S, Fischer S, Seiffert U, Mock HP (2016) Noninvasive presymptomatic detection of Cercospora beticola infection and identification of early metabolic responses in sugar beet. Frontiers in Plant Science 7:1377CrossRef Arens N, Backhaus A, Döll S, Fischer S, Seiffert U, Mock HP (2016) Noninvasive presymptomatic detection of Cercospora beticola infection and identification of early metabolic responses in sugar beet. Frontiers in Plant Science 7:1377CrossRef
5.
Zurück zum Zitat Kaspar-Schönefeld S, Merx K, Jozefowicz AM, Hartmann A, Seiffert U, Weschke W, Matros A, Mock HP (2016) Label-free proteome profiling reveals developmental-dependent patterns in young barley grains. Journal of Proteomics 143:106–121CrossRef Kaspar-Schönefeld S, Merx K, Jozefowicz AM, Hartmann A, Seiffert U, Weschke W, Matros A, Mock HP (2016) Label-free proteome profiling reveals developmental-dependent patterns in young barley grains. Journal of Proteomics 143:106–121CrossRef
6.
Zurück zum Zitat Seiffert U, Schleif FM, Zühlke D. (2011) Recent trends in computational intelligence in life sciences. In: Verleysen M (Ed) Proceedings of the 19. European Symposium on Artificial Neural Networks (ESANN). D-Side Publications (2011) 77–86 Seiffert U, Schleif FM, Zühlke D. (2011) Recent trends in computational intelligence in life sciences. In: Verleysen M (Ed) Proceedings of the 19. European Symposium on Artificial Neural Networks (ESANN). D-Side Publications (2011) 77–86
7.
Zurück zum Zitat Backhaus A., Seiffert U (2014) Classification in high-dimensional spectral data: Accuracy vs. interpretability vs. model size. Neurocomputing 131:15–22CrossRef Backhaus A., Seiffert U (2014) Classification in high-dimensional spectral data: Accuracy vs. interpretability vs. model size. Neurocomputing 131:15–22CrossRef
8.
Zurück zum Zitat Villmann T, Kästner M, Backhaus A, Seiffert U (2013) Processing hyperspectral data in machine learning. In: Verleysen M (Ed) Proceedings of the 21. European Symposium on Artificial Neural Networks (ESANN). D-Side Publications, p 1–10 Villmann T, Kästner M, Backhaus A, Seiffert U (2013) Processing hyperspectral data in machine learning. In: Verleysen M (Ed) Proceedings of the 21. European Symposium on Artificial Neural Networks (ESANN). D-Side Publications, p 1–10
9.
Zurück zum Zitat Backhaus A, Bollenbeck F, Seiffert U (2011) Robust classification of the nutrition state in crop plants by hyperspectral imaging and artificial neural networks. In: Proceedings of the 3rd IEEE Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE Press, p 9. doi:https://doi.org/10.1109/WHISPERS.2011.6080898 Backhaus A, Bollenbeck F, Seiffert U (2011) Robust classification of the nutrition state in crop plants by hyperspectral imaging and artificial neural networks. In: Proceedings of the 3rd IEEE Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE Press, p 9. doi:https://​doi.​org/​10.​1109/​WHISPERS.​2011.​6080898
10.
Zurück zum Zitat Backhaus A, Seiffert U (2013) Quantitative measurements of model interpretability for the analysis of spectral data. In: Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining (CIDM). IEEE Press, p 18–25 Backhaus A, Seiffert U (2013) Quantitative measurements of model interpretability for the analysis of spectral data. In: Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining (CIDM). IEEE Press, p 18–25
11.
Zurück zum Zitat Backhaus A., Seiffert U (2012) Classification in high-dimensional spectral data – Precision vs. interpretability vs. model size. Machine Learning Reports 6:88–96 Backhaus A., Seiffert U (2012) Classification in high-dimensional spectral data – Precision vs. interpretability vs. model size. Machine Learning Reports 6:88–96
12.
Zurück zum Zitat Arens N, Backhaus A, Döll S, Fischer S, Seiffert U, Mock HP (2016): Noninvasive presymptomatic detection of Cercospora Beticola infection and identification of early metabolic responses in sugar beet. Frontiers in Plant Science 7:1377CrossRef Arens N, Backhaus A, Döll S, Fischer S, Seiffert U, Mock HP (2016): Noninvasive presymptomatic detection of Cercospora Beticola infection and identification of early metabolic responses in sugar beet. Frontiers in Plant Science 7:1377CrossRef
13.
Zurück zum Zitat Knauer U, Matros A, Petrovic T, Zanker T, Scott ES, Seiffert U (2017) Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images. Plant Methods 13:47CrossRef Knauer U, Matros A, Petrovic T, Zanker T, Scott ES, Seiffert U (2017) Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images. Plant Methods 13:47CrossRef
14.
Zurück zum Zitat Kicherer A, Herzog K, Bendel N, Klück HC, Backhaus A, Wieland M, Rose JC, Klingbeil L, Läbe T, Hohl C, Petry W, Kuhlmann H, Seiffert U, Töpfer R (2017) Phenoliner: A New Field Phenotyping Platform for Grapevine Research. Sensors 7:1625CrossRef Kicherer A, Herzog K, Bendel N, Klück HC, Backhaus A, Wieland M, Rose JC, Klingbeil L, Läbe T, Hohl C, Petry W, Kuhlmann H, Seiffert U, Töpfer R (2017) Phenoliner: A New Field Phenotyping Platform for Grapevine Research. Sensors 7:1625CrossRef
15.
Zurück zum Zitat Soleimani B, Sammler R, Backhaus A, Beschow H, Schumann E, Mock HP, von Wirén N, Seiffert U, Pillen K (2018) Genetic regulation of growth and nutrient content under phosphorus deficiency in the wild barley introgression library S42IL. Plant Breeding 136:892–907CrossRef Soleimani B, Sammler R, Backhaus A, Beschow H, Schumann E, Mock HP, von Wirén N, Seiffert U, Pillen K (2018) Genetic regulation of growth and nutrient content under phosphorus deficiency in the wild barley introgression library S42IL. Plant Breeding 136:892–907CrossRef
Metadaten
Titel
Phenotyping and Genotyping of Plants
verfasst von
Udo Seiffert, Prof.
Andreas Herzog, Dr.
Copyright-Jahr
2020
Verlag
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-662-59659-3_6

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