<abstract xmlns="http://www.w3.org/1999/xhtml">Hyperspectral remote sensing of plants is widely used in agriculture and forestry. Fast, large-area monitoring is applied, among others, in detecting and diagnosing diseases, stress conditions or predicting the yields. Using available tools to increase the yields of most important crop plants (wheat, rice, corn) without posing threat to food security is essential in the situation of current climate changes.Spectral plant indices are associated with biochemical and biophysical plant characteristics. Using the plant spectral properties (mainly chlorophyll red light absorption and near-infrared range light reflectance in leaf intercellular spaces), it is possible to estimate plant condition, water and carotenoid contents or detect disease. More and more often, based on commonly used hyperspectral vegetation indices, new, more sensitive indices are introduced. Furthermore, to facilitate data processing, artificial intelligence is employed, i.e., neural networks and deep convolutional neural networks.It is important in ecological research to carry out long-term observations and measurements of organisms throughout their lifespan. A non-invasive, quick method ensures that it may be used many times and at each stage of plant development.</abstract>

Hyperspectral remote sensing of plants is widely used in agriculture and forestry. Fast, large-area monitoring is applied, among others, in detecting and diagnosing diseases, stress conditions or predicting the yields. Using available tools to increase the yields of most important crop plants (wheat, rice, corn) without posing threat to food security is essential in the situation of current climate changes.Spectral plant indices are associated with biochemical and biophysical plant characteristics. Using the plant spectral properties (mainly chlorophyll red light absorption and near-infrared range light reflectance in leaf intercellular spaces), it is possible to estimate plant condition, water and carotenoid contents or detect disease. More and more often, based on commonly used hyperspectral vegetation indices, new, more sensitive indices are introduced. Furthermore, to facilitate data processing, artificial intelligence is employed, i.e., neural networks and deep convolutional neural networks.It is important in ecological research to carry out long-term observations and measurements of organisms throughout their lifespan. A non-invasive, quick method ensures that it may be used many times and at each stage of plant development.

Hyperspectral imaging in assessing the condition of plants: strengths and weaknesses

Department of Plant Taxonomy, Faculty of Biology

Center for Research and Conservation of Mountain Plants, Institute of Nature Conservation PAS

Laboratory of Biological Spatial Information

Biodiversity Research and Conservation

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Hyperspectral remote sensing of plants is widely used in agriculture and forestry. Fast, large-area monitoring is applied, among others, in detecting and...

Hyperspectral imaging in assessing the condition of plants: strengths and weaknesses

Online veröffentlicht: 17. Feb. 2020

Seitenbereich: 25 - 30

Eingereicht: 24. Apr. 2019

Akzeptiert: 30. Sept. 2019

DOI: https://doi.org/10.2478/biorc-2019-0011

Schlüsselwörter
hyperspectral remote sensing, hyperspectral vegetation indices, clonal plant, life history

© 2019 Martyna Dominiak-Świgoń et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Hyperspectral imaging in assessing the condition of plants: strengths and weaknesses

Online veröffentlicht: 17. Feb. 2020

Seitenbereich: 25 - 30

Eingereicht: 24. Apr. 2019

Akzeptiert: 30. Sept. 2019

DOI: https://doi.org/10.2478/biorc-2019-0011

Schlüsselwörterhyperspectral remote sensing, hyperspectral vegetation indices, clonal plant, life history

© 2019 Martyna Dominiak-Świgoń et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
hyperspectral remote sensing, hyperspectral vegetation indices, clonal plant, life history