Details
| Original language | English |
|---|---|
| Title of host publication | Photonic Instrumentation Engineering X |
| Editors | Lynda E. Busse, Yakov Soskind |
| Publisher | SPIE |
| ISBN (electronic) | 9781510659612 |
| Publication status | Published - 8 Mar 2023 |
| Event | SPIE Photonics West 2023 - Moscone Center, San Francisco, United States Duration: 28 Jan 2023 → 2 Feb 2023 https://spie.org/conferences-and-exhibitions/photonics-west |
Publication series
| Name | Proceedings of SPIE - The International Society for Optical Engineering |
|---|---|
| Volume | 12428 |
| ISSN (Print) | 0277-786X |
| ISSN (electronic) | 1996-756X |
Abstract
Hyperspectral imaging is a key technology for monitoring agricultural crops and vegetation. It can be used for health estimation and the early detection of disease symptoms in plants. This can help to reduce the use of pesticides by allowing targeted and early intervention. Cost-efficient hyperspectral imaging systems are necessary to meet the increasing demand for monitoring techniques for agricultural products. These systems usually suffer from sub-optimal image quality. Here we present a digital aberration correction for hyperspectral image data.
Keywords
- hyperspectral imaging, spatial resolution, deconvolution, plant disease detection, agriculture
ASJC Scopus subject areas
- Materials Science(all)
- Electronic, Optical and Magnetic Materials
- Physics and Astronomy(all)
- Condensed Matter Physics
- Mathematics(all)
- Applied Mathematics
- Engineering(all)
- Electrical and Electronic Engineering
- Computer Science(all)
- Computer Science Applications
Cite this
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- BibTeX
- RIS
Photonic Instrumentation Engineering X. ed. / Lynda E. Busse; Yakov Soskind. SPIE, 2023. 124280R (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 12428).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Characterization and digital aberration correction of a hyperspectral imaging system for plant disease detection
AU - Zabic, Miroslav
AU - Jose, Lijin
AU - Landes, Timm
AU - Fritz, Jan-Michael
AU - Weisheit, Inga
AU - Heinemann, Dag
PY - 2023/3/8
Y1 - 2023/3/8
N2 - Hyperspectral imaging is a key technology for monitoring agricultural crops and vegetation. It can be used for health estimation and the early detection of disease symptoms in plants. This can help to reduce the use of pesticides by allowing targeted and early intervention. Cost-efficient hyperspectral imaging systems are necessary to meet the increasing demand for monitoring techniques for agricultural products. These systems usually suffer from sub-optimal image quality. Here we present a digital aberration correction for hyperspectral image data.
AB - Hyperspectral imaging is a key technology for monitoring agricultural crops and vegetation. It can be used for health estimation and the early detection of disease symptoms in plants. This can help to reduce the use of pesticides by allowing targeted and early intervention. Cost-efficient hyperspectral imaging systems are necessary to meet the increasing demand for monitoring techniques for agricultural products. These systems usually suffer from sub-optimal image quality. Here we present a digital aberration correction for hyperspectral image data.
KW - hyperspectral imaging
KW - spatial resolution
KW - deconvolution
KW - plant disease detection
KW - agriculture
UR - http://www.scopus.com/inward/record.url?scp=85159777074&partnerID=8YFLogxK
U2 - 10.1117/12.2647833
DO - 10.1117/12.2647833
M3 - Conference contribution
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Photonic Instrumentation Engineering X
A2 - Busse, Lynda E.
A2 - Soskind, Yakov
PB - SPIE
T2 - SPIE Photonics West 2023
Y2 - 28 January 2023 through 2 February 2023
ER -