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פותח על ידי קלירמאש פתרונות בע"מ -
Hyperspectral imagery for the detection of nitrogen stress in potato for in-season management
Year:
2012
Authors :
אלחנתי, ויקטור
;
.
כהן, יפית
;
.
רוד, רונית
;
.
Volume :
Co-Authors:

Nigon, Tyler; Rosen, Carl; Mulla, David

Facilitators :
From page:
1
To page:
15
(
Total pages:
15
)
Abstract:

Potato (Solanum tuberosum, L.) yield and quality are highly dependent on the availability of nitrogen (N) during the crop’s critical growth stages. Canopylevel hyperspectral (HS) imagery has been shown to be an effective research tool for determining the best wavelengths and/or indices for the detection of N stress in a number of crops. Research findings from HS imagery can be applied to active sensors as a way to increase the effectiveness of real-time, variable rate N applications, but limited data exist for potato production. A field study was conducted in 2010 and 2011 at the Sand Plain Research Farm in Becker, MN on a Hubbard loamy sand soil to evaluate the effects of water stress, N fertilizer rate/timing, the varieties Russet Burbank (RB) and Alpine Russet (AR), and growth stage on the ability of canopy-level HS imagery (401-982 nm) to detect N stress in a potato crop. The ability of canopy-level narrowband reflectance to detect differences in potato N status was evaluated by performing linear regression between HS narrowband reflectance and leaf N concentration as a means to distinguish the optimum wavelengths to detect N stress. As the potato crop matured, the coefficient of determination (r 2 ) decreased at most HS wavelengths, especially in the NIR region. The far green and near red regions from ~582-610 nm, and those at the beginning of the red-edge from ~685-695 nm were the best performing wavelengths for detecting N stress overall. Canopy reflectance was able to predict leaf N among image dates more consistently for RB than for AR.

Note:
Related Files :
Hyperspectral imagery
in-season fertilizer applications
Nitrogen stress
potato
עוד תגיות
תוכן קשור
More details
DOI :
Article number:
0
Affiliations:
Database:
גוגל סקולר
Publication Type:
מאמר מתוך כינוס
;
.
Language:
אנגלית
Editors' remarks:
ID:
36615
Last updated date:
02/03/2022 17:27
Creation date:
14/08/2018 11:06
Scientific Publication
Hyperspectral imagery for the detection of nitrogen stress in potato for in-season management

Nigon, Tyler; Rosen, Carl; Mulla, David

Hyperspectral imagery for the detection of nitrogen stress in potato for in-season management

Potato (Solanum tuberosum, L.) yield and quality are highly dependent on the availability of nitrogen (N) during the crop’s critical growth stages. Canopylevel hyperspectral (HS) imagery has been shown to be an effective research tool for determining the best wavelengths and/or indices for the detection of N stress in a number of crops. Research findings from HS imagery can be applied to active sensors as a way to increase the effectiveness of real-time, variable rate N applications, but limited data exist for potato production. A field study was conducted in 2010 and 2011 at the Sand Plain Research Farm in Becker, MN on a Hubbard loamy sand soil to evaluate the effects of water stress, N fertilizer rate/timing, the varieties Russet Burbank (RB) and Alpine Russet (AR), and growth stage on the ability of canopy-level HS imagery (401-982 nm) to detect N stress in a potato crop. The ability of canopy-level narrowband reflectance to detect differences in potato N status was evaluated by performing linear regression between HS narrowband reflectance and leaf N concentration as a means to distinguish the optimum wavelengths to detect N stress. As the potato crop matured, the coefficient of determination (r 2 ) decreased at most HS wavelengths, especially in the NIR region. The far green and near red regions from ~582-610 nm, and those at the beginning of the red-edge from ~685-695 nm were the best performing wavelengths for detecting N stress overall. Canopy reflectance was able to predict leaf N among image dates more consistently for RB than for AR.

Scientific Publication
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