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פותח על ידי קלירמאש פתרונות בע"מ -
Spatial-spectral processing strategies for detection of salinity effects in cauliflower, aubergine and kohlrabi
Year:
2013
Source of publication :
Biosystems Engineering
Authors :
אלחנתי, ויקטור
;
.
רוד, רונית
;
.
Volume :
114
Co-Authors:
Rud, R., Faculty of Civil and Environmental Engineering, Technion, Israel Institute of Technology, Haifa 32000, Israel, Agricultural Research Organization, Institute of Agricultural Engineering, Bet-Dagan 50250, Israel
Shoshany, M., Faculty of Civil and Environmental Engineering, Technion, Israel Institute of Technology, Haifa 32000, Israel
Alchanatis, V., Agricultural Research Organization, Institute of Agricultural Engineering, Bet-Dagan 50250, Israel
Facilitators :
From page:
384
To page:
396
(
Total pages:
13
)
Abstract:
Hyperspectral images and spectroradiometer measurements were taken from cauliflower (Brassica oleracea, Botrytis group), aubergine (Solanum melongena) and kohlrabi (Brassica oleracea, Gongylodes group) plants in a controlled experiment. Plants were grown in media with sodiumchloride (NaCl) concentrations between 30 and 150 mmol. Spectral and spatial processing techniques were developed to assess the ability to distinguish between plants exposed to various levels of salinity stress. Local autocorrelation analysis was used to detect the spatial patterns that characterise the effects of salinity on crop canopy. This analysis was applied on a vegetation index in the spectral range of 435-554 nm, the green indigo ratio (GIR) index. The processing strategies that were developed were able to distinguish three levels of salinity effects. The strategy based on a combined spatialespectral index yielded the most consistent results with average total accuracy of 62%, whereas accuracies obtained with known spectral vegetation indices were 29%. The presented method may be implemented in other cases of vegetation stresses where symptoms are characterised by patchiness and can be imaged, not necessarily in the visible spectral range (400-750 nm). © 2012 IAgrE.
Note:
Related Files :
Agronomy
Brassica oleracea var. gongylodes
food technology
Solanum melongena
spectroscopy
עוד תגיות
תוכן קשור
More details
DOI :
10.1016/j.biosystemseng.2012.11.012
Article number:
Affiliations:
Database:
סקופוס
Publication Type:
מאמר
;
.
Language:
אנגלית
Editors' remarks:
ID:
30589
Last updated date:
02/03/2022 17:27
Creation date:
17/04/2018 00:55
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Scientific Publication
Spatial-spectral processing strategies for detection of salinity effects in cauliflower, aubergine and kohlrabi
114
Rud, R., Faculty of Civil and Environmental Engineering, Technion, Israel Institute of Technology, Haifa 32000, Israel, Agricultural Research Organization, Institute of Agricultural Engineering, Bet-Dagan 50250, Israel
Shoshany, M., Faculty of Civil and Environmental Engineering, Technion, Israel Institute of Technology, Haifa 32000, Israel
Alchanatis, V., Agricultural Research Organization, Institute of Agricultural Engineering, Bet-Dagan 50250, Israel
Spatial-spectral processing strategies for detection of salinity effects in cauliflower, aubergine and kohlrabi
Hyperspectral images and spectroradiometer measurements were taken from cauliflower (Brassica oleracea, Botrytis group), aubergine (Solanum melongena) and kohlrabi (Brassica oleracea, Gongylodes group) plants in a controlled experiment. Plants were grown in media with sodiumchloride (NaCl) concentrations between 30 and 150 mmol. Spectral and spatial processing techniques were developed to assess the ability to distinguish between plants exposed to various levels of salinity stress. Local autocorrelation analysis was used to detect the spatial patterns that characterise the effects of salinity on crop canopy. This analysis was applied on a vegetation index in the spectral range of 435-554 nm, the green indigo ratio (GIR) index. The processing strategies that were developed were able to distinguish three levels of salinity effects. The strategy based on a combined spatialespectral index yielded the most consistent results with average total accuracy of 62%, whereas accuracies obtained with known spectral vegetation indices were 29%. The presented method may be implemented in other cases of vegetation stresses where symptoms are characterised by patchiness and can be imaged, not necessarily in the visible spectral range (400-750 nm). © 2012 IAgrE.
Scientific Publication
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