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Weed detection in multi-spectral images of cotton fields
Year:
2005
Authors :
Alchanatis, Victor
;
.
Hetzroni, Amots
;
.
Volume :
47
Co-Authors:
Alchanatis, V., Inst. of Agricultural Engineering, Agricultural Research Organization, Volcani Center, Bet Dagan 50250, Israel
Ridel, L., Dept. of Interdisciplinary Studies, Iby Aladar Fleischman Fac. of Eng., Tel Aviv University, Ramat Aviv 69978, Israel
Hetzroni, A., Inst. of Agricultural Engineering, Agricultural Research Organization, Volcani Center, Bet Dagan 50250, Israel
Yaroslavsky, L., Dept. of Interdisciplinary Studies, Iby Aladar Fleischman Fac. of Eng., Tel Aviv University, Ramat Aviv 69978, Israel
Facilitators :
From page:
243
To page:
260
(
Total pages:
18
)
Abstract:
A means for automatic detection and evaluation of weeds in the field was developed and tested; it was based on an acousto-optic tunable hyperspectral sensor and a detection algorithm. The algorithm that was developed used spectral reflectance properties and robust statistics features for weed detection. Soil-crop segmentation was done with two spectral channels, chosen from 100 channels available from the hyperspectral sensor. Weed detection was based on texture features, extracted from the segmented images. The algorithm was applied to a database of images of cotton plants and weeds, in their early stages of development. The results showed a good detection ability. The occurrence of weeds was detected in all images; the weed-infested area was estimated with 14% error, and the false detection rate was 15%. © 2005 Elsevier B.V. All rights reserved.
Note:
Related Files :
Agriculture
Crops
image analysis
multispectral image
soil
Soils
Weed
weeds
Show More
Related Content
More details
DOI :
10.1016/j.compag.2004.11.019
Article number:
Affiliations:
Database:
Scopus
Publication Type:
Conference paper
;
.
Language:
English
Editors' remarks:
ID:
31130
Last updated date:
02/03/2022 17:27
Creation date:
17/04/2018 00:59
Scientific Publication
Weed detection in multi-spectral images of cotton fields
47
Alchanatis, V., Inst. of Agricultural Engineering, Agricultural Research Organization, Volcani Center, Bet Dagan 50250, Israel
Ridel, L., Dept. of Interdisciplinary Studies, Iby Aladar Fleischman Fac. of Eng., Tel Aviv University, Ramat Aviv 69978, Israel
Hetzroni, A., Inst. of Agricultural Engineering, Agricultural Research Organization, Volcani Center, Bet Dagan 50250, Israel
Yaroslavsky, L., Dept. of Interdisciplinary Studies, Iby Aladar Fleischman Fac. of Eng., Tel Aviv University, Ramat Aviv 69978, Israel
Weed detection in multi-spectral images of cotton fields
A means for automatic detection and evaluation of weeds in the field was developed and tested; it was based on an acousto-optic tunable hyperspectral sensor and a detection algorithm. The algorithm that was developed used spectral reflectance properties and robust statistics features for weed detection. Soil-crop segmentation was done with two spectral channels, chosen from 100 channels available from the hyperspectral sensor. Weed detection was based on texture features, extracted from the segmented images. The algorithm was applied to a database of images of cotton plants and weeds, in their early stages of development. The results showed a good detection ability. The occurrence of weeds was detected in all images; the weed-infested area was estimated with 14% error, and the false detection rate was 15%. © 2005 Elsevier B.V. All rights reserved.
Scientific Publication
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