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Using genetically modified tomato crop plants with purple leaves for absolute weed/crop classification
Year:
2014
Source of publication :
Pest Management Science
Authors :
Aly, Radi
;
.
Eizenberg, Hanan
;
.
Landa, Tal
;
.
Lati, Ran
;
.
Levin, Ilan
;
.
Volume :
70
Co-Authors:
Lati, R.N., Department of Weed Research and Plant Pathology, Agricultural Research Organization, Newe Ya'ar Research Center, Israel, Mapping and Geo-Information Engineering, Technion-Israel Institute of Technology, Haifa, Israel
Filin, S., Mapping and Geo-Information Engineering, Technion-Israel Institute of Technology, Haifa, Israel
Aly, R., Department of Weed Research and Plant Pathology, Agricultural Research Organization, Newe Ya'ar Research Center, Israel
Lande, T., Department of Weed Research and Plant Pathology, Agricultural Research Organization, Newe Ya'ar Research Center, Israel
Levin, I., Department of Vegetable Research, Institute of Plant Sciences, Agricultural Research Organization, The Volcani Center, Bet Dagan, Israel
Eizenberg, H., Department of Weed Research and Plant Pathology, Agricultural Research Organization, Newe Ya'ar Research Center, Israel
Facilitators :
From page:
1059
To page:
1065
(
Total pages:
7
)
Abstract:
BACKGROUND: Weed/crop classification is considered the main problem in developing precise weed-management methodologies, because both crops and weeds share similar hues. Great effort has been invested in the development of classification models, most based on expensive sensors and complicated algorithms. However, satisfactory results are not consistently obtained due to imaging conditions in the field. RESULTS: We report on an innovative approach that combines advances in genetic engineering and robust image-processing methods to detect weeds and distinguish them from crop plants by manipulating the crop's leaf color. We demonstrate this on genetically modified tomato (germplasm AN-113) which expresses a purple leaf color. An autonomous weed/crop classification is performed using an invariant-hue transformation that is applied to images acquired by a standard consumer camera (visible wavelength) and handles variations in illumination intensities. CONCLUSION: The integration of these methodologies is simple and effective, and classification results were accurate and stable under a wide range of imaging conditions. Using this approach, we simplify the most complicated stage in image-based weed/crop classification models. © 2013 Society of Chemical Industry.
Note:
Related Files :
color
Genetics
Image-processing
metabolism
pigmentation
Site-specific weed management
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More details
DOI :
10.1002/ps.3647
Article number:
0
Affiliations:
Database:
Scopus
Publication Type:
article
;
.
Language:
English
Editors' remarks:
ID:
19931
Last updated date:
02/03/2022 17:27
Creation date:
16/04/2018 23:32
Scientific Publication
Using genetically modified tomato crop plants with purple leaves for absolute weed/crop classification
70
Lati, R.N., Department of Weed Research and Plant Pathology, Agricultural Research Organization, Newe Ya'ar Research Center, Israel, Mapping and Geo-Information Engineering, Technion-Israel Institute of Technology, Haifa, Israel
Filin, S., Mapping and Geo-Information Engineering, Technion-Israel Institute of Technology, Haifa, Israel
Aly, R., Department of Weed Research and Plant Pathology, Agricultural Research Organization, Newe Ya'ar Research Center, Israel
Lande, T., Department of Weed Research and Plant Pathology, Agricultural Research Organization, Newe Ya'ar Research Center, Israel
Levin, I., Department of Vegetable Research, Institute of Plant Sciences, Agricultural Research Organization, The Volcani Center, Bet Dagan, Israel
Eizenberg, H., Department of Weed Research and Plant Pathology, Agricultural Research Organization, Newe Ya'ar Research Center, Israel
Using genetically modified tomato crop plants with purple leaves for absolute weed/crop classification
BACKGROUND: Weed/crop classification is considered the main problem in developing precise weed-management methodologies, because both crops and weeds share similar hues. Great effort has been invested in the development of classification models, most based on expensive sensors and complicated algorithms. However, satisfactory results are not consistently obtained due to imaging conditions in the field. RESULTS: We report on an innovative approach that combines advances in genetic engineering and robust image-processing methods to detect weeds and distinguish them from crop plants by manipulating the crop's leaf color. We demonstrate this on genetically modified tomato (germplasm AN-113) which expresses a purple leaf color. An autonomous weed/crop classification is performed using an invariant-hue transformation that is applied to images acquired by a standard consumer camera (visible wavelength) and handles variations in illumination intensities. CONCLUSION: The integration of these methodologies is simple and effective, and classification results were accurate and stable under a wide range of imaging conditions. Using this approach, we simplify the most complicated stage in image-based weed/crop classification models. © 2013 Society of Chemical Industry.
Scientific Publication
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