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Hyperspectral Technologies for Assessing Seed Germination and Trifloxysulfuron-methyl Response in Amaranthus palmeri (Palmer Amaranth).

Authors :
Matzrafi, Maor
Matzrafi, Maor
Herrmann, Ittai
Nansen, Christian
Kliper, Tom
Zait, Yotam
Ignat, Timea
Siso, Dana
Rubin, Baruch
Karnieli, Arnon
Eizenberg, Hanan
Matzrafi, Maor
Matzrafi, Maor
Herrmann, Ittai
Nansen, Christian
Kliper, Tom
Zait, Yotam
Ignat, Timea
Siso, Dana
Rubin, Baruch
Karnieli, Arnon
Eizenberg, Hanan
Publication Year :
2017

Abstract

Weed infestations in agricultural systems constitute a serious challenge to agricultural sustainability and food security worldwide. Amaranthus palmeri S. Watson (Palmer amaranth) is one of the most noxious weeds causing significant yield reductions in various crops. The ability to estimate seed viability and herbicide susceptibility is a key factor in the development of a long-term management strategy, particularly since the misuse of herbicides is driving the evolution of herbicide response in various weed species. The limitations of most herbicide response studies are that they are conducted retrospectively and that they use in vitro destructive methods. Development of a non-destructive method for the prediction of herbicide response could vastly improve the efficacy of herbicide applications and potentially delay the evolution of herbicide resistance. Here, we propose a toolbox based on hyperspectral technologies and data analyses aimed to predict A. palmeri seed germination and response to the herbicide trifloxysulfuron-methyl. Complementary measurement of leaf physiological parameters, namely, photosynthetic rate, stomatal conductence and photosystem II efficiency, was performed to support the spectral analysis. Plant response to the herbicide was compared to image analysis estimates using mean gray value and area fraction variables. Hyperspectral reflectance profiles were used to determine seed germination and to classify herbicide response through examination of plant leaves. Using hyperspectral data, we have successfully distinguished between germinating and non-germinating seeds, hyperspectral classification of seeds showed accuracy of 81.9 and 76.4%, respectively. Sensitive and resistant plants were identified with high degrees of accuracy (88.5 and 90.9%, respectively) from leaf hyperspectral reflectance profiles acquired prior to herbicide application. A correlation between leaf physiological parameters and herbicide response (sensitivity/resistance) wa

Details

Database :
OAIster
Notes :
application/pdf
Publication Type :
Electronic Resource
Accession number :
edsoai.on1287355397
Document Type :
Electronic Resource