Seed Science and Technology, ( ISI ), Volume (41), No (1), Year (2013-3) , Pages (107-120)

Title : ( Medicinal plant seed identification using machine vision )

Authors: Mohammad Khajeh Hosseini , Sepideh Anvarkhah , Ali Davari- Edlat- Panah , M0hammad Hassan Rashed Mohassel ,

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Abstract

Medicinal plants are used for personal health-care and environmental landscaping. They include annual and perennial species and may be cultivated or occur in the wild. It is important to note that some medicinal plants are rare and limited in their geographical range; if they are not managed appropriately, their existence may be threatened (Joy et al., 1998). Al though medicinal plants play an important role in the drug industry and health care, and attract much attention, few studies have been conducted on their seed identification. Knowledge of seed morphology is important in theoretical botany and could be useful for seed identification for seed testing, seed quarantine, forensic work, seed dispersal an d soil seed bank studies, and for identification of macrofossils in archaeobotanical studies (Jensen, 1998). Seed morphology is a genetic trait, although environmental factors may exert some effect. Baskin and Baskin (1998) listed 79 species in which seed size has been observed to be affected by maternal position and environmental factors that the mother plant has been exposed to during seed maturation.

Keywords

Machine vision; medicinal plants; neural network; seed identification; seed morphology.
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@article{paperid:1033191,
author = {Khajeh Hosseini, Mohammad and Anvarkhah, Sepideh and Ali Davari- Edlat- Panah and Rashed Mohassel, M0hammad Hassan},
title = {Medicinal plant seed identification using machine vision},
journal = {Seed Science and Technology},
year = {2013},
volume = {41},
number = {1},
month = {March},
issn = {0251-0952},
pages = {107--120},
numpages = {13},
keywords = {Machine vision; medicinal plants; neural network; seed identification; seed morphology.},
}

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%0 Journal Article
%T Medicinal plant seed identification using machine vision
%A Khajeh Hosseini, Mohammad
%A Anvarkhah, Sepideh
%A Ali Davari- Edlat- Panah
%A Rashed Mohassel, M0hammad Hassan
%J Seed Science and Technology
%@ 0251-0952
%D 2013

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