The Influence of Colour Features on Seed Identification

Little studies have been done on morphology of medicinal plants seeds. This paper presents an automatic system for medicinal plant seed identification and evaluates the influence of colour features on seed identification. Six colour features (means of red, colours of the seed surface, as well as means of hue, intensity and saturation) were extracted by algorithm and applied as ne Different combinations of colour features (one, two three, four, five and six colour features) were used to f combination for seed identification. Results showed that the six colour feature was the most accurate combination for seed id (99.184% and 87.719% for training and test of neural network respectively). One colour featu seed identification (3.120% and 2.771%). In general, increasing the number of colour features increased the total average of


Introduction
Although medicinal plants play an important role in the drug industry and health care, and thus draw much attention, few studies have been conducted on their seed identification.Knowledge of seed in theoretical botany and could be useful within seed identification for seed testing, seed quarantine, seed dispersal and soil seed bank studies (Jensen, 1995).
Recently, machine vision has become a useful technology for quick seed et al., 2001).Recent advances in hardware and software have enabled machine vision and imaging systems to identify, analyse and display finer details of objects from their digital images (Paliwal In laboratories, the most common method for cultivars' identification is to compare morphological characteristics of seeds with standard samples.Such characters include length, width, thickness, shape, weight, hilum colour and seed coat colour (Cope Studies showed that colour is a useful characteristic to divide different varieties based on seed coloration such as red-, amber-and white for categorizing them into classes (Zhang Lev-Yadun and Ne'eman, Colour is one of the most important features in seeds classification and grading.Different seeds and their varieties are identified by their colours.Thomson and Pomeranz (1991) classified the Western Canadian wheat to six groups Although medicinal plants play an important role in the drug industry and health care, and thus draw much attention, few studies have been conducted on their seed identification.Knowledge of seed morphology is important in theoretical botany and could be useful within seed identification for seed testing, seed quarantine, seed dispersal and soil seed bank studies (Jensen, 1995).
Recently, machine vision has become a useful control and identification (Ureña ., 2001).Recent advances in hardware and software have enabled machine vision and imaging systems to identify, analyse and display finer details of objects from their digital ., 2003).tories, the most common method for cultivars' identification is to compare morphological characteristics of seeds with standard samples.Such characters include length, width, thickness, shape, weight, hilum colour and seed coat Studies showed that colour is a useful characteristic to divide different varieties based on seed coloration such as coloured, but could not be useful for categorizing them into classes (Zhang 2013).Colour is one of the most important features in seeds classification and grading.Different seeds and their varieties are identified by their colours.Thomson and Pomeranz (1991) classified the Western Canadian wheat to six groups Although medicinal plants play an important role in the drug industry and health care, and thus draw much attention, few studies have been conducted on their seed morphology is important in theoretical botany and could be useful within seed identification for seed testing, seed quarantine, seed dispersal and soil seed bank studies (Jensen, 1995).
Recently, machine vision has become a useful control and identification (Ureña ., 2001).Recent advances in hardware and software have enabled machine vision and imaging systems to identify, analyse and display finer details of objects from their digital tories, the most common method for cultivars' identification is to compare morphological characteristics of seeds with standard samples.Such characters include length, width, thickness, shape, weight, hilum colour and seed coat Studies showed that colour is a useful characteristic to divide different varieties based on seed coloration such as coloured, but could not be useful for categorizing them into classes (Zhang et al., 2012; Colour is one of the most important features in seeds classification and grading.Different seeds and their varieties are identified by their colours.Thomson and Pomeranz (1991) classified the Western Canadian wheat to six groups Although medicinal plants play an important role in the drug industry and health care, and thus draw much attention, few studies have been conducted on their seed morphology is important in theoretical botany and could be useful within seed identification for seed testing, seed quarantine, seed Recently, machine vision has become a useful control and identification (Ureña ., 2001).Recent advances in hardware and software have enabled machine vision and imaging systems to identify, analyse and display finer details of objects from their digital tories, the most common method for cultivars' identification is to compare morphological characteristics of seeds with standard samples.Such characters include length, width, thickness, shape, weight, hilum colour and seed coat Studies showed that colour is a useful characteristic to divide different varieties based on seed coloration such as coloured, but could not be useful ., 2012; Colour is one of the most important features in seeds classification and grading.Different seeds and their varieties are identified by their colours.Thomson and Pomeranz (1991) classified the Western Canadian wheat to six groups using a limited s (G) and blue (B) pixel reflectance features).In general, the red, white and amber coloured wheat types were well separated, while some confusion existed between certain red kernel types.Also, Luo separation of healthy seeds of Western Canadian wheat from damaged ones using colour features.
Seed colour images might be used also to describe seed quality and hardness, fungal damages, viral diseases, as well as for separating imma (Ducournau identification of weed seeds for one crop might be a major interest in the agricultural industry.It can also be useful for chemical control of weed growth (Gran Studies showed that there is a correlation between seed colour and seed quality.For example, it has been reported that the seeds of naturally occurring yellow seeded genotypes greater oil, h seeds of black/brown seeded genotypes of these species (Rahman and McVetty, 2011).The yellow seeded genotypes of these species had a thinner and more translucent seed coat, lower hull proportion wit embryo and consequently greater oil and protein percentage (Rahman and McVetty, 2011).Proanthocyanidins and tannins are the major compounds involved in seed coat pigmentation.These are deposited in the seed coat of black/brown seeded digestibility of seed meal for livestock.However, the seeds' studies have been done on morphology of medicinal plants seeds.This paper presents an automatic system for medicinal plant seed identification and evaluates the influence of colour features on seed identification.Six colour features (means of red, colours of the seed surface, as well as means of hue, intensity and saturation) were extracted by algorithm and applied as ne Different combinations of colour features (one, two three, four, five and six colour features) were used to f combination for seed identification.Results showed that the six colour feature was the most accurate combination for seed id (99.184% and 87.719% for training and test of neural network respectively).One colour featu seed identification (3.120% and 2.771%).In general, increasing the number of colour features increased the total average of , seed identification, seed morphology www.notulaebiologicae.ro3205; Electronic 2067-3264 10.15835/nsb.8.1.9743using a limited set of colour features (mean red (R), green (G) and blue (B) pixel reflectance features).In general, the red, white and amber coloured wheat types were well separated, while some confusion existed between certain red kernel types.Also, Luo separation of healthy seeds of Western Canadian wheat from damaged ones using colour features.
Seed colour images might be used also to describe seed quality and hardness, fungal damages, viral diseases, as well as for separating imma (Ducournau et al., 2004; Liu identification of weed seeds for one crop might be a major interest in the agricultural industry.It can also be useful for chemical control of weed growth (Gran Studies showed that there is a correlation between seed colour and seed quality.For example, it has been reported that the seeds of naturally occurring yellow seeded genotypes B. rapa greater oil, higher protein and lower fibre contents than the seeds of black/brown seeded genotypes of these species (Rahman and McVetty, 2011).The yellow seeded genotypes of these species had a thinner and more translucent seed coat, lower hull proportion wit embryo and consequently greater oil and protein percentage (Rahman and McVetty, 2011).Proanthocyanidins and tannins are the major compounds involved in seed coat pigmentation.These are deposited in the seed coat of black/brown seeded digestibility of seed meal for livestock.However, the seeds' studies have been done on morphology of medicinal plants seeds.This paper presents an automatic system for medicinal plant seed identification and evaluates the influence of colour features on seed identification.Six colour features (means of red, colours of the seed surface, as well as means of hue, intensity and saturation) were extracted by algorithm and applied as ne Different combinations of colour features (one, two three, four, five and six colour features) were used to f combination for seed identification.Results showed that the six colour feature was the most accurate combination for seed id (99.184% and 87.719% for training and test of neural network respectively).One colour feature had the lowest average accuracy values for seed identification (3.120% and 2.771%).In general, increasing the number of colour features increased the total average of , seed identification, seed morphology www.notulaebiologicae.ronsb.8.1.9743et of colour features (mean red (R), green (G) and blue (B) pixel reflectance features).In general, the red, white and amber coloured wheat types were well separated, while some confusion existed between certain red kernel types.Also, Luo et al. (1999) separation of healthy seeds of Western Canadian wheat from damaged ones using colour features.
Seed colour images might be used also to describe seed quality and hardness, fungal damages, viral diseases, as well as for separating immature seeds apart of mature ones ., 2004; Liu et al identification of weed seeds for one crop might be a major interest in the agricultural industry.It can also be useful for chemical control of weed growth (Gran Studies showed that there is a correlation between seed colour and seed quality.For example, it has been reported that the seeds of naturally occurring yellow seeded B. rapa, B. juncea igher protein and lower fibre contents than the seeds of black/brown seeded genotypes of these species (Rahman and McVetty, 2011).The yellow seeded genotypes of these species had a thinner and more translucent seed coat, lower hull proportion wit embryo and consequently greater oil and protein percentage (Rahman and McVetty, 2011).Proanthocyanidins and tannins are the major compounds involved in seed coat pigmentation.These are deposited in the seed coat of black/brown seeded Brassica genotypes and reduce the digestibility of seed meal for livestock.However, the seeds'

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The Influence of Colour Features on Seed Identification studies have been done on morphology of medicinal plants seeds.This paper presents an automatic system for medicinal plant seed identification and evaluates the influence of colour features on seed identification.Six colour features (means of red, colours of the seed surface, as well as means of hue, intensity and saturation) were extracted by algorithm and applied as ne Different combinations of colour features (one, two three, four, five and six colour features) were used to find out the most accurate combination for seed identification.Results showed that the six colour feature was the most accurate combination for seed id re had the lowest average accuracy values for seed identification (3.120% and 2.771%).In general, increasing the number of colour features increased the total average of accuracy values.
, seed identification, seed morphology et of colour features (mean red (R), green (G) and blue (B) pixel reflectance features).In general, the red, white and amber coloured wheat types were well separated, while some confusion existed between certain red .(1999) set an experiment for separation of healthy seeds of Western Canadian wheat from damaged ones using colour features.
Seed colour images might be used also to describe seed quality and hardness, fungal damages, viral diseases, as well ture seeds apart of mature ones et al., 2005).In addition, early identification of weed seeds for one crop might be a major interest in the agricultural industry.It can also be useful for chemical control of weed growth (Granitto et al Studies showed that there is a correlation between seed colour and seed quality.For example, it has been reported that the seeds of naturally occurring yellow seeded and B. carinata igher protein and lower fibre contents than the seeds of black/brown seeded genotypes of these species (Rahman and McVetty, 2011).The yellow seeded genotypes of these species had a thinner and more translucent seed coat, lower hull proportion wit embryo and consequently greater oil and protein percentage (Rahman and McVetty, 2011).Proanthocyanidins and tannins are the major compounds involved in seed coat pigmentation.These are deposited in the seed coat of genotypes and reduce the digestibility of seed meal for livestock.However, the seeds' studies have been done on morphology of medicinal plants seeds.This paper presents an automatic system for medicinal plant green and blue colours of the seed surface, as well as means of hue, intensity and saturation) were extracted by algorithm and applied as network input.
ind out the most accurate combination for seed identification.Results showed that the six colour feature was the most accurate combination for seed identification re had the lowest average accuracy values for accuracy values.
et of colour features (mean red (R), green (G) and blue (B) pixel reflectance features).In general, the red, white and amber coloured wheat types were well separated, while some confusion existed between certain red set an experiment for separation of healthy seeds of Western Canadian wheat Seed colour images might be used also to describe seed quality and hardness, fungal damages, viral diseases, as well ture seeds apart of mature ones ., 2005).In addition, early identification of weed seeds for one crop might be a major interest in the agricultural industry.It can also be useful for et al., 2002).Studies showed that there is a correlation between seed colour and seed quality.For example, it has been reported that the seeds of naturally occurring yellow seeded B. carinata contained igher protein and lower fibre contents than the seeds of black/brown seeded genotypes of these species (Rahman and McVetty, 2011).The yellow seeded Brassica genotypes of these species had a thinner and more translucent seed coat, lower hull proportion with a bigger embryo and consequently greater oil and protein percentage (Rahman and McVetty, 2011).Proanthocyanidins and tannins are the major compounds involved in seed coat pigmentation.These are deposited in the seed coat of genotypes and reduce the digestibility of seed meal for livestock.However, the seeds' studies have been done on morphology of medicinal plants seeds.This paper presents an automatic system for medicinal plant and blue twork input.ind out the most accurate entification re had the lowest average accuracy values for et of colour features (mean red (R), green (G) and blue (B) pixel reflectance features).In general, the red, white and amber coloured wheat types were well separated, while some confusion existed between certain red set an experiment for separation of healthy seeds of Western Canadian wheat Seed colour images might be used also to describe seed quality and hardness, fungal damages, viral diseases, as well ture seeds apart of mature ones ., 2005).In addition, early identification of weed seeds for one crop might be a major interest in the agricultural industry.It can also be useful for Studies showed that there is a correlation between seed colour and seed quality.For example, it has been reported that the seeds of naturally occurring yellow seeded contained igher protein and lower fibre contents than the seeds of black/brown seeded genotypes of these species Brassica genotypes of these species had a thinner and more h a bigger embryo and consequently greater oil and protein percentage (Rahman and McVetty, 2011).Proanthocyanidins and tannins are the major compounds involved in seed coat pigmentation.These are deposited in the seed coat of genotypes and reduce the digestibility of seed meal for livestock.However, the seeds' coat of black/ brown seeded Brassica genotypes contained more fiber and less protein than those of yellow seeded genotypes.Therefore, B. napus lines have been developed from interspecific crosses with related species, namely B. rapa, B. oleracea spp.alboglabra, B. juncea and B. carinata (Rahman and McVetty, 2011).
One of the most important attributes for introducing sesame grains in the market was seed colour (Pandey et al., 2013;Zhang et al., 2013).Although most are light coloured, there is a wide variability in sesame seed coat colour, which varies from white to black.Due to the importance of this trait for the export market, seed colour is a central target in sesame breeding programs; however, there are few studies on the inheritance of this essential seed attribute, and determination of genetic factors affecting any trait is necessary to establish useful breeding programs (Laurentin and Benítez, 2014).
Seeds acquire primary dormancy during their development to enhance adaptation, as the capacity of wild species, to diverse environments, by distributing germination over time and space.Domestication tends to reduce dormancy by selection for rapid and uniform germination.Differentiation in seed dormancy between cereal crops and wild relatives has been associated with some factors such as seed morphologies (Guo et al., 2000).For example, the most persistent type of weedy rice is red rice, which is characterized by a red pericarp colour.Red rice has strong seed dormancy (Lim and Ha, 2013).x In some cases, there is a correlated relationship between seed coat colour and seed quality.Some studies showed that seed lots of red clover visually inspected in terms of seed colour were separated based on a larger colour spectrum thereafter, by digital colour measurement equipment, as seed colour yellow, purple, brown and mixed.Results revealed that seed coat colour of red clover could be preferred as an indicator of seed quality and seedling growth ability.Yellow coloured seeds lots of red clover had higher vigour and seed quality than other colours.Mean germination time (MGT) and electrical conductivity (EC 4 h) test showed significant differences among the seed coat colour.Meanwhile, tests also showed a highly significant correlation in emergence and seedling percentage in salt stress conditions (Atis et al., 2011).
Some seeds are valued according to their appearance, and thus colour is the most important factor for grading (Copeland and McDonald, 2012).The purpose of the current research was to determine the influence of colour features on seed automatic identification.

Grain samples
Seeds from 75 species of medicinal plants (Table 1) were used for this study.
Seeds were photographed using a Dinolite Digital Microscope model 4050 with 640 × 480 to 1024 × 768 pixel resolution at 30-to 80-times magnification, depending on their original size.A database containing 1,800 images of the 75 species was constructed.

Algorithm development
For algorithm development MATLAB 7.9 (Version 2009b) software and windows Vista (Service Pack 1) were used.Employed hardware was an IBM compatible laptop (model Vostro 1500 from DELL Company).In the algorithm, the seed image was segmented from the background image, and its features were extracted and used for the neural network training (Anvarkhah et al., 2012).
Six colour features were extracted by algorithm and applied as network input: -Mean of red colour of seed surface (R) -Mean of green colour of seed surface (G) -Mean of blue colour of seed surface (B) -Hue means (H) -Intensity (I) -Saturation means (Sa) Different combinations of colour features were used to find out the most accurate combination for seed identification.

One colour feature
Table 2 shows the total average values of training and test parts of neural network, when using each colour feature individually.The use of hue had the highest accuracy values of training and test with values of 9.239% and 8.771% respectively.However, employing one colour feature led to a low rate of accuracy values.For example, no accurate identification was shown when using red, blue and saturation features separately (0%).The rest of the colour features tested hereby had low accuracy percents, below 8%.

Two Colour Features
It was noted that two colour features led to a more accurate identification.However, combinations of red, green, blue and saturation with hue caused 0% of accuracy values, while by using other features paired two by two the results had higher values.The most accurate identification was shown within the combination of hue and intensity, which led to 24.184% and 19.298% for training and test parts of neural network respectively (Table 3).

Three colour features
Table 4 shows the training and test accuracy values obtained by using three colour features within the different colour combinations.Except of two combinations ([red + hue + intensity], [blue + hue + saturation], both with 0%), all others had accuracy values of 30-99% for training and 20-85% for test parts of neural network.

Four colour features
Except two combinations of [hue + saturation + red + green] and [hue + saturation + red + blue], all other combinations of four colour features caused above 60% and 50% for training and test parts of neural network respectively (Table 5).It seems that using triple effect of hue, saturation and red with features of green and may cause training errors.

Five colour features
Table 6 shows identification accuracy using five colour features.These combinations led to training and test accuracy values higher than 90% and 75% respectively, for all features' combinations.

Six colour features
The most accurate identification was shown using combination of six colour features (99.18% and 87.71% for training and test of neural network respectively) (Table 7).In general, increasing the number of colour features increased the total (Anvarkhah et al  hue, saturation and red with features of green and may cause training errors.

Five colour features
Table 6 shows identification accuracy using five colour features.These combinations led to training and test accuracy values higher than 90% and 75% respectively, for all features' combinations.

colour features
The most accurate identification was shown using combination of six colour features (99.18% and 87.71% for training and test of neural network respectively) (Table 7).In general, increasing the number of colour features increased the total et al., 2013).hue, saturation and red with features of green and Table 6 shows identification accuracy using five colour features.These combinations led to training and test accuracy values higher than 90% and 75% respectively, for all features' combinations.

Comparison among colour features combination
The most accurate identification was shown using combination of six colour features (99.18% and 87.71% for training and test of neural network respectively) (Table 7).In general, increasing the number of colour features increased the total average of accuracy hue, saturation and red with features of green and Table 6 shows identification accuracy using five colour features.These combinations led to training and test accuracy values higher than 90% and 75% respectively, for all features' combinations.

Comparison among colour features combination
The most accurate identification was shown using combination of six colour features (99.18% and 87.71% for training and test of neural network respectively) (Table 7).In general, increasing the number of colour average of accuracy   1hue, saturation and red with features of green and blue Table 6 shows identification accuracy using five colour features.These combinations led to training and test accuracy values higher than 90% and 75%

Comparison among colour features combination
The most accurate identification was shown using combination of six colour features (99.18% and 87.71% for training and test of neural network respectively) (Table 7).In general, increasing the number of colour average of accuracy   In general, increasing the number of colour features increased the total average of accuracy (Anvarkhah et al., 2013).
Colour is one of the most important features in seeds classification and grading.Different seeds and their varieties are identified by their colours.Thomson and Pomeranz (1991) classified the Western Canadian wheat to six groups using a limited set of colour features (mean Red (R), Green (G) and Blue (B) pixel reflectance features).In general, the red, white and amber coloured wheat types were well separated, while some confusion existed between certain red kernel types.Also, Luo et al. (1999) set an experiment for separation of healthy seeds of Western Canadian wheat from damaged ones using colour features.

Conclusions
Different combinations of colour features (one, two three, four, five and six colour features) were used to find out the most accurate combination for seed identification by machine vision and algorithm determinations.Results showed that the six colour feature was the most accurate combination for seed identification for training and test of neural network respectively, while employing one colour feature led to a low rate of accuracy values, with a lack of accurate identification when using red, blue and saturation features separately.
Department of Crop Science, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran (*corresponding author) Fig. 1 shows the average accuracy values of different Fig. 1 shows the average accuracy values of different Fig. 1 shows the average accuracy values of different Scientific name and family of the 75 species of medicinal plan Fig. 1 shows the average accuracy values of different

Fig. 1
Fig. 1 shows the average accuracy values of different colour combinations.Increasing the number of colour features led to higher accuracy values.Combination of colour features was the most accurate combination with

Table 1 .
Scientific name and family of the 75 species of medicinal plan Scientific name and family of the 75 species of medicinal plan Scientific name and family of the 75 species of medicinal plan

Table 2 .
Accuracy values using one colour feature

Table 3 .
Accuracy values using two colour features

Table 4 .
Accuracy values using three colour features

Table 5 .
Accuracy values using four colour features

Table 7 .
Accuracy values using six colour features

Table 6 .
Accuracy values using five colour features