Seed identification of medicinal plants using machine vision

S. Anvarkhah M. Khajeh-Hosseini A. Davari M.H. Rashed Department of Crop Science, Faculty of Agriculture, Ferdowsi University of

Mashhad, Iran

Why seed identification?

q Taxonomic purposes q Ecology seed size

q Soil seed bank

q Quarantine

q Seed quality control purity 2

Background Historical background of seed identification:

Observation Drawing Machine

vision

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Machine vision Machine vision is a promising technology

for rapid seed identification. The recent advances in hardware and

software have enabled the machine vision and imaging systems to detect, process, analyze and display a wide range of finer details of seeds.

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Material and methods Seeds of 75 species of medicinal plants collected

from natural landscapes or cultivated species in the Medicinal Plant Garden

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No. Family No. Of Species Example Medicinal uses 1 Asteraceae 12 Calendula officinalis Stomach upset, ulcers, inflammation 2 Lamiaceae 11 Hyssopus officinalis Eases nervous tension and anxiety 3 Leguminoseae 6 Astragalus squarosus Anemia, Colds and influenza, 4 Poaceae 6 Hordeum bulbosum Anti-cholesterol, anti- cancer 5 Plantaginaceae 5 Plantago psyllium Lowering of cholesterol 6 Apiaceae 4 Prangos ferulaceae Antispasmodic 7 Liliaceae 3 Allium cepa L. Improve eyesight, blood purifier 8 Malvaceae 3 Althaea officinalis Treating urethritis and kidney 9 Solanaceae 3 Hyoscyamus niger Pain – relieving, antispasmodic

10 Amaranthaceae 2 Amaranthus retroflexus Helpful in treating throat inflammations 11 Brassicaceae 2 Lepidium sativum Treatment of dysentery 12 Chenopodiaceae 2 Kochia prostrata Antiseizure activity 13 Ranunculaceae 2 Nigella sativa Prevent cancerous pancreatic cells 14 Alliaceae 1 Allium sativum Anti-cholesterol, antibacterial, anti-HIV 15 Boraginaceae 1 Borago officinalis Antidepressant 16 Cannabaceae 1 Cannabis sativa Used to treat old cancer 17 Caryophyllaceae 1 Saponaria officinalis A valuable remedy for rheumatism 18 Cucurbitaceae 1 Cucurbita pepo Treating bladder disorders 19 Euphorbiaceae 1 Ricinus communis Treat colds, colic, convulsions, tumors 20 Fumariaceae 1 Fumaria parviflora Fever, Chronic skin disease 21 Linaceae 1 Linum usitatissimum Anticancer, anti-cholesterolemia 22 Myrtaceae 1 Caryophyllus aromaticus Anodyne, antiemetic, antiseptic 23 Onagraceae 1 Oenothera biennis Reducing blood lipid concentrations 24 Pedaliacae 1 Sesamum indicum Anticancer, demulcent, emollient 25 Portulacaceae 1 Portulaca oleracea Treat headaches 26 Rutaceae 1 Ruta graveolens Strongly stimulating and antispasmodic 27 Zygophyllaceae 1 Zygophyllum eurypterum Anti-fungal, anti-worm 6

Table1. Seeds of 75 species of medicinal plants from the 27 families

Taking photos Monitoring

and analyzing photos

Machine learning

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1 2 3

1. Taking photos 24 photos from different orientations of each species

24 photos × 75 species = 1800 photos

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2. Monitoring and analyzing photos Filtering:

Altering the colour of images to gray

Making black and white images

Omitting noise of images using filtering functions

Preparing images for machine learning 9

3. Machine learning

Two types of neural networks were used

1. Radial Basis Function (RBF)

2. A neural net classifier based on pattern

recognation with two neural network

layers

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Network Inputs

Morphological feature

Aspect ratio

Eccentricity

Colour feature

Red

Green

Blue

Hue

Intensity

Saturation

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Table 2. Feature combinations

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Results and Discussion

Feature Training Accuracy (%) Test Accuracy (%) One Morphological 9 8 Two Morphological 0 0

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Morphological feature Table 3. Average accuracy of training and test data sets

Seed morphology, individually was not a good feature for accurate seed identification

Colour feature Colour Features Training Accuracy (%) Test Accuracy (%) One 3 2 Two 13 11 Three 62 54 Four 79 69 Five 98 85 Six 99 87

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Table 4. Average accuracy of training and test data sets for colour feature

Therefore, six features of colour was able to

identify the seeds more accurate than the

others.

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Morphology + colour Table 5. Average accuracy of training and test data sets

Feature Training Accuracy (%) Test Accuracy (%)

Morphology + Colour One One 18 15 One Two 92 80 One Three 82 65 One Four 70 62 One Five 49 44 One Six 50 42 Two one 17 15 Two Two 39 34 Two Three 65 58 Two Four 33 30 Two Five 33 31 Two Six 0 0

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Combination of one morphological with different

colour features was more accurate than two

morphological with different colour features.

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What would be seen at the end

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Astragalus siliquosus

Astragalus squarosus

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Conclusion

Six colour features more accurate

One morphological + two colour features accurate More features not necessarily more accurate

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Further works q More precise photography techniques that enable more accurate identification

q More seed photos in more different orientations

q Developing neural networks q Adding more seed species photos

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Any Question?

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