The key works of image analysis of spices and condiments included family, genus, morphological characteristics, and habitat and so on.
Imaging of live plants
High quality of images require on the move toward to presentation of information through electronic way. Digital cameras is the most effective way and affordable to produce these images. As a result, the number of images available to students and faculty is much higher than at any time in the past. As the internet set in motion to develop, photographers quickly realized that slide images and specimens could be shared more widely when they were digitized and made electronically available. Digital images over represent the most photogenic species, and present few features of each plant. These photographic specimens can serve as important supplements to herbarium collections by providing teaching and identification resources that are easily accessible to the public.
Spice and condiment of BSMRAU digital herbarium
There are various features of a plant’s collection and appearance that are used by expert botanists in plant morphological research.
Hottest chili (Capsicum chinense Jack.) ( http://dhcrop.bsmrau.net/hottest-chili/) : Image has taken in the flowering stage as well as fruit set in twig another one is ripe fruit, and processed chili in bottle.
Canary grass (Phalaris canariensis L.) (http://dhcrop.bsmrau.net/canary-grass/): There are one picture in field, grass with inflorescence/cob/panicle.
Celery (Apium graveoliens L.)(http://dhcrop.bsmrau.net/celery/): Image of whole plant in field.
Bay leaf ( Cinnamomum tamala (Buch.-Ham.) T.Nees & Eberm.)( http://dhcrop.bsmrau.net/indian-cassia/?doing_wp_cron ): Image has taken whole plant in field, another one twig and one more leave.
Rrosemary (Rosmarinus officinalis L.)( http://dhcrop.bsmrau.net/3710-2/): There are whole plant in field and an additional one whole plant with flower in field. Rosemary oil is used for purposes of fragrant bodily. perfumes or to emit an aroma into a room. It is also burnt as incense, and used in shampoos and cleaning products.
Star anise (Illicium verum Hook.)( http://dhcrop.bsmrau.net/star-anise-2/) : Image has in use fruits in plant, another image flower in plant and a further one carpel and seed.
Laurel (Laurus nobilis L.)( http://dhcrop.bsmrau.net/laurel/?doing_wp_cron): Twig with leaves image has here.
Basil ( Ocimum basilicum L.)( http://dhcrop.bsmrau.net/basil/): There are image of twig with flowering , Stem Anatomy, Leaf Anatomy, Inflorescence
Olive (Olea europea) (http://dhcrop.bsmrau.net/olive/?doing_wp_cron): Here is whole plant in field and another one flowering and one mature fruit in plant.
Fenugreek(Trigonella foenum-graecum L.)( http://dhcrop.bsmrau.net/3454-2/): There are image of one is whole plant , whole plant in field and methi itself(fruit/seed).
Camellia (Camellia spp.)(http://dhcrop.bsmrau.net/camellia/?doing_wp_cron): There are image of whole plant with flower.
Clove (Syzygium aromaticum (L.) Merr. & L.M.Perry)(http://dhcrop.bsmrau.net/clove/): There are one is heading, colve itself, whole plant, flowering and inflorescence and harvested clove.
Pepper (Piper nigrum)( http://dhcrop.bsmrau.net/pepper/): There are image of whole plant, leaf and fruit in inflorescence.
Lemon grass (Cymbopogon citratus (DC.) Stapf ) ( http://dhcrop.bsmrau.net/lemon-grass/): Image has taken of lemmon grass in field.
Panbilash (Clausena heptaphylla) (http://dhcrop.bsmrau.net/panbilash/): Here is leaf in plant.
Turmeric(http://dhcrop.bsmrau.net/3016-2/): There are image of plant in field and some are flowering stage, whole plant and rhizome.
Ginger ( Curcuma longa L.) ( http://dhcrop.bsmrau.net/zinger/?doing_wp_cron): There are image of rhizome, close view of rhizome, plant with rhizome and adventitious root system , picture of different part of plant and whole plant and flower of ginger plant.
Aloo Bokhara (Prunus bokharensis) (http://dhcrop.bsmrau.net/aloo-bukhara/): Image has taken of whole plant, fruit with plant, stem anatomy,leaf anatomy, close view of fruit with plant, close view of fruit with plant and close view of fruit with plant. The tree is of medium hardiness.
Curry patta (Murraya koenigii (L.) Spreng) (http://dhcrop.bsmrau.net/curry-patta/): There are image of whole plant, fruit with twig in plant.
Fennel (Foeniculum vulgare Mill.) ( http://dhcrop.bsmrau.net/fennel/?doing_wp_cron): Here is image of whole plant in field,flowering in field, and harvested curry fruit/seed.
Cardamom (Elettaria cardamomum (L.) Maton) (http://dhcrop.bsmrau.net/cardamon/): (A): whole plant in field,(B): real stem of plant underground of rhizome, (C): infloroscense, (D): capsule.
Black cumin ( Nigella sativa L.) (http://dhcrop.bsmrau.net/black-cumin/?doing_wp_cron): (A): whole plant in field,(B): close view of flower,(C): harvested blackcumin of BU kalajira-1 variety,(D): flowering stage,(E) : close view of capsule,(F): burst capsule.
Saffron(Crocus cartwrightianu)( http://dhcrop.bsmrau.net/saffron/): (A): whole plant in field with flower.
Chili (Capsicum spp.) (http://dhcrop.bsmrau.net/2564-2/) : Video are present, (A): twig of plant with flower, (B): fruiting stage of whole plant, (C): whole plant with root in laboratory,(D): cross section of chilli stem, (E): BARI variety of sweet pepper, (E): BARI variety of sweet pepper.
Vanilla (Vanilla planifolia Jacks. ex Andrews)( http://dhcrop.bsmrau.net/vanilla/): (A): one is plant with flower.
Garlic (Allium sativum L.) (http://dhcrop.bsmrau.net/garlic/): (A): plant in field, (B): mulching in field, (C): umbel inflorescence,(D): (E): umbel inflorescence.
Coriander (Coriandrum sativum L.)( http://dhcrop.bsmrau.net/coriander/): (A): whole plant in field with flowering stage, (B): close view of flower , after harvested coriander.
Cinnamon (Cinnamomum verum J.Presl) (http://dhcrop.bsmrau.net/2046-2/): whole plant in field, 3rd stem,, 4th bark and 5th whole plant (close view), another photo is twig.
Cumin (Cuminum cyminum L.) (http://dhcrop.bsmrau.net/2034-2/): (A): whole plant in field with flowering stage , (B): another harvested cumin.
Indian olive ( Elaeocarpus serratus L.) (http://dhcrop.bsmrau.net/indian-olive/): (A): whole plant, (B): close view of inflorescence, (C): stem of plant,(D): root anatomy, (E): stem anatomy, (F): leaf anatomy.
Linseed (Linum usitatissimum L.) ( http://dhcrop.bsmrau.net/1351-2/): Video has present, (A): whole plant of linseed in field, (B): close view of whole plant, (C): (D) close view of flower. (E): linseed after harvested.
Black mustard/true mustard ( Brassica nigra (L.) Koch) (http://dhcrop.bsmrau.net/black-mustard/): (A): whole plant, black mustard.
Our new algorithm locates leaves, stem and features of spices and condiments are most useful and performance against a manually analyzed subset of the images. The algorithm achieves an accuracy of features are sufficient to identify different species of different spices (Kislov et al., 2017; Groom et al., 2017). The global climate is changing rapidly, changes in plant flowering times of warming temperatures gives us a way to look at the impacts of climate change and allows us to predict further changes in coming decades. (Panchen et al., 2012). Phenological information included in digital herbarium archives can produce annual phenological estimates and it may be utilized due to their longer duration and ability to discriminate among the various components of the plant community, hold imperative potential for use in future research to supplement the deficiencies of other data sources as well as address a wide array of important issues in ecology (Park, 2012). The shapes of plant leaves are of great importance to plant biologists and botanists, as they can help in distinguishing plant species and measure their health (Laga et al., 2013). Digital pictures of leaves were enhanced, segmented, and a set of features were extracted from the image. (Joaci de M. Sá Junior et al., 2011, Easlon and Bloom, 2014, Wijesingha and Marikar,2012).
Advances in online resources and electronic publication provide the sciences with tools to revolutionize education and research. (Brach and Boufford, 2011). Image identification of plant requires the domain knowledge in the botanist field (Hussein et al., 2011). Image processing used for objective evaluation of quality parameters which is rapid, economical with great advantages in its objectiveness, efficiency and reliability (Dressler et al., 2017). The taxonomic classification of spices and condiments has usually been done by simple morphological observation and visual comparison, although the use of biometric indices has often proved to be a powerful approach in the taxonomic studies of the genus. Using image analysis techniques, (Orrù et al., 2013). Image analysis reassesses the taxonomic position of some neglected or doubtful taxa (Bacchetta et al., 2011). Identification keys require the observation of one or more morphological characters of an organism at each step of the process. While modern digital keys can overcome several constraints of classical paper-printed keys, their performance is not error-free (Carranza-Rozas et al., 2017). Moreover, identification cannot be always achieved when a specimen lacks some morphological features (Bruni et al., 2012). The characteristics of plant leaves image features are used to plant leaves classification and recognition (Du et al., 2013). Recognition of plant images is one of the research topics of computer vision. Object shape is more informative than its appearance properties such as texture and color vary between object instances more than the shape and focuses mainly on image enhancement, morphological feature such as area convexity (Bhardwaj et al., 2013; Carranza-Rozas et al., 2017). In recognition of different plant species through their leaf images is investigated by decision-making algorithm. The algorithm is employed to investigate inside the feature search space in order to obtain the best discriminant features for the recognition of individual species (Dressler et al., 2017).
In order to establish a feature search space, a set of feasible characteristics such as shape, morphology, texture and color are extracted from plant species (Ghasab et al.,2015). Using a computer-aided imaging analysis has been capable to identify of the seed morphometric variation as well as detect differences in seed morphology, both within and among populations. Morpho-colorimetric analysis clearly identified seeds from different populations and discriminated (Santo et al., 2015). Generally, cultivar identification is done on the basis of distinctive traits, such as shape, size, colour of the testa and ornamentations. Therefore, it is important, from both technical and economical points of view, to have a quick, reliable, repeatable, and nondestructive method to be able to identify and classify seeds objectively (Seregin and Stepanova, 2020). In order to promote computerised image analysis as a tool to aid visual inspection in the discrimination of different seeds, this technique was applied to analyze and identify seeds of varieties (Soltis, 2017). The assessment of some seed aspects such as colour, size and shape is important in grading system as well as to characterize accessions of core collections (Iva e al., 2013). Morpho-colorimetric quantitative variables describing seed size, shape, colour and texture were analyzed using image analysis techniques (Bianco et al., 2015). Human perception of plant leaf and flower colour can influence species management. Colour and colour contrast may influence the detectability of invasive or rare species during surveys. For measuring plant leaf and flower colour traits using images taken with digital cameras. The analysis of digital images taken with digital cameras is a practicable method for identifying in the field or lab (Kendal et al., 2013). Plant species classification using leaf samples is a challenging and important problem to solve. This motivates a separate processing of three feature types: shape, texture, and margin; combined using a probabilistic framework (Mallah et al., 2013). Colour, size and shape of seeds are very important in the grading system for many crops. Image analysis system for spices and condiments seeds was able to highlight differences among varieties and different stability of variety (Smykalova et al., 2011). The steps of image analysis done in some image processing focusing on agriculture application and also the details analysis of parallel and distributed image processing (Nasir et al., 2012).
Adopting features and orientations allows alongside comparison of images and the creation of standard displays for the identification of closely related species. Since the set of images from a given individual is intended to be a photographic specimen, the image set must contain enough images to allow the plant to be definitely identified at the species level (Walter et al., 2015). To make this possible, as complete a set of primary features as is practical should be taken from the same individual plant. The locality of each individual should also be recorded.
Recommendations
Images photographed can be used for a variety of purposes, including taxon identification, comparison of similar taxa while learning taxon recognition, and presentation in print publications or posters. Such uses require high quality images present the subject in a method that does not detract from the feature being presented. If the educational promise of digital plant images is to be fulfilled, many high quality images must be collected and made available. To accomplish these goals and this level of quality, the following recommendations are suggested:
1. Photographic standards should be developed for taxa other than plants.
2. The discussion of standards for photographing live plants needs to become part of the broader discussion of standards for sharing digital data. This discussion is ongoing for the sharing of metadata with different organization such as Bangladesh Agricultural Research Institute (BARI), Bangladesh Rice Research Institute (BRRI), Bangladesh Institute of Nuclear Agriculture (BINA), Bangladesh Jute Research Institute (BJRI), Bangladesh Sugar Crop Research Institute (BSRI), Bangladesh Tea Research Institute (BTRI) etc.
3. Digital image specimen collections should in due lessons take account of multiple individuals of each species. This would bless users to consider the range of variation among individuals in a specified area, and across the species' geographic range.
4. Progression should be developed to let for appearance of distance scales beside the images, without disrupting the images themselves. Because the images should be fitting for presentation in learning environments and print applications, people in the actual image is not recommended. 5. The images necessitate being permanently archived consent to resource developers to locate and access the originals. This can be accomplished by acclimatized biodiversity collections software to create suitable databases, or archiving the images in an on-line image repository.
6. A minimum image resolution of 6.0 megapixels will discuss images suitable for most print applications, and for enlargement to allow assessment of details here in the image.
7. The use of flash for seal images bring into being maximum depth of field, reduces blurring from camera motion, and minimizes distracting background. Flash photography allows for rapid, high quality photography even under the poor lighting conditions common in forests, and on cloudy days.