Quality Evaluation Technique For Phyllanthus Emblica(Gooseberry) Using Comput...ijsrd.com
This paper proposes quality assessment method to classify a phyllanthus emblica (gooseberry) using computer vision by surface and geometric features. India is one of the most important gooseberry producers in North Asia, than Germany, Poland, U.K, Russia etc., but fruit sorting in some area is still done by hand which is tedious and inaccurate. Thus, the need exists for improvement of efficiency and accuracy of this fruit quality assessment that can meet the demands of international markets. Low-cost and non-destructive technologies capable of sorting gooseberry according to their properties would help to promote the gooseberry export industries. This paper propose the method of colorization and extracting value parameters, by this parameters the detection of browning or affected part and identification of the uniform shape and size. This differentiates the quality of gooseberries processed as well as fresh. For classification the decision tree is used.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Classification of Mango Fruit Varieties using Naive Bayes Algorithmijtsrd
Mangos are an important agricultural commodity in the global market for fresh products. In Myanmar, the type of mango called SeinTaLone is the best taste and the most people like it. Another type of mango called MaSawYin is not good taste but it is visually similar to the SeinTaLone. So, some people are difficult to classify the mango varieties. A means for distinguishing mango varieties is needed and therefore, some reliable technique is needed to discriminate varieties rapidly and non destructively. The main objective of this research was to classify the varieties of mango fruit that occur in Myanmar using Naive Bayes algorithm. The methodology involved image acquisition, pre processing and segmentation, feature extraction and classification of mango varieties. A method for classifying varieties of mangos using image processing technique is proposed in this paper. RGB image was first converted to HSV image. Then by using edge detection method and morphological operation, region of interest was segmented by taking into account only the HUE component image of the HSV image. Later, a total of 4 shape features and 13 texture features were extracted. Extracted features were given as inputs to a Naive Byaesian classifier to classify the test images as each type. The data set used had 50 mango images for each varieties of mango for training and 20 images of mango for each variety for testing. Ohnmar Win "Classification of Mango Fruit Varieties using Naive Bayes Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26677.pdfPaper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/26677/classification-of-mango-fruit-varieties-using-naive-bayes-algorithm/ohnmar-win
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Quality Evaluation Technique For Phyllanthus Emblica(Gooseberry) Using Comput...ijsrd.com
This paper proposes quality assessment method to classify a phyllanthus emblica (gooseberry) using computer vision by surface and geometric features. India is one of the most important gooseberry producers in North Asia, than Germany, Poland, U.K, Russia etc., but fruit sorting in some area is still done by hand which is tedious and inaccurate. Thus, the need exists for improvement of efficiency and accuracy of this fruit quality assessment that can meet the demands of international markets. Low-cost and non-destructive technologies capable of sorting gooseberry according to their properties would help to promote the gooseberry export industries. This paper propose the method of colorization and extracting value parameters, by this parameters the detection of browning or affected part and identification of the uniform shape and size. This differentiates the quality of gooseberries processed as well as fresh. For classification the decision tree is used.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Classification of Mango Fruit Varieties using Naive Bayes Algorithmijtsrd
Mangos are an important agricultural commodity in the global market for fresh products. In Myanmar, the type of mango called SeinTaLone is the best taste and the most people like it. Another type of mango called MaSawYin is not good taste but it is visually similar to the SeinTaLone. So, some people are difficult to classify the mango varieties. A means for distinguishing mango varieties is needed and therefore, some reliable technique is needed to discriminate varieties rapidly and non destructively. The main objective of this research was to classify the varieties of mango fruit that occur in Myanmar using Naive Bayes algorithm. The methodology involved image acquisition, pre processing and segmentation, feature extraction and classification of mango varieties. A method for classifying varieties of mangos using image processing technique is proposed in this paper. RGB image was first converted to HSV image. Then by using edge detection method and morphological operation, region of interest was segmented by taking into account only the HUE component image of the HSV image. Later, a total of 4 shape features and 13 texture features were extracted. Extracted features were given as inputs to a Naive Byaesian classifier to classify the test images as each type. The data set used had 50 mango images for each varieties of mango for training and 20 images of mango for each variety for testing. Ohnmar Win "Classification of Mango Fruit Varieties using Naive Bayes Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26677.pdfPaper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/26677/classification-of-mango-fruit-varieties-using-naive-bayes-algorithm/ohnmar-win
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
THE INFLUENCE OF MICROSTRUCTURE IN THE HOMOGENEITY OF HARDNESS STANDARD BLOCKSTito Livio M. Cardoso
The paper presents results of studies microhardness standard commercial blocks, indicating that a strict control of grain size is required in its manufacture punctual to avoid uncertainties in the measurements for calibration of durometers
descrição da publicação: Simposio Brasileiro de Estruturologia
data da publicação: 1998
Identification and Classification of Fruit DiseasesIJERA Editor
Diseases in fruit cause major problem in agricultural industry and also causes economic loss.The diseases in
fruits reduce the yield and also deteriorate the variety and its withdraw from the cultivation.So, earlier detection
of symptoms of fruit disease is required.In this paper a solution for identification and classification of fruit
diseases is proposed and experimentally validated.Ten different fruits has been selected with few of their
diseases . The image processing based proposed approach is composed of following main steps,first step is
segmentation using K-means and C-Means clustering algorithms,Second step is conducted a performance
evaluation of segmentation algorithm by measuring the parameters such as Measure of overlapping (MOL),
Measure of under-segmentation (MUS), Measure of over segmentation (MOS), Dice similarity measure (DSM),
Error-rate (ER). The segmentation performance is calculated based on the comparison between the manually
segmented ground truth G and segmentation result S generated by the image segmentation approach. After
segmentation features are extracted using GLCM. The k Nearest Neighbours Algorithm classifier is used to
classify the diseases in fruits .The collected database consists of 34 classes with 243 images .The experiment has
been conducted on database of 34 classes having training samples of 30,50,70 percent of database .The result of
classification has relatively higher accuracy in all cases when segmented using K-Means than C-Means
clustering algorithms
Metas são essências no mundo nos negócios no BNI não é diferente.
Traçar objetivos de curto, médio e longo prazo farão você tirar proveito da maior organização mundial de troca de referências qualificadas.
todos tenemos cicatrices, unas de un doloroso pasaedo causadas por nostros mismos al pecar y otras porque DIOS nos ha agarrado tan fuerte que no nos ha dejado caer en las garras del mal
L'hydrogène dans tous ses états - 1er décembre 2016Cluster TWEED
Voitures à l'hydrogène, bus à l'hydrogène, trains à l'hydrogène: c'est une réalité d'aujourd'hui, pleine de promesses pour l'avenir. L'hydrogène «énergie» se retrouve déjà dans les transports, oui, mais pas uniquement!
THE INFLUENCE OF MICROSTRUCTURE IN THE HOMOGENEITY OF HARDNESS STANDARD BLOCKSTito Livio M. Cardoso
The paper presents results of studies microhardness standard commercial blocks, indicating that a strict control of grain size is required in its manufacture punctual to avoid uncertainties in the measurements for calibration of durometers
descrição da publicação: Simposio Brasileiro de Estruturologia
data da publicação: 1998
Identification and Classification of Fruit DiseasesIJERA Editor
Diseases in fruit cause major problem in agricultural industry and also causes economic loss.The diseases in
fruits reduce the yield and also deteriorate the variety and its withdraw from the cultivation.So, earlier detection
of symptoms of fruit disease is required.In this paper a solution for identification and classification of fruit
diseases is proposed and experimentally validated.Ten different fruits has been selected with few of their
diseases . The image processing based proposed approach is composed of following main steps,first step is
segmentation using K-means and C-Means clustering algorithms,Second step is conducted a performance
evaluation of segmentation algorithm by measuring the parameters such as Measure of overlapping (MOL),
Measure of under-segmentation (MUS), Measure of over segmentation (MOS), Dice similarity measure (DSM),
Error-rate (ER). The segmentation performance is calculated based on the comparison between the manually
segmented ground truth G and segmentation result S generated by the image segmentation approach. After
segmentation features are extracted using GLCM. The k Nearest Neighbours Algorithm classifier is used to
classify the diseases in fruits .The collected database consists of 34 classes with 243 images .The experiment has
been conducted on database of 34 classes having training samples of 30,50,70 percent of database .The result of
classification has relatively higher accuracy in all cases when segmented using K-Means than C-Means
clustering algorithms
Metas são essências no mundo nos negócios no BNI não é diferente.
Traçar objetivos de curto, médio e longo prazo farão você tirar proveito da maior organização mundial de troca de referências qualificadas.
todos tenemos cicatrices, unas de un doloroso pasaedo causadas por nostros mismos al pecar y otras porque DIOS nos ha agarrado tan fuerte que no nos ha dejado caer en las garras del mal
L'hydrogène dans tous ses états - 1er décembre 2016Cluster TWEED
Voitures à l'hydrogène, bus à l'hydrogène, trains à l'hydrogène: c'est une réalité d'aujourd'hui, pleine de promesses pour l'avenir. L'hydrogène «énergie» se retrouve déjà dans les transports, oui, mais pas uniquement!
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
AN ANALYSIS OF SURFACE AND GROWTH DIFFERENCES IN PLANTS OF DIFFERENT STAGES U...ijcseit
Genomes are main reason for growth and surface differences in the plants. All plants grow on basis of their
different surrounding like soil, water, breed of seed, and climatic session. This paper attempts to find stem
growth from birth to maturity level of selected plant using image processing technique. Few reasons have
been observed commonly by the researchers that some plants could not grow sufficiently as to the other
plants of similar breed and age. Images were taken of different stage of bean plant and images of some
other plant samples were also included for better assessment. Researchers are trying to understand
through their calculative analysis by image processing emulator in MATLAB to view its reasons. Suitable
comparison technique is applied to detect their period of growth. Image processing methods like
Restoration, stem or leaves spots, filtering, and plant comparison have applied in MATLAB. Those effects
that are not supporting to grow the plants of their similar age group are matter to calculate scientifically
later in the future. The observation would help for further support in medicinal science or agricultural
science of plant and Bio-chemical.
AN ANALYSIS OF SURFACE AND GROWTH DIFFERENCES IN PLANTS OF DIFFERENT STAGES ...ijcseit
Genomes are main reason for growth and surface differences in the plants. All plants grow on basis of their
different surrounding like soil, water, breed of seed, and climatic session. This paper attempts to find stem
growth from birth to maturity level of selected plant using image processing technique. Few reasons have
been observed commonly by the researchers that some plants could not grow sufficiently as to the other
plants of similar breed and age. Images were taken of different stage of bean plant and images of some
other plant samples were also included for better assessment. Researchers are trying to understand
through their calculative analysis by image processing emulator in MATLAB to view its reasons. Suitable
comparison technique is applied to detect their period of growth. Image processing methods like
Restoration, stem or leaves spots, filtering, and plant comparison have applied in MATLAB. Those effects
that are not supporting to grow the plants of their similar age group are matter to calculate scientifically
later in the future. The observation would help for further support in medicinal science or agricultural
science of plant and Bio-chemical.
AN ANALYSIS OF SURFACE AND GROWTH DIFFERENCES IN PLANTS OF DIFFERENT STAGES U...ijcseit
Genomes are main reason for growth and surface differences in the plants. All plants grow on basis of their different surrounding like soil, water, breed of seed, and climatic session. This paper attempts to find stem growth from birth to maturity level of selected plant using image processing technique. Few reasons have been observed commonly by the researchers that some plants could not grow sufficiently as to the other plants of similar breed and age. Images were taken of different stage of bean plant and images of some other plant samples were also included for better assessment. Researchers are trying to understand through their calculative analysis by image processing emulator in MATLAB to view its reasons. Suitable comparison technique is applied to detect their period of growth. Image processing methods like Restoration, stem or leaves spots, filtering, and plant comparison have applied in MATLAB. Those effects that are not supporting to grow the plants of their similar age group are matter to calculate scientifically later in the future. The observation would help for further support in medicinal science or agricultural science of plant and Bio-chemical.
AN ANALYSIS OF SURFACE AND GROWTH DIFFERENCES IN PLANTS OF DIFFERENT STAGES U...ijcseit
Genomes are main reason for growth and surface differences in the plants. All plants grow on basis of their
different surrounding like soil, water, breed of seed, and climatic session. This paper attempts to find stem
growth from birth to maturity level of selected plant using image processing technique. Few reasons have
been observed commonly by the researchers that some plants could not grow sufficiently as to the other
plants of similar breed and age. Images were taken of different stage of bean plant and images of some
other plant samples were also included for better assessment. Researchers are trying to understand
through their calculative analysis by image processing emulator in MATLAB to view its reasons. Suitable
comparison technique is applied to detect their period of growth. Image processing methods like
Restoration, stem or leaves spots, filtering, and plant comparison have applied in MATLAB. Those effects
that are not supporting to grow the plants of their similar age group are matter to calculate scientifically
later in the future. The observation would help for further support in medicinal science or agricultural
science of plant and Bio-chemical.
AN ANALYSIS OF SURFACE AND GROWTH DIFFERENCES IN PLANTS OF DIFFERENT STAGES U...ijcseit
Genomes are main reason for growth and surface differences in the plants. All plants grow on basis of their
different surrounding like soil, water, breed of seed, and climatic session. This paper attempts to find stem
growth from birth to maturity level of selected plant using image processing technique. Few reasons have
been observed commonly by the researchers that some plants could not grow sufficiently as to the other
plants of similar breed and age. Images were taken of different stage of bean plant and images of some
other plant samples were also included for better assessment. Researchers are trying to understand
through their calculative analysis by image processing emulator in MATLAB to view its reasons. Suitable
comparison technique is applied to detect their period of growth. Image processing methods like
Restoration, stem or leaves spots, filtering, and plant comparison have applied in MATLAB. Those effects
that are not supporting to grow the plants of their similar age group are matter to calculate scientifically
later in the future. The observation would help for further support in medicinal science or agricultural
science of plant and Bio-chemical.
AN ANALYSIS OF SURFACE AND GROWTH DIFFERENCES IN PLANTS OF DIFFERENT STAGES U...ijcseit
Genomes are main reason for growth and surface differences in the plants. All plants grow on basis of their
different surrounding like soil, water, breed of seed, and climatic session. This paper attempts to find stem
growth from birth to maturity level of selected plant using image processing technique. Few reasons have
been observed commonly by the researchers that some plants could not grow sufficiently as to the other
plants of similar breed and age. Images were taken of different stage of bean plant and images of some
other plant samples were also included for better assessment. Researchers are trying to understand
through their calculative analysis by image processing emulator in MATLAB to view its reasons. Suitable
comparison technique is applied to detect their period of growth. Image processing methods like
Restoration, stem or leaves spots, filtering, and plant comparison have applied in MATLAB. Those effects
that are not supporting to grow the plants of their similar age group are matter to calculate scientifically
later in the future. The observation would help for further support in medicinal science or agricultural
science of plant and Bio-chemical.
An Effective Tea Leaf Recognition Algorithm for Plant Classification Using Ra...IJMER
A leaf is an organ of a vascular plant, as identified in botanical terms, and in particular in plant morphology. Naturally a leaf is a thin, flattened organ bear above ground and it is mainly used for photosynthesis. Recognition of plants has become an active area of research as most of the plant species are at the risk of extinction. Most of the leaves cannot be recognized easily since some are not flat (e.g. succulent leaves and conifers), some does not grow above ground (e.g. bulb scales), and some does not undergo photosynthetic function (e.g. cataphylls, spines, and cotyledons).In this paper, we mainly focused on tea leaves to identify the leaf type for improving tea leaf classification. Tea leaf images are loaded from digital cameras or scanners in the system. This proposed approach consists of three phases such as preprocessing, feature extraction and classification to process the loaded image. The tea leaf images can be identified accurately in the preprocessing phase by fuzzy denoising using Dual Tree Discrete Wavelet Transform (DT-DWT) in order to remove the noisy features and boundary enhancement to obtain the shape of leaf accurately. In the feature extraction phase, Digital Morphological Features (DMFs) are derived to improve the classification accuracy. Radial Basis Function (RBF) is used for efficient classification. The RBF is trained by 60 tea leaves to classify them into 6 types. Experimental results proved that the proposed method classifies the tea leaves with more accuracy in less time. Thus, the proposed method achieves more accuracy in retrieving the leaf type.
DETERMINATION OF ENGINEERING PROPERTIES OF POMEGRANATE FRUIT TO CALCULATION ...IJSIT Editor
In avoiding damage to fruit species the permissible falling height and permissible static pressure are
of great importance. The former is important in planning harvesting and handling operations, the latter in
selecting the height of transport containers. Fruits are generally transported in containers. The static and
dynamic forces which then act on the fruit will cause damage if they exceed given value. The static force may
be calculated from the weight of the fruit column being transported while the dynamic load is a consequence
of vibration caused by transport. The permitted static load for a given fruit may be determined
experimentally. In this study, physical properties of interest were determined for fresh pomegranate fruit
then calculations for the design of a suitable height were conducted based on the measured properties using
Ross and Isaacs’s theory. Maximum height for packing and storing of fresh pomegranate fruit in the box was
determined to be less than 123 cm based on a rupture force of 40.7 N.
Plant species identification based on leaf venation features using SVMTELKOMNIKA JOURNAL
The purpose of this study is to identify plant species using leaf venation
features. Leaf venation features were obtained through the extraction of leaf
venation features. The leaf image segmentation was performed to obtain
the binary image of the leaf venation which is then determined the branching
point and ending point. From these points, the extraction of leaf venation
feature was performed by calculating the value of straightness, a different
angle, length ratio, scale projection, skeleton length, number of segments, total
skeleton length, number of branching points and number of ending points.
So that from the extraction of leaf venation features 19 features were obtained.
Identification of plant species was carried out using Support Vector Machine
(SVM) with RBF kernel. The learning model was built using 75% of
the training data. The testing results using 25% of the data on the training
model, obtained an accuracy of 82.67%, with an average of precision of 84%
and recall of 83%.
A Comparison of Accuracy Measures for Remote Sensing Image Classification: Ca...CSCJournals
This work investigated the consistency of both the category-level and the map-level accuracy measures for different scenarios and features using Support Vector Machine. It was verified that the classification scenario and the features adopted have not influenced the accuracy measure consistency and all accuracy measures are highly positively correlated.
Computer Vision based Model for Fruit Sorting using K-Nearest Neighbour clas...IJEEE
Food grading and estimation has been observed as a key aspect in the field of food and agriculture. Increasing awareness towards quality of food has opened new opportunities of research in this area. Fruit grading and classification is also an important procedure to increase the quality evaluation in fruits grading which affects the export market. Computer vision plays an important role in automation of fruit classification. Total six varities of fruits and vegetable, i.e. red delicious apples, golden apples, green apples, oranges, bananas and carrots are analyzed. The system uses two image databases, one image database for training on the system and other for implementation of query images. In the packaging industry, color and morphological features are the most important feature for classification of fruits. After preprocessing, segmentation is done to extract the region of interest. In this paper, k mean clustering method is used for segmentation to extract region of interest from background. Color features are extracted from the RGB image and HSI image. Morphological features are calculated from RGB segmented image. In this paper, fruits are classified using the nearest neighbor classifier. Euclidean Distance Metric based k- Nearest Neighbor Classifier is developed for this particular application. The overall accuracy of the system is 100%.
For Domestic Wastewater Treatment, Finding Optimum Conditions by Particle Swa...Agriculture Journal IJOEAR
Abstract— Performing jar test method is used for finding out optimum conditions (coagulant type, coagulant dose, pH etc.)for treatment of domestic wastewater before physicochemical process, or coagulation process. In this study, Response Surface Method (RSM) is applied to determine optimum combinations of coagulant dose and pH value in jar test. Alum, FeCl3 and FeSO4 are used as coagulant and compared with highest removal efficiency of their two responses which turbidity and chemical oxygen demand (COD).Finding equations from RSM are also evaluated with Particle Swarm Optimization (PSO) method by using Matlab Program. Alum and Ferric Chloridedose500 mg/lat pH7 found as optimum conditions for domestic wastewater treatment. COD removal for Alum and Ferric Chloride are 90% and 70%,respectively.In addition, Because of becoming low COD removal (maximum 50%) and ineffectively color removal, Ferric Sulfate coagulant found as inconvenient for treating domestic wastewater.
Some Physical and Mechanical Properties of Xylopia aethiopica FruitScientific Review SR
Xylopia aethiopica is important crop that has medicinal and economic values and commonly utilized traditionally in
the treatment of several ailments. Thus, research carried out to investigate the physical and mechanical properties of
xylopia aethiopica, namely length, width, thickness, mean diameters, sphericity, surface area, volume, true and bulk
densities, porosity, angle of repose and static coefficient of friction, angle of repose, rupture force of xylopia
aethiopica at 9.7% moisture content (w.b.). Also, develop a database for engineers as well as by food scientists,
processors and breeders properties of xylopia aethiopica. The average length, width, and thickness varied between
25.13 and 87.60 mm, 3.48 and 5.79 mm, and 2.14 and 4.78 mm. The average sphericity, aspect ratio, surface area,
volume, 1000 unit mass bulk and true densities of xylopia aethiopica revealed 15.50%, 6.69%, 450.38 g, 389.31
kg/m
3
and 873.04 kg/m
3
.Detailed information (database) provided will be useful in the design and development of
machines to mitigate against stress involved in handling and processing of xylopia aethiopica (Annonaceae) fruit. It
is recommended to use stainless steel as materials for equipment construction.
In this study, an electronic system was built to determine the mass and volume of orange fruits from their dimensions using ultrasonic sensors. The system hardware parts include a metal box, three ultrasonic sensors, a load-cell sensor, an Arduino board, a memory card module, a voltage converter, a keypad, a display and a power adapter. A computer program was written to obtain data from ultrasonic sensors and determine the mass and volume of fruits using regression relationships in Arduino software. 100 samples of orange fruits (Dezful local variety) were picked randomly from a garden and various measurements were done to determine the main physical properties of fruits including three dimensions, mass (M), and volume (V). The system output values for mass and volume of orange fruits with their actual values had no significant difference at 1% probability level. The root mean square error (RMSE) in determining the oranges mass and volume by the system were 9.02 g and 10.90 cm3, respectively. In general, the proposed system performance was acceptable and it can be used for determining the mass and volume of orange fruits.
Google Calendar is a versatile tool that allows users to manage their schedules and events effectively. With Google Calendar, you can create and organize calendars, set reminders for important events, and share your calendars with others. It also provides features like creating events, inviting attendees, and accessing your calendar from mobile devices. Additionally, Google Calendar allows you to embed calendars in websites or platforms like SlideShare, making it easier for others to view and interact with your schedules.
Building a Raspberry Pi Robot with Dot NET 8, Blazor and SignalR - Slides Onl...Peter Gallagher
In this session delivered at Leeds IoT, I talk about how you can control a 3D printed Robot Arm with a Raspberry Pi, .NET 8, Blazor and SignalR.
I also show how you can use a Unity app on an Meta Quest 3 to control the arm VR too.
You can find the GitHub repo and workshop instructions here;
https://bit.ly/dotnetrobotgithub
1. Mohammad Bagher Lak. Geometric properties of Kohanz apple fruits. Agricultural Engineering
International: CIGR Journal. Vol.13, No.4, 2011. Manuscript No. 1825.
1
Geometric properties of Kohanz apple fruits
Mohammad Bagher Lak
(Young Researchers Club, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Abstract: Kohanz is a domestic apple variety which is grown in Iran. It is sensitive to packing
conditions. Bad packages normally cause decline in its quality. Therefore, apple’s geometric
properties are of important consideration in the design of the fruit’s packaging facilities. In this
study, a sample of 38 freshly harvested apple fruits was obtained, 82 images of their sides were
acquired and geometric dimensions were measured. Their axial dimensions, equivalent diameter
and sphericity were obtained using a vernier caliper with accuracy of 0.05 mm, while the cross
section area, eccentricity, perimeter and roundness were measured using a color based image
processing method. Their arithmetic mean diameter was 57.6 mm, while the mean eccentricity
was 0.3058, mean roundness was 0.5858 and mean sphericity was 0.9985. Therefore, the
packing design must be collections of spheroid spaces with diameters of about 6.8 mm which will
include all of the apples.
Keywords: eccentricity, equivalent diameter, image processing, roundness, sphericity
1. Introduction
Among all the fruits produced in Iran, apple is the most important economical and industrial fruit
(Meisami-asl et al, 2009). Kohanz apple is one of the famous Iranian domestic apple varieties.
The fruits are currently filled in woody or plastic made boxes without any arrangement. The
packages usually include some damaged fruits.
Bruise damage is a major cause of fruit quality loss (Zarifneshat et al., 2010). The packing
method causes compression damage in which fruit are bruised as they are pushed into a bin or
bag (Kupferman, 2006). Conformity of size is particularly desirable for packaging and display
purposes (Studman, 2001). Therefore, apples should be sorted into categories in order to provide
better markets. The sorting method can be size-based in which they are classified according to
their geometric properties.
Aviara et al. (2007), Hasankhani (2008), Amiriparian et al. (2008), Meisami-asl et al. (2009), and
Zarifneshat et al. (2010) worked on estimation of some agricultural products physical properties.
Application of image processing based methods in agricultural activities has been developed for
years. The applications involve activities such as auto-guidance (Benson et al., 2003; Han et al.,
2004), weed control (Nieuwenhuizen et al., 2007; Ghazali et al., 2009), harvesting (Lak et al.,
2010; Bulanon and Kataoka, 2010), yield monitoring (Chinchuluun et al., 2007; Annamalai, 2004)
and post harvest (Amiriparian et al., 2008; Rao and Renganathan, 2002; Zion et al., 1999).
2. Mohammad Bagher Lak. Geometric properties of Kohanz apple fruits. Agricultural Engineering
International: CIGR Journal. Vol.13, No.4, 2011. Manuscript No. 1825.
2
Geometric properties of fruits can be investigated by machine vision based methods (Rao and
Renganathan, 2002; Rashidi and Seyfi, 2007). Aviara et al. (2007) determined physical
properties of guna fruits; Davies (2010) investigated the physical properties of arigo seeds; and
Hasankhani (2008) studied some geometric properties (area, volume, shape, external defects such
as: greening, cracks and insect attack defects) of potato using machine vision. Amiriparian et al.
(2008) estimated three agricultural products’ (apple (Golden Delicious), pistachio and onion)
volume using image processing.
It is likely that consumer demand for improved quality, longer storage life and guaranteed
product safety will continue to grow (Studman, 2001). Therefore, providing an appropriate
package will promote Kohanz apple marketing.
The main objective of this study is to determine the freshly harvested Kohanz apple’s geometric
properties. The axial dimensions, namely, length L (maximum dimension), thickness T (medium
dimension) and width W (minimum dimension) were measured manually, meanwhile, cross
sectional areas (pixels included in detected feature), perimeters (pixels surrounding detected
feature), eccentricities (the ratio of the distance between the foci of the ellipse and its major axis
length ( 10 and circle = 0) (Gonzalez, et al., 2004), equivalent diameters (the diameter of a
circle with the same area as the region (Gonzalez, et al., 2004), and roundness of sides of the
fruits are the properties which can be defined using image processing. Sphericity was calculated
using manually measured quantities.
Good packaging would vouch for better marketing. While, identification of fruits’ geometric
properties is required to design appropriate package size. Therefore, the main goal of this study
is to define the optimum spheroid packing size for Kohanz apples using its geometric properties.
2. Materials and Methods
Geometric properties of Kohanz apples were divided into two categories: parameters which could
be measured using a vernier caliper; and properties that can be extracted from processed images.
2.1 Manual measured parameters
A sample of 38 apples was selected randomly from a grove in Hamedan, western Iran. The axial
dimensions, namely, length L, thickness T, and width W were measured using a vernier caliper
(TAKA
Vernier Caliper, 200×0.05mm). Arithmetic mean diameter Da, and sphericity were
calculated using Equation (1) and (2) as follows (Kibar and Öztürk, 2008; Jain and Bal, 1997).
3
WTL
Da
(1)
Where:
D a= arithmetic mean diameter (mm), W = width (mm), T = thickness (mm), and L = length (mm)
3. Mohammad Bagher Lak. Geometric properties of Kohanz apple fruits. Agricultural Engineering
International: CIGR Journal. Vol.13, No.4, 2011. Manuscript No. 1825.
3
3 2
)2(
L
WTLWT
(2)
Where:
= sphericity (dimensionless)
2.2 Image processing extracted properties
A digital camera (Sony, DSC-H5, Color CCD Camera) was used to acquire 82 images of 38
apples’ sides. At least, two sides of each apple were imaged. Format of the images was jpeg and
they were in RGB (red-green-blue) color space. Because some of the samples were not
symmetric, they were imaged from more than two sides.
The images were converted to L*a*b color space. The L*a*b* space consists of a luminosity
layer L*, chromaticity layer a* indicating where color falls along the red-green axis, and
chromaticity layer b* indicating where the color falls along the blue-yellow axis (Matlab, 2007).
Then, they were converted to binary form, noise-reduced, labeled, and the properties were
extracted. The properties were: area, eccentricity and perimeter. Area and perimeter were in
terms of pixel and eccentricity was dimensionless.
Roundness was estimated using the relationship between area and perimeter (Equation (3)) and it
was also dimensionless.
2
4
P
A
R
(MATLAB, 2007)
(3)
Where:
R = roundness (dimensionless), A = area (pixel), and P = perimeter (pixel).
3. Results and Discussion
Apples’ length, thickness and width were measured; therefore their equivalent diameter and
sphericity were calculated by the data. Table 1 shows the descriptive statistics of properties
which were measured by means of a caliper.
All the apples were imaged at least with two sides (Figure 1). First, the images were converted to
L*a*b color space (Figure 2), then they were converted to binary form (Figure 3). Finally, they
were noise-reduced and some properties were extracted (Figure 4). Area, eccentricity and
perimeter were the properties extracted from processed images. The roundness was calculated by
4. Mohammad Bagher Lak. Geometric properties of Kohanz apple fruits. Agricultural Engineering
International: CIGR Journal. Vol.13, No.4, 2011. Manuscript No. 1825.
4
using a function between area and perimeter (Equation (3)). The descriptive statistics of apples’
properties which obtained from image processing are listed in Table 2.
a b
Figure 1 Typical original images acquired from two sides of apples
a b
Figure 2 Images in L*a*b color space
5. Mohammad Bagher Lak. Geometric properties of Kohanz apple fruits. Agricultural Engineering
International: CIGR Journal. Vol.13, No.4, 2011. Manuscript No. 1825.
5
a b
Figure 3 Binary images
a b
Figure 4 Noise-reduced binary images
Table 1 Descriptive statistics of manually measured properties of apples
Properties Na
Range Minimum Maximum Mean Std. Deviation Variance
Lb
82 17.45 49.65 67.10 60.05 3.0189 9.114
Tc
82 15.50 49.50 65.00 57.99 2.9438 8.666
Wd
82 13.50 47.00 60.50 54.83 3.1641 10.011
Da
e
82 15.48 48.72 64.20 57.62 1.7572 3.088
a
Sample size
b
Length in millimeter
c
Thickness in millimeter
d
Width in millimeter
e
Arithmetic mean diameter in millimeter
6. Mohammad Bagher Lak. Geometric properties of Kohanz apple fruits. Agricultural Engineering
International: CIGR Journal. Vol.13, No.4, 2011. Manuscript No. 1825.
6
Table 2 Descriptive statistics of the properties measured by image processing
N Range Minimum Maximum Mean Std.
Deviation
Variance
Area (pixels) 82 1014172 1044468 2058640 1494828.10 204750.905 4.192E10
Eccentricity (pixels) 82 0.6758 0.0446 0.7204 0.3058 0.1116 0.012
Equivalent Diameter (pixels) 82 15.03 48.72 63.75 57.63 2.8285 8.000
Perimeter (pixels) 82 13130.5 4080.5 17211.0 6321.7 2239.0 5013152.3
Roundness (dimensionless) 82 0.7881 0.0526 0.8407 0.5858 0.2396 0.057
Sphericity (dimensionless) 82 0.0051 0.9948 0.9999 0.9985 0.0012 0.000
Mean and range of length, thickness, width, eccentricity, equivalent diameter, roundness and
sphericity were 60.05[49.65 67.10], 57.99[49.50 65.00], 54.83[47.00 60.50], 0.3058
[0.0446 0.7204], 57.63[48.72 63.75], 0.5858 [0.0526 0.8407], and 0.9985 [0.9948 0.9999]
respectively (Table 1 and Table 2).
Extracted geometric properties of Kohanz apples were of at least two sides of each apple,
therefore, the properties involve three dimension properties of them. The maximum, mean and
minimum diameters of Golab apple were 65.04, 53.50 and 35.14 mm respectively (Meisami-asl,
2009); meanwhile the measures were 64.20, 57.62, and 48.72 mm for Kohanz variety.
The apples’ sphericity ranges show that the apples can be considered as spheres with diameters
equal to equivalent diameters. On the other hand, their maximum dimension is their length.
Therefore, their packing design must be collections of spheroid spaces with diameters of about 68
mm which includes all of them.
4. Conclusions
This paper considered Kohanz apple’s geometric properties. Their length, thickness and width
were measured by caliper. Their equivalent diameters and sphericity were calculated. Their area,
perimeter and eccentricity were extracted by image processing and roundness was computed by a
function with area and perimeter.
Finally it was determined that a collection of spheroid spaces with diameters of about 68 mm will
be the most appropriate package design.
References
Amiriparian, J., M. H. Khoshtaghaza, and E. Kabir. 2008. A practical model for estimation of
agricultural products volume using machine vision (in Persian). In Proc. 5th National
Congress on Agricultural Machinery Engineering and Mechanization, Ferdowsi University of
Mashhad, Iran.
Annamalai, P. 2004. Citrus yield mapping system using machine vision. M.S. Thesis,
Department of Agricultural and Biological Engineering, University of Florida. 81.
7. Mohammad Bagher Lak. Geometric properties of Kohanz apple fruits. Agricultural Engineering
International: CIGR Journal. Vol.13, No.4, 2011. Manuscript No. 1825.
7
Aviara, N. A, S. K. Shittu, and M. A. Haque. 2007. Physical properties of guna fruits relevant in
bulk handling and mechanical processing. International Agrophysics, 21(1): 7-16.
Benson, E. R., J. F. Reid, and Q. Zhang. 2003. Machine vision-based guidance system for
agricultural grain harvester using cut-edge detection. Biosystems Engineering, 86 (4), 389–
398.
Bulanon, D. M., and T. Kataoka . 2010. Fruit detection system and an end effector for robotic
harvesting of Fuji apples. CIGR Journal, 12(1): 203-210.
Chinchuluun, R., W. S. Lee, and R. Ehsani. 2007. Citrus yield mapping system on a canopy,
shake and catch harvester. In Proc. ASABE Annual International Meeting, Paper Number:
073050.
Davies, R. M. 2010. Some physical properties of arigo seeds. International Agrophysics, 24(1):
89-92.
Ghazali, K. H, M. M. Mustafa, and A. Hussain. 2009. Machine vision system for automatic
weeding strategy in oil palm plantation using image filtering technique. International Journal
of Electrical, Computer, and Systems Engineering 3:4, 193-197.
Gonzalez, R. C., R. E. Woods, and S. L. Eddins. 2004. Digital image processing using
MATLAB. Pearson Education. 609 p.
Han, S., Q. Zhang, B. Ni, and J. F. Reid. 2004. A guidance directrix approach to vision-based
vehicle guidance systems. Computers and Electronics in Agriculture, 43: 179-195.
Hasankhani, R. 2008. Studying of potato physical properties by means of machine vision (in
Persian). In Proc. 5th National Congress on Agricultural Machinery Engineering and
Mechanization, Ferdowsi University of Mashhad, Iran.
Jain, R. K., and S. Bal. 1997. Properties of pearl millet. Journal of Agricultural Engineering
Research, 66(2): 85-91.
Kibar, H., and T. Öztürk. 2008. Physical and mechanical properties of soybean. International
Agrophysics, 22: 239-244.
Kupferman, E. 2006. Minimizing bruising in apples. Postharvest Information Network,
Washington State University, Tree Fruit Research and Extension Center.
Lak, M. B., S. Minaei, J. Amiriparian, and B. Beheshti. 2010. Apple fruits recognition under
natural luminance using machine vision. Advance Journal of Food Science and Technology,
2(6): 325-327.
MATLAB. 2007. Image processing toolbox help.
Meisami-asl, E., S. Rafiee, A. Keyhani, and A. Tabatabaeefar. 2009. Some physical properties
of apple cv. ‘Golab’. CIGR Journal, 11, 6, Manuscript 1124.
Nieuwenhuizen, A. T., L. Tang, J. W. Hofstee, J. Muller, and E. J. V. Henten. 2007. Colour
based detection of volunteer potatoes as weeds in sugar beet fields using machine vision.
Precision Agriculture, 8: 267–278.
Rashidi, M., and K. Seyfi. 2007. Classification of fruits shape in cantaloupe using the analysis
of geometrical attributes. World Journal of Agricultural Science, 3(6): 735-740.
Rao, P. S., and S. Renganathan. 2002. New approaches for size determination of apple fruits for
automatic sorting and grading. Iranian Journal of Electrical and Computer Engineering. 1(2):
90-97.
Studman, C. J. 2001. Computers and electronics in postharvest technology - a review.
Computers and Electronics in Agriculture, 30: 109-124.
Zarifneshat, S., H. R. Ghassemzadeh, M. Sadeghi, M. H. Abbaspour-Fard, E. Ahmadi, A. Javadi,
and M. T. Shervani-Tabar. 2010. Effect of impact level and fruit properties on Golden
8. Mohammad Bagher Lak. Geometric properties of Kohanz apple fruits. Agricultural Engineering
International: CIGR Journal. Vol.13, No.4, 2011. Manuscript No. 1825.
8
Delicious apple bruising. American Journal of Agricultural and Biological Sciences, 5 (2):
114-121.
Zion, B., A. Shklyar, and I. Karplus. 1999. Sorting fish by computer vision. Computers and
Electronics in Agriculture, 23: 175-187.