Human visual perception on color of melon fruit for ripeness judgement is a complex
phenomenon that depends on many factors, making the judgement is often inaccurate and inconsistent.
The objective of this study is to develop an image processing algorithm that can be used for distinguishing
ripe melons from unripe ones based on their skin color. The image processing algorithm could then be
used as a pre-harvest tool to facilitate farmers with enough information for making decisions about whether
or not the melon is ready to harvest. Four sample groups of Golden Apollo melon were harvested at four
different age, with 55 fruits in each group. Using the color distribution as results of the image analysis, the
first two groups of the samples can be separated from other groups with minimal overlap, but they cannot
be separated in the other two groups. The color image analysis of the melons in combination with
discriminant analysis could be used to distiguish between harvesting age groups with an average accuracy
of 86%.
Computer Vision based Model for Fruit Sorting using K-Nearest Neighbour clas...IJEEE
This document presents a computer vision based model for fruit sorting using a K-nearest neighbor classifier. It uses color and morphological features extracted from images to classify six types of fruits (red apples, green apples, golden apples, oranges, bananas, and carrots). The methodology involves image segmentation using K-means clustering, followed by extraction of color features from RGB and HSI color spaces and morphological features. A K-nearest neighbor classifier with Euclidean distance metric is then used for classification. The system achieved 100% accuracy in classifying the six fruit types based on the extracted features.
The quality identification of fruits in image processing using matlabeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IRJET- Automatic Fruit Quality Detection SystemIRJET Journal
This document presents an automatic fruit quality detection system that uses computer vision and image processing techniques. The system captures images of fruits on a conveyor belt using a camera. It then performs image processing on the images to analyze features like color, size, and texture. It can detect defects in fruits based on pixel analysis of the images. The fruits are then sorted based on color and graded based on size. The system aims to automate and improve the efficiency of the fruit sorting and grading process compared to manual methods. It analyzes the images, detects quality factors, and controls hardware like the conveyor belt based on the analysis results.
This document describes a fruit detection technique using morphological image processing. It outlines image acquisition by collecting fruit sample images in JPEG format. Image preprocessing steps like enhancement and noise removal are applied. Color and texture features are then extracted using color space conversion and Canny edge detection. Image segmentation is performed using a clustering algorithm. Morphological dilation is applied to segmented images to count fruit objects. The results show this technique can automatically count and distinguish fruits, providing a low-cost alternative to manual quality inspection.
An automatic fruit quality inspection system for sorting and grading of tomato fruit and defected tomato detection discussed here.The main aim of this system is to replace the manual inspection system.
This helps in speed up the process improve accuracy and efficiency and reduce time. This system collect image from camera which is placed on conveyor belt.
Then image processing is done to get required features of fruits such as texture, color and size.
Defected fruit is detected based on blob detection, color detection is done based on thresholding.
Size detection is based on binary image of tomato. Sorting is done based on color and grading is done based on size.
Scientific classification of ripening period and development ofAmirtha Ganesh
1. The study developed a color grade chart to classify the ripening stages of two mango varieties, Alphonso and Banganapalli, based on measurements of physio-chemical, internal and external color values, and textural characteristics throughout the ripening period.
2. Principal component analysis and hierarchical clustering classified the ripening period into five stages: unripe, early ripe, partially ripe, ripe, and overripe.
3. The color grade chart provides images and quality parameter values for each ripening stage, to allow non-destructive grading of mangoes for packing and processing industries.
The Effects of Segmentation Techniques in Digital Image Based Identification ...TELKOMNIKA JOURNAL
This paper presents the effects of segmentation techniques in the identification of Ethiopian
coffee variety. In Ethiopia, coffee varieties are classified based on their growing region. The most widely
coffee growing regions in Ethiopia are Bale, Harar, Jimma, Limu, Sidamo and Welega. Coffee beans of
these regions very in color shape and texture. We investigated various segmentation techniques for
efficient coffee beans variety identification system. Images of six different coffee beans varieties in Oromia
and Southern Ethiopia were acquired and analyzed. For this study Otsu, Fuzzy-C-Means (FCM) and Kmeans
segmentation techniques are considered. For classification of the varieties of Ethiopian coffee
beans back propagation neural network (BPNN) is used. From the experiment 94.54% accuracy is
achieved when BPNN is used on FCM segmentation technique.
IRJET- Use of E-Waste Material for Improving the Properties of Black Cotton SoilIRJET Journal
This document presents research on improving the engineering properties of black cotton soil through the addition of e-waste material. Black cotton soil has low bearing capacity and undergoes significant volumetric changes when moisture levels fluctuate. The researchers conducted various tests to analyze how adding 2-8% e-waste by weight impacted properties like specific gravity, consistency limits, maximum dry density, swelling index, and shear strength. They found that specific gravity, maximum dry density, and shear strength increased with up to 6% e-waste addition. Consistency limits, swelling index, and optimum moisture content generally improved as well. Overall, the study demonstrated that e-waste addition can effectively enhance the strength and reduce problematic swelling of black cotton soil for construction applications
Computer Vision based Model for Fruit Sorting using K-Nearest Neighbour clas...IJEEE
This document presents a computer vision based model for fruit sorting using a K-nearest neighbor classifier. It uses color and morphological features extracted from images to classify six types of fruits (red apples, green apples, golden apples, oranges, bananas, and carrots). The methodology involves image segmentation using K-means clustering, followed by extraction of color features from RGB and HSI color spaces and morphological features. A K-nearest neighbor classifier with Euclidean distance metric is then used for classification. The system achieved 100% accuracy in classifying the six fruit types based on the extracted features.
The quality identification of fruits in image processing using matlabeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IRJET- Automatic Fruit Quality Detection SystemIRJET Journal
This document presents an automatic fruit quality detection system that uses computer vision and image processing techniques. The system captures images of fruits on a conveyor belt using a camera. It then performs image processing on the images to analyze features like color, size, and texture. It can detect defects in fruits based on pixel analysis of the images. The fruits are then sorted based on color and graded based on size. The system aims to automate and improve the efficiency of the fruit sorting and grading process compared to manual methods. It analyzes the images, detects quality factors, and controls hardware like the conveyor belt based on the analysis results.
This document describes a fruit detection technique using morphological image processing. It outlines image acquisition by collecting fruit sample images in JPEG format. Image preprocessing steps like enhancement and noise removal are applied. Color and texture features are then extracted using color space conversion and Canny edge detection. Image segmentation is performed using a clustering algorithm. Morphological dilation is applied to segmented images to count fruit objects. The results show this technique can automatically count and distinguish fruits, providing a low-cost alternative to manual quality inspection.
An automatic fruit quality inspection system for sorting and grading of tomato fruit and defected tomato detection discussed here.The main aim of this system is to replace the manual inspection system.
This helps in speed up the process improve accuracy and efficiency and reduce time. This system collect image from camera which is placed on conveyor belt.
Then image processing is done to get required features of fruits such as texture, color and size.
Defected fruit is detected based on blob detection, color detection is done based on thresholding.
Size detection is based on binary image of tomato. Sorting is done based on color and grading is done based on size.
Scientific classification of ripening period and development ofAmirtha Ganesh
1. The study developed a color grade chart to classify the ripening stages of two mango varieties, Alphonso and Banganapalli, based on measurements of physio-chemical, internal and external color values, and textural characteristics throughout the ripening period.
2. Principal component analysis and hierarchical clustering classified the ripening period into five stages: unripe, early ripe, partially ripe, ripe, and overripe.
3. The color grade chart provides images and quality parameter values for each ripening stage, to allow non-destructive grading of mangoes for packing and processing industries.
The Effects of Segmentation Techniques in Digital Image Based Identification ...TELKOMNIKA JOURNAL
This paper presents the effects of segmentation techniques in the identification of Ethiopian
coffee variety. In Ethiopia, coffee varieties are classified based on their growing region. The most widely
coffee growing regions in Ethiopia are Bale, Harar, Jimma, Limu, Sidamo and Welega. Coffee beans of
these regions very in color shape and texture. We investigated various segmentation techniques for
efficient coffee beans variety identification system. Images of six different coffee beans varieties in Oromia
and Southern Ethiopia were acquired and analyzed. For this study Otsu, Fuzzy-C-Means (FCM) and Kmeans
segmentation techniques are considered. For classification of the varieties of Ethiopian coffee
beans back propagation neural network (BPNN) is used. From the experiment 94.54% accuracy is
achieved when BPNN is used on FCM segmentation technique.
IRJET- Use of E-Waste Material for Improving the Properties of Black Cotton SoilIRJET Journal
This document presents research on improving the engineering properties of black cotton soil through the addition of e-waste material. Black cotton soil has low bearing capacity and undergoes significant volumetric changes when moisture levels fluctuate. The researchers conducted various tests to analyze how adding 2-8% e-waste by weight impacted properties like specific gravity, consistency limits, maximum dry density, swelling index, and shear strength. They found that specific gravity, maximum dry density, and shear strength increased with up to 6% e-waste addition. Consistency limits, swelling index, and optimum moisture content generally improved as well. Overall, the study demonstrated that e-waste addition can effectively enhance the strength and reduce problematic swelling of black cotton soil for construction applications
1. Traditionally, fruits were sorted manually based on shape, size, and color, which was time-consuming. Image processing techniques were developed to automate fruit sorting using features extracted from images, such as size, shape, color, and texture.
2. Shape, color, and texture are used to classify fruits in the automated system. Shape is calculated using features like area, perimeter, axis lengths. Color is recognized using models from the RGB spectrum. Texture is determined from the spatial distribution of gray levels in an image.
3. Additional research reviews shape-based and texture-based recognition of fruits and vegetables. Shape is identified from standard geometric features. Texture is calculated using gray level co-occurrence matrix
Identification of Cocoa Pods with Image Processing and Artificial Neural Netw...IJAEMSJORNAL
Cocoa pods harvest is a process where peasant makes use of his experience to select the ripe fruit. During harvest, the color of the pods is a ripening indicator and is related to the quality of the cocoa bean. This paper proposes an algorithm capable of identifying ripe cocoa pods through the processing of images and artificial neural networks. The input image pass in a sequence of filters and morphological transformations to obtain the features of objects present in the image. From these features, the artificial neural network identifies ripe pods. The neural network is trained using the scaled conjugate gradient method. The proposed algorithm, developed in MATLAB ®, obtained a 91% of assertiveness in the identification of the pods. Features used to identify the pods were not affected by the capture distance of the image. The criterion for selecting pods can be modified to get similar samples with each other. For correct identification of the pods, it is necessary to take care of illumination and shadows in the images. In the same way, for accurate discrimination, the morphology of the pod was important.
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
Computer vision for purity, phenol, and pH detection of Luwak Coffee green beanTELKOMNIKA JOURNAL
Computer vision as a non-invasive bio-sensing method provided opportunity to detect purity, total
phenol, and pH in Luwak coffee green bean. This study aimed to obtain the best Artificial Neural Network
(ANN) model to detect the percentage of purity, total phenol, and pH on Luwak coffee green bean by using
color features (red-green-blue, gray, hue-saturation-value, hue-saturation-lightness, L*a*b*), and Haralick
textural features with color co-occurrence matrix including entropy, energy, contrast, homogeneity, sum
mean, variance, correlation, maximum probability, inverse difference moment, and cluster tendency.
The best ANN structure was (5 inputs; 30 nodes in hidden layer 1; 40 nodes in hidden layer 2; and 3
outputs) which had training mean square error (MSE) of 0.0085 and validation MSE of 0.0442.
IRJET- Identify Quality Index of the Fruit Vegetable by Non Destructive or wi...IRJET Journal
This document presents a literature review and proposed system for identifying the quality of fruits and vegetables using non-destructive image processing techniques. It discusses using computer vision algorithms like filtering, segmentation, feature extraction and classification to analyze images of fruits and determine quality metrics like size, shape, color and defects. The proposed system would capture images, preprocess them, extract features and classify fruits as good or defective quality without damaging the fruits. This could help automate quality inspection and grading of agricultural produce.
This document proposes a method for stem removal of citrus fruit images using morphological image processing and thresholding. The method involves preprocessing images by resizing, converting to HSV color space, and removing noise using Gaussian filtering. Stem removal is then performed using morphological opening, distance transforms, top-hat filtering, and thresholding the grayscale values to isolate the stem pixels. The proposed stem removal process aims to accurately extract citrus fruit from images for classification.
Defect Fruit Image Analysis using Advanced Bacterial Foraging Optimizing Algo...IOSR Journals
This document presents a method for segmenting defect areas on fruit images using an improved bacterial foraging optimization algorithm (ABFOA). The algorithm first decomposes the input fruit image into its red, green, and blue color components. It then applies the ABFOA to each color component separately to calculate individual thresholds. The final threshold is calculated as the average of the individual thresholds. This threshold is then applied to the original image to segment the defected areas. The method is tested on images of apples with defects like scab, rot, and blotch disease. Results show the ABFOA approach more accurately segments the defect areas compared to Otsu thresholding in terms of entropy, standard deviation, and peak signal-to
Transformations for non-destructive evaluation of brix in mango by reflectanc...IJECEIAES
Mango is a very popular climacteric fruit in America and Europe. Among the internal properties of the mango, total soluble solids (TSS) are an adequate indicator to estimate the quality of mango, however, the measurement of this indicator requires destructive tests. Several research have addressed similar issues; they have made use of pre-processing transformations without making it clear which of them is statistically better. Here, we created a new spectral database to build machine learning (ML) models. We analyzed a total of 18 principal component regression (PCR) models and 18 partial least squared regression (PLSR) models, where 4 types of transformations, 3 different feature extractors, and 3 different pre-processing techniques are combined. The research proposes a double cross validation (CV) both to determine the optimal number of components and to obtain the final metrics. The best model had a root mean square error (RMSE) of 1.1382 °Brix and a RMSE on the transformed scale of 0.5140. The best model used 4 components, used y2 transformation, reflectance R as the independent variable and MSC as a pre- processing technique.
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.
The document proposes an automated machine vision system for grading harvested mangoes based on maturity and quality using techniques like support vector regression to predict days until rotting, multi-attribute decision making to estimate quality, and fuzzy incremental learning to grade mangoes. Existing grading methods are discussed along with the methodology of the proposed system, which extracts image features to classify mangoes into four grades based on predicted maturity and estimated quality. The system aims to objectively grade mangoes to help vendors based on market distance and demand.
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.
Nowadays digital cameras are equipped with a
single sensor (CCD/CMOS), to reduce the size and cost of the
camera. The color filter array (CFA) is used to cover the sensor
and it consist of three primary colors such as red, green and blue
and it samples only one color component at each pixel location.
The process of estimating the other two missing color components
at each pixel location is known as demosaicing. The proposed
algorithm uses the directional color difference and multiscale
gradient method for green plane interpolation, this type of
interpolation method is used to reduce the artifacts and improve
the image quality. The red and blue plane are interpolated using
the estimated green plane, the bayer pattern is used for the
interpolation technique. The performance of the image is
measured using the CPSNR value
This document presents a method for detecting the quality of fruits using artificial neural networks (ANN). Images of fruit samples are taken and features like color, shape, and size are extracted. These features are used to train an ANN. Then, additional fruit samples can be tested using the trained ANN to classify them into categories representing quality levels like best, medium, or poor quality. The method was tested on three lemon samples of varying color, shape and size. The ANN accurately classified each sample based on its extracted features. This quality detection technique using ANN could be useful for applications in the agriculture industry.
AN ANALYSIS OF SURFACE AND GROWTH DIFFERENCES IN PLANTS OF DIFFERENT STAGES U...ijcseit
This document summarizes a research paper that analyzed surface and growth differences in plants at different stages using image processing techniques. Images were taken of bean plants over multiple stages and analyzed using MATLAB. Methods like filtering, edge detection, segmentation, and comparison were applied to detect differences in growth. The Canny edge detector was used to identify edges and the number of detected edges correlated with plant age and growth. Observations of leaf color changes could help identify plant diseases affecting growth. The image analysis helped quantify stem height increases over different plant stages.
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
This document summarizes a research paper that analyzed surface and growth differences in plants at different stages using image processing techniques. Images were taken of bean plants over multiple stages of growth and analyzed using MATLAB. Methods like filtering, edge detection, segmentation, and comparison were used to detect differences in plant growth. The Canny edge detection method was useful for identifying more edges in older, more mature plants compared to younger plants. The analysis found differences in binary values and detected edges that confirmed greater growth in older plants. This technique could help identify plant diseases or other factors affecting growth.
1) The document describes the development of color guides to evaluate the maturity of three cacao clones (EET8, CCN51, ICS60) using digital image processing.
2) Samples from different maturity stages were photographed and their colors were analyzed digitally. The colors changed with maturity and differed between clones.
3) Based on the color analysis, ranges were established for colors like green, yellow, orange, red and purple that appeared at different maturity stages for each clone. This will help establish maturity and harvest times to obtain high quality cacao.
Enzyme-Assisted Extraction of Anthocyanins Pigment from Purple Sweet Potatoes...IJERA Editor
Herein, anthocyanins pigment was extracted from purple sweet potatoes (PrunusnepalensisL.) with the assistant of the enzymes alpha-amylase in order to gather the natural colorants used in the food industry. To optimize the extraction conditions, the effect of extraction temperature and time was also investigated. The results showed that extraction temperature and time play a significant role in theextraction process. The optimum conditions are extraction temperature: 65 oC and time: 60 min, exhibited thehighest yield
Amazon products reviews classification based on machine learning, deep learni...TELKOMNIKA JOURNAL
In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Design, simulation, and analysis of microstrip patch antenna for wireless app...TELKOMNIKA JOURNAL
In this study, a microstrip patch antenna that works at 3.6 GHz was built and tested to see how well it works. In this work, Rogers RT/Duroid 5880 has been used as the substrate material, with a dielectric permittivity of 2.2 and a thickness of 0.3451 mm; it serves as the base for the examined antenna. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. The goal of this study was to get a more extensive transmission capacity, a lower voltage standing wave ratio (VSWR), and a lower return loss, but the main goal was to get a higher gain, directivity, and efficiency. After simulation, the return loss, gain, directivity, bandwidth, and efficiency of the supplied antenna are found to be -17.626 dB, 9.671 dBi, 9.924 dBi, 0.2 GHz, and 97.45%, respectively. Besides, the recreation uncovered that the transfer speed side-lobe level at phi was much better than those of the earlier works, at -28.8 dB, respectively. Thus, it makes a solid contender for remote innovation and more robust communication.
More Related Content
Similar to Color Distribution Analysis for Ripeness Prediction of Golden Apollo Melon
1. Traditionally, fruits were sorted manually based on shape, size, and color, which was time-consuming. Image processing techniques were developed to automate fruit sorting using features extracted from images, such as size, shape, color, and texture.
2. Shape, color, and texture are used to classify fruits in the automated system. Shape is calculated using features like area, perimeter, axis lengths. Color is recognized using models from the RGB spectrum. Texture is determined from the spatial distribution of gray levels in an image.
3. Additional research reviews shape-based and texture-based recognition of fruits and vegetables. Shape is identified from standard geometric features. Texture is calculated using gray level co-occurrence matrix
Identification of Cocoa Pods with Image Processing and Artificial Neural Netw...IJAEMSJORNAL
Cocoa pods harvest is a process where peasant makes use of his experience to select the ripe fruit. During harvest, the color of the pods is a ripening indicator and is related to the quality of the cocoa bean. This paper proposes an algorithm capable of identifying ripe cocoa pods through the processing of images and artificial neural networks. The input image pass in a sequence of filters and morphological transformations to obtain the features of objects present in the image. From these features, the artificial neural network identifies ripe pods. The neural network is trained using the scaled conjugate gradient method. The proposed algorithm, developed in MATLAB ®, obtained a 91% of assertiveness in the identification of the pods. Features used to identify the pods were not affected by the capture distance of the image. The criterion for selecting pods can be modified to get similar samples with each other. For correct identification of the pods, it is necessary to take care of illumination and shadows in the images. In the same way, for accurate discrimination, the morphology of the pod was important.
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
Computer vision for purity, phenol, and pH detection of Luwak Coffee green beanTELKOMNIKA JOURNAL
Computer vision as a non-invasive bio-sensing method provided opportunity to detect purity, total
phenol, and pH in Luwak coffee green bean. This study aimed to obtain the best Artificial Neural Network
(ANN) model to detect the percentage of purity, total phenol, and pH on Luwak coffee green bean by using
color features (red-green-blue, gray, hue-saturation-value, hue-saturation-lightness, L*a*b*), and Haralick
textural features with color co-occurrence matrix including entropy, energy, contrast, homogeneity, sum
mean, variance, correlation, maximum probability, inverse difference moment, and cluster tendency.
The best ANN structure was (5 inputs; 30 nodes in hidden layer 1; 40 nodes in hidden layer 2; and 3
outputs) which had training mean square error (MSE) of 0.0085 and validation MSE of 0.0442.
IRJET- Identify Quality Index of the Fruit Vegetable by Non Destructive or wi...IRJET Journal
This document presents a literature review and proposed system for identifying the quality of fruits and vegetables using non-destructive image processing techniques. It discusses using computer vision algorithms like filtering, segmentation, feature extraction and classification to analyze images of fruits and determine quality metrics like size, shape, color and defects. The proposed system would capture images, preprocess them, extract features and classify fruits as good or defective quality without damaging the fruits. This could help automate quality inspection and grading of agricultural produce.
This document proposes a method for stem removal of citrus fruit images using morphological image processing and thresholding. The method involves preprocessing images by resizing, converting to HSV color space, and removing noise using Gaussian filtering. Stem removal is then performed using morphological opening, distance transforms, top-hat filtering, and thresholding the grayscale values to isolate the stem pixels. The proposed stem removal process aims to accurately extract citrus fruit from images for classification.
Defect Fruit Image Analysis using Advanced Bacterial Foraging Optimizing Algo...IOSR Journals
This document presents a method for segmenting defect areas on fruit images using an improved bacterial foraging optimization algorithm (ABFOA). The algorithm first decomposes the input fruit image into its red, green, and blue color components. It then applies the ABFOA to each color component separately to calculate individual thresholds. The final threshold is calculated as the average of the individual thresholds. This threshold is then applied to the original image to segment the defected areas. The method is tested on images of apples with defects like scab, rot, and blotch disease. Results show the ABFOA approach more accurately segments the defect areas compared to Otsu thresholding in terms of entropy, standard deviation, and peak signal-to
Transformations for non-destructive evaluation of brix in mango by reflectanc...IJECEIAES
Mango is a very popular climacteric fruit in America and Europe. Among the internal properties of the mango, total soluble solids (TSS) are an adequate indicator to estimate the quality of mango, however, the measurement of this indicator requires destructive tests. Several research have addressed similar issues; they have made use of pre-processing transformations without making it clear which of them is statistically better. Here, we created a new spectral database to build machine learning (ML) models. We analyzed a total of 18 principal component regression (PCR) models and 18 partial least squared regression (PLSR) models, where 4 types of transformations, 3 different feature extractors, and 3 different pre-processing techniques are combined. The research proposes a double cross validation (CV) both to determine the optimal number of components and to obtain the final metrics. The best model had a root mean square error (RMSE) of 1.1382 °Brix and a RMSE on the transformed scale of 0.5140. The best model used 4 components, used y2 transformation, reflectance R as the independent variable and MSC as a pre- processing technique.
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.
The document proposes an automated machine vision system for grading harvested mangoes based on maturity and quality using techniques like support vector regression to predict days until rotting, multi-attribute decision making to estimate quality, and fuzzy incremental learning to grade mangoes. Existing grading methods are discussed along with the methodology of the proposed system, which extracts image features to classify mangoes into four grades based on predicted maturity and estimated quality. The system aims to objectively grade mangoes to help vendors based on market distance and demand.
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.
Nowadays digital cameras are equipped with a
single sensor (CCD/CMOS), to reduce the size and cost of the
camera. The color filter array (CFA) is used to cover the sensor
and it consist of three primary colors such as red, green and blue
and it samples only one color component at each pixel location.
The process of estimating the other two missing color components
at each pixel location is known as demosaicing. The proposed
algorithm uses the directional color difference and multiscale
gradient method for green plane interpolation, this type of
interpolation method is used to reduce the artifacts and improve
the image quality. The red and blue plane are interpolated using
the estimated green plane, the bayer pattern is used for the
interpolation technique. The performance of the image is
measured using the CPSNR value
This document presents a method for detecting the quality of fruits using artificial neural networks (ANN). Images of fruit samples are taken and features like color, shape, and size are extracted. These features are used to train an ANN. Then, additional fruit samples can be tested using the trained ANN to classify them into categories representing quality levels like best, medium, or poor quality. The method was tested on three lemon samples of varying color, shape and size. The ANN accurately classified each sample based on its extracted features. This quality detection technique using ANN could be useful for applications in the agriculture industry.
AN ANALYSIS OF SURFACE AND GROWTH DIFFERENCES IN PLANTS OF DIFFERENT STAGES U...ijcseit
This document summarizes a research paper that analyzed surface and growth differences in plants at different stages using image processing techniques. Images were taken of bean plants over multiple stages and analyzed using MATLAB. Methods like filtering, edge detection, segmentation, and comparison were applied to detect differences in growth. The Canny edge detector was used to identify edges and the number of detected edges correlated with plant age and growth. Observations of leaf color changes could help identify plant diseases affecting growth. The image analysis helped quantify stem height increases over different plant stages.
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
This document summarizes a research paper that analyzed surface and growth differences in plants at different stages using image processing techniques. Images were taken of bean plants over multiple stages of growth and analyzed using MATLAB. Methods like filtering, edge detection, segmentation, and comparison were used to detect differences in plant growth. The Canny edge detection method was useful for identifying more edges in older, more mature plants compared to younger plants. The analysis found differences in binary values and detected edges that confirmed greater growth in older plants. This technique could help identify plant diseases or other factors affecting growth.
1) The document describes the development of color guides to evaluate the maturity of three cacao clones (EET8, CCN51, ICS60) using digital image processing.
2) Samples from different maturity stages were photographed and their colors were analyzed digitally. The colors changed with maturity and differed between clones.
3) Based on the color analysis, ranges were established for colors like green, yellow, orange, red and purple that appeared at different maturity stages for each clone. This will help establish maturity and harvest times to obtain high quality cacao.
Enzyme-Assisted Extraction of Anthocyanins Pigment from Purple Sweet Potatoes...IJERA Editor
Herein, anthocyanins pigment was extracted from purple sweet potatoes (PrunusnepalensisL.) with the assistant of the enzymes alpha-amylase in order to gather the natural colorants used in the food industry. To optimize the extraction conditions, the effect of extraction temperature and time was also investigated. The results showed that extraction temperature and time play a significant role in theextraction process. The optimum conditions are extraction temperature: 65 oC and time: 60 min, exhibited thehighest yield
Similar to Color Distribution Analysis for Ripeness Prediction of Golden Apollo Melon (20)
Amazon products reviews classification based on machine learning, deep learni...TELKOMNIKA JOURNAL
In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Design, simulation, and analysis of microstrip patch antenna for wireless app...TELKOMNIKA JOURNAL
In this study, a microstrip patch antenna that works at 3.6 GHz was built and tested to see how well it works. In this work, Rogers RT/Duroid 5880 has been used as the substrate material, with a dielectric permittivity of 2.2 and a thickness of 0.3451 mm; it serves as the base for the examined antenna. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. The goal of this study was to get a more extensive transmission capacity, a lower voltage standing wave ratio (VSWR), and a lower return loss, but the main goal was to get a higher gain, directivity, and efficiency. After simulation, the return loss, gain, directivity, bandwidth, and efficiency of the supplied antenna are found to be -17.626 dB, 9.671 dBi, 9.924 dBi, 0.2 GHz, and 97.45%, respectively. Besides, the recreation uncovered that the transfer speed side-lobe level at phi was much better than those of the earlier works, at -28.8 dB, respectively. Thus, it makes a solid contender for remote innovation and more robust communication.
Design and simulation an optimal enhanced PI controller for congestion avoida...TELKOMNIKA JOURNAL
This document describes using a snake optimization algorithm to tune the gains of an enhanced proportional-integral controller for congestion avoidance in a TCP/AQM system. The controller aims to maintain a stable and desired queue size without noise or transmission problems. A linearized model of the TCP/AQM system is presented. An enhanced PI controller combining nonlinear gain and original PI gains is proposed. The snake optimization algorithm is then used to tune the parameters of the enhanced PI controller to achieve optimal system performance and response. Simulation results are discussed showing the proposed controller provides a stable and robust behavior for congestion control.
Improving the detection of intrusion in vehicular ad-hoc networks with modifi...TELKOMNIKA JOURNAL
Vehicular ad-hoc networks (VANETs) are wireless-equipped vehicles that form networks along the road. The security of this network has been a major challenge. The identity-based cryptosystem (IBC) previously used to secure the networks suffers from membership authentication security features. This paper focuses on improving the detection of intruders in VANETs with a modified identity-based cryptosystem (MIBC). The MIBC is developed using a non-singular elliptic curve with Lagrange interpolation. The public key of vehicles and roadside units on the network are derived from number plates and location identification numbers, respectively. Pseudo-identities are used to mask the real identity of users to preserve their privacy. The membership authentication mechanism ensures that only valid and authenticated members of the network are allowed to join the network. The performance of the MIBC is evaluated using intrusion detection ratio (IDR) and computation time (CT) and then validated with the existing IBC. The result obtained shows that the MIBC recorded an IDR of 99.3% against 94.3% obtained for the existing identity-based cryptosystem (EIBC) for 140 unregistered vehicles attempting to intrude on the network. The MIBC shows lower CT values of 1.17 ms against 1.70 ms for EIBC. The MIBC can be used to improve the security of VANETs.
Conceptual model of internet banking adoption with perceived risk and trust f...TELKOMNIKA JOURNAL
Understanding the primary factors of internet banking (IB) acceptance is critical for both banks and users; nevertheless, our knowledge of the role of users’ perceived risk and trust in IB adoption is limited. As a result, we develop a conceptual model by incorporating perceived risk and trust into the technology acceptance model (TAM) theory toward the IB. The proper research emphasized that the most essential component in explaining IB adoption behavior is behavioral intention to use IB adoption. TAM is helpful for figuring out how elements that affect IB adoption are connected to one another. According to previous literature on IB and the use of such technology in Iraq, one has to choose a theoretical foundation that may justify the acceptance of IB from the customer’s perspective. The conceptual model was therefore constructed using the TAM as a foundation. Furthermore, perceived risk and trust were added to the TAM dimensions as external factors. The key objective of this work was to extend the TAM to construct a conceptual model for IB adoption and to get sufficient theoretical support from the existing literature for the essential elements and their relationships in order to unearth new insights about factors responsible for IB adoption.
Efficient combined fuzzy logic and LMS algorithm for smart antennaTELKOMNIKA JOURNAL
The smart antennas are broadly used in wireless communication. The least mean square (LMS) algorithm is a procedure that is concerned in controlling the smart antenna pattern to accommodate specified requirements such as steering the beam toward the desired signal, in addition to placing the deep nulls in the direction of unwanted signals. The conventional LMS (C-LMS) has some drawbacks like slow convergence speed besides high steady state fluctuation error. To overcome these shortcomings, the present paper adopts an adaptive fuzzy control step size least mean square (FC-LMS) algorithm to adjust its step size. Computer simulation outcomes illustrate that the given model has fast convergence rate as well as low mean square error steady state.
Design and implementation of a LoRa-based system for warning of forest fireTELKOMNIKA JOURNAL
This paper presents the design and implementation of a forest fire monitoring and warning system based on long range (LoRa) technology, a novel ultra-low power consumption and long-range wireless communication technology for remote sensing applications. The proposed system includes a wireless sensor network that records environmental parameters such as temperature, humidity, wind speed, and carbon dioxide (CO2) concentration in the air, as well as taking infrared photos.The data collected at each sensor node will be transmitted to the gateway via LoRa wireless transmission. Data will be collected, processed, and uploaded to a cloud database at the gateway. An Android smartphone application that allows anyone to easily view the recorded data has been developed. When a fire is detected, the system will sound a siren and send a warning message to the responsible personnel, instructing them to take appropriate action. Experiments in Tram Chim Park, Vietnam, have been conducted to verify and evaluate the operation of the system.
Wavelet-based sensing technique in cognitive radio networkTELKOMNIKA JOURNAL
Cognitive radio is a smart radio that can change its transmitter parameter based on interaction with the environment in which it operates. The demand for frequency spectrum is growing due to a big data issue as many Internet of Things (IoT) devices are in the network. Based on previous research, most frequency spectrum was used, but some spectrums were not used, called spectrum hole. Energy detection is one of the spectrum sensing methods that has been frequently used since it is easy to use and does not require license users to have any prior signal understanding. But this technique is incapable of detecting at low signal-to-noise ratio (SNR) levels. Therefore, the wavelet-based sensing is proposed to overcome this issue and detect spectrum holes. The main objective of this work is to evaluate the performance of wavelet-based sensing and compare it with the energy detection technique. The findings show that the percentage of detection in wavelet-based sensing is 83% higher than energy detection performance. This result indicates that the wavelet-based sensing has higher precision in detection and the interference towards primary user can be decreased.
A novel compact dual-band bandstop filter with enhanced rejection bandsTELKOMNIKA JOURNAL
In this paper, we present the design of a new wide dual-band bandstop filter (DBBSF) using nonuniform transmission lines. The method used to design this filter is to replace conventional uniform transmission lines with nonuniform lines governed by a truncated Fourier series. Based on how impedances are profiled in the proposed DBBSF structure, the fractional bandwidths of the two 10 dB-down rejection bands are widened to 39.72% and 52.63%, respectively, and the physical size has been reduced compared to that of the filter with the uniform transmission lines. The results of the electromagnetic (EM) simulation support the obtained analytical response and show an improved frequency behavior.
Deep learning approach to DDoS attack with imbalanced data at the application...TELKOMNIKA JOURNAL
A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
The appearance of uncertainties and disturbances often effects the characteristics of either linear or nonlinear systems. Plus, the stabilization process may be deteriorated thus incurring a catastrophic effect to the system performance. As such, this manuscript addresses the concept of matching condition for the systems that are suffering from miss-match uncertainties and exogeneous disturbances. The perturbation towards the system at hand is assumed to be known and unbounded. To reach this outcome, uncertainties and their classifications are reviewed thoroughly. The structural matching condition is proposed and tabulated in the proposition 1. Two types of mathematical expressions are presented to distinguish the system with matched uncertainty and the system with miss-matched uncertainty. Lastly, two-dimensional numerical expressions are provided to practice the proposed proposition. The outcome shows that matching condition has the ability to change the system to a design-friendly model for asymptotic stabilization.
Implementation of FinFET technology based low power 4×4 Wallace tree multipli...TELKOMNIKA JOURNAL
Many systems, including digital signal processors, finite impulse response (FIR) filters, application-specific integrated circuits, and microprocessors, use multipliers. The demand for low power multipliers is gradually rising day by day in the current technological trend. In this study, we describe a 4×4 Wallace multiplier based on a carry select adder (CSA) that uses less power and has a better power delay product than existing multipliers. HSPICE tool at 16 nm technology is used to simulate the results. In comparison to the traditional CSA-based multiplier, which has a power consumption of 1.7 µW and power delay product (PDP) of 57.3 fJ, the results demonstrate that the Wallace multiplier design employing CSA with first zero finding logic (FZF) logic has the lowest power consumption of 1.4 µW and PDP of 27.5 fJ.
Evaluation of the weighted-overlap add model with massive MIMO in a 5G systemTELKOMNIKA JOURNAL
The flaw in 5G orthogonal frequency division multiplexing (OFDM) becomes apparent in high-speed situations. Because the doppler effect causes frequency shifts, the orthogonality of OFDM subcarriers is broken, lowering both their bit error rate (BER) and throughput output. As part of this research, we use a novel design that combines massive multiple input multiple output (MIMO) and weighted overlap and add (WOLA) to improve the performance of 5G systems. To determine which design is superior, throughput and BER are calculated for both the proposed design and OFDM. The results of the improved system show a massive improvement in performance ver the conventional system and significant improvements with massive MIMO, including the best throughput and BER. When compared to conventional systems, the improved system has a throughput that is around 22% higher and the best performance in terms of BER, but it still has around 25% less error than OFDM.
Reflector antenna design in different frequencies using frequency selective s...TELKOMNIKA JOURNAL
In this study, it is aimed to obtain two different asymmetric radiation patterns obtained from antennas in the shape of the cross-section of a parabolic reflector (fan blade type antennas) and antennas with cosecant-square radiation characteristics at two different frequencies from a single antenna. For this purpose, firstly, a fan blade type antenna design will be made, and then the reflective surface of this antenna will be completed to the shape of the reflective surface of the antenna with the cosecant-square radiation characteristic with the frequency selective surface designed to provide the characteristics suitable for the purpose. The frequency selective surface designed and it provides the perfect transmission as possible at 4 GHz operating frequency, while it will act as a band-quenching filter for electromagnetic waves at 5 GHz operating frequency and will be a reflective surface. Thanks to this frequency selective surface to be used as a reflective surface in the antenna, a fan blade type radiation characteristic at 4 GHz operating frequency will be obtained, while a cosecant-square radiation characteristic at 5 GHz operating frequency will be obtained.
Reagentless iron detection in water based on unclad fiber optical sensorTELKOMNIKA JOURNAL
A simple and low-cost fiber based optical sensor for iron detection is demonstrated in this paper. The sensor head consist of an unclad optical fiber with the unclad length of 1 cm and it has a straight structure. Results obtained shows a linear relationship between the output light intensity and iron concentration, illustrating the functionality of this iron optical sensor. Based on the experimental results, the sensitivity and linearity are achieved at 0.0328/ppm and 0.9824 respectively at the wavelength of 690 nm. With the same wavelength, other performance parameters are also studied. Resolution and limit of detection (LOD) are found to be 0.3049 ppm and 0.0755 ppm correspondingly. This iron sensor is advantageous in that it does not require any reagent for detection, enabling it to be simpler and cost-effective in the implementation of the iron sensing.
Impact of CuS counter electrode calcination temperature on quantum dot sensit...TELKOMNIKA JOURNAL
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
A progressive learning for structural tolerance online sequential extreme lea...TELKOMNIKA JOURNAL
This article discusses the progressive learning for structural tolerance online sequential extreme learning machine (PSTOS-ELM). PSTOS-ELM can save robust accuracy while updating the new data and the new class data on the online training situation. The robustness accuracy arises from using the householder block exact QR decomposition recursive least squares (HBQRD-RLS) of the PSTOS-ELM. This method is suitable for applications that have data streaming and often have new class data. Our experiment compares the PSTOS-ELM accuracy and accuracy robustness while data is updating with the batch-extreme learning machine (ELM) and structural tolerance online sequential extreme learning machine (STOS-ELM) that both must retrain the data in a new class data case. The experimental results show that PSTOS-ELM has accuracy and robustness comparable to ELM and STOS-ELM while also can update new class data immediately.
Electroencephalography-based brain-computer interface using neural networksTELKOMNIKA JOURNAL
This study aimed to develop a brain-computer interface that can control an electric wheelchair using electroencephalography (EEG) signals. First, we used the Mind Wave Mobile 2 device to capture raw EEG signals from the surface of the scalp. The signals were transformed into the frequency domain using fast Fourier transform (FFT) and filtered to monitor changes in attention and relaxation. Next, we performed time and frequency domain analyses to identify features for five eye gestures: opened, closed, blink per second, double blink, and lookup. The base state was the opened-eyes gesture, and we compared the features of the remaining four action gestures to the base state to identify potential gestures. We then built a multilayer neural network to classify these features into five signals that control the wheelchair’s movement. Finally, we designed an experimental wheelchair system to test the effectiveness of the proposed approach. The results demonstrate that the EEG classification was highly accurate and computationally efficient. Moreover, the average performance of the brain-controlled wheelchair system was over 75% across different individuals, which suggests the feasibility of this approach.
Adaptive segmentation algorithm based on level set model in medical imagingTELKOMNIKA JOURNAL
For image segmentation, level set models are frequently employed. It offer best solution to overcome the main limitations of deformable parametric models. However, the challenge when applying those models in medical images stills deal with removing blurs in image edges which directly affects the edge indicator function, leads to not adaptively segmenting images and causes a wrong analysis of pathologies wich prevents to conclude a correct diagnosis. To overcome such issues, an effective process is suggested by simultaneously modelling and solving systems’ two-dimensional partial differential equations (PDE). The first PDE equation allows restoration using Euler’s equation similar to an anisotropic smoothing based on a regularized Perona and Malik filter that eliminates noise while preserving edge information in accordance with detected contours in the second equation that segments the image based on the first equation solutions. This approach allows developing a new algorithm which overcome the studied model drawbacks. Results of the proposed method give clear segments that can be applied to any application. Experiments on many medical images in particular blurry images with high information losses, demonstrate that the developed approach produces superior segmentation results in terms of quantity and quality compared to other models already presented in previeous works.
Automatic channel selection using shuffled frog leaping algorithm for EEG bas...TELKOMNIKA JOURNAL
Drug addiction is a complex neurobiological disorder that necessitates comprehensive treatment of both the body and mind. It is categorized as a brain disorder due to its impact on the brain. Various methods such as electroencephalography (EEG), functional magnetic resonance imaging (FMRI), and magnetoencephalography (MEG) can capture brain activities and structures. EEG signals provide valuable insights into neurological disorders, including drug addiction. Accurate classification of drug addiction from EEG signals relies on appropriate features and channel selection. Choosing the right EEG channels is essential to reduce computational costs and mitigate the risk of overfitting associated with using all available channels. To address the challenge of optimal channel selection in addiction detection from EEG signals, this work employs the shuffled frog leaping algorithm (SFLA). SFLA facilitates the selection of appropriate channels, leading to improved accuracy. Wavelet features extracted from the selected input channel signals are then analyzed using various machine learning classifiers to detect addiction. Experimental results indicate that after selecting features from the appropriate channels, classification accuracy significantly increased across all classifiers. Particularly, the multi-layer perceptron (MLP) classifier combined with SFLA demonstrated a remarkable accuracy improvement of 15.78% while reducing time complexity.
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
AI for Legal Research with applications, toolsmahaffeycheryld
AI applications in legal research include rapid document analysis, case law review, and statute interpretation. AI-powered tools can sift through vast legal databases to find relevant precedents and citations, enhancing research accuracy and speed. They assist in legal writing by drafting and proofreading documents. Predictive analytics help foresee case outcomes based on historical data, aiding in strategic decision-making. AI also automates routine tasks like contract review and due diligence, freeing up lawyers to focus on complex legal issues. These applications make legal research more efficient, cost-effective, and accessible.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
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The objective of this study is to develop an image processing algorithm that can be
used for distinguishing ripe melons from unripe ones based on their skin color. The image
processing algorithm could then be used as a pre-harvest tool to facilitate farmers with enough
information for making decisions if the melon is ready to harvest.
2. Research Method
Four sample groups of Golden Apollo melon were obtained from a farmer in Sragen
District, Central Java Province, and categorized according to harvesting ages (46, 53, 60, and
67 Days After Planting or DAP, with 55 fruits in each group). At first, each melon was placed in
an enclosure (70×50×80 cm
3
) made from plywood and white thick clothe at one side, with four 5
W 235 lm cool daylight Philips lamps located at each of the four upper corners to illuminate
inside the enclosure (Figure 1a). A black clothe was placed on the bottom, while the insides of
the enclosure were covered by white papersat three sides and a white clothe at one side.
Images of the melon were captured using a DFK21BUCO3 (Imaging Source) with
2.8-12 mm CS-mount lens (Computar) color CCD camera mounted 40 cm above the floor of the
enclosure, and saved in jpeg format with a 744 by 480 pixels resolution. After the images had
been captured, the TSS was measured using a PR-210 type Atago refractometer (Figure 1b) at
three different locations: the upper, middle, and bottom parts of the fruit. An image processing
computer program was developed using OpenCV on a Windows platform to analyze the
captured images.
(a) (b)
Figure 1. Apparatus used in the experiment; (a) camera configuration for image acquisition and
(b) pocket refractometer for total soluble solid measurement
Feature extraction is an important process in the classification [14,15], but we have to
do some pre-processing to facilitate it. The pre-processing applied to the captured images were
binarization and binary opening and closing operations to remove noises, and masking of the
color image with the result of binary image with eliminated background. The binarization was
performed by converting the color image to grayscale, and pixels were assigned as parts of
object if their intensities were 60 or more, and the rest pixels were considered as background
and they were eliminated. To remove the fruit stem, an opening operation using a structuring
element of 7 by 7 pixels was conducted on the resulted binary image, followed by closing
operation using the same structuring element to keep the object in original size. Then the
resulting clean binary image from these operations was used to obtain a color image of the
melon minus the background by masking the binary image into the original image, as shown in
Figure 2.
Finally, the color of the fruit in normalized RGB (red-green-blue symbolized by r, g, and
b) and HSI (hue-saturation-intensity symbolized by H, S, and I) color models were extracted
from the masked image. The resulting of color distribution in each image was used to identify
parameters that were correlated with harvesting ages and fruit sweetness (as measured TSS of
the melon flesh). The two color distributions (rgb and HSI) of the melons were determined and
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processed using discriminant analysis to search for color attributes that can be used to
distinguish melon according to harvesting age (DAP), for real-time field applications just before
harvest as a final goal of this research.
(a) (b) (c) (d)
Figure 2. Image processing applied to extract color of melon fruits; a) original color image of
melon captured by CCD camera, b) binary image obtained by grayscale thresholding, c) binary
image after opening and closing with 7 by 7 pixels structuring element, and d) result of masking
for background elimination prior to color extraction
3. Results and Analysis
The relationship between average color values in RGB color model and harvesting age
and the relationship between average color values in HSI color model are shown in Figure 3.
The values of RGB components were displayed after normalization to 0 to 1 range. From the
figures, it is clear that the red component of the RGB color model increased with harvesting age,
though the incremental increase declined from harvesting age 60 to 67 DAP.
(a) (b) (c)
(d) (e) (f)
Figure 3. Change of color of melon skin with harvesting ages measured in RGB and HSI color
models; a) red color component, b) green color component, c) blue color component,d) hue
component, e) saturation component, and f) intensity component
The green component shows no clear differences between harvesting ages, while the
blue component responded opposite to that of the red; decreasing with increase in harvesting
age. This indicates that the color of melon skin changed from a blueish-green to a reddish-green
as harvesting age increased. However, the change from 60 to 67 DAP was smaller than that for
earlier harvesting ages. Similar phenomena are also shown in HSI color model, where values of
hue and saturation showing relationships with harvesting ages, while intensity shows positive
relationship with high overlapping. A similiar phenomenon was observed for TSS values, as
shown in Figure 4. The TSS increased rapidly from 46 to 60 DAP, but flattened off from 60 to 67
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DAP, indicating the melons were already entering the ripening stage by 60 DAP. This trend in
TSS values is consistent with melons being a non-climacteric fruit entering the ripening
stage [16]. From a harvest point of view, it is recommended that Golden Apollo melon be
harvested between 60 and 67 DAP, since they are already ripe, so they still have long enough
shelf life for distribution before consumption.
Figure 4. Change of TSS with harvesting ages measured using hand refractometer
(a)
(b)
(c) (d)
Figure 5. Color distribution in RGB and HSI color models of melon at several harvesting age as
results of image analysis; a) green-red distribution, b) blue-red distribution, c) saturation-hue
distribution, and d) intensity-hue distribution
The color distribution as results of the image analysis for the two color models are
shown in Figure 5. In the RGB color model as shown in Figure 5, it can be seen that the red and
blue components of the melons can be separated at 46 and 53 DAP groups from other groups
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with minimal overlap, but they cannot be separated from the other two groups (60 and 67 DAP).
This means that the RGB color distribution cannot be used to distinguish melons at 60 and 67
DAP, because they are all grouped together. The same phenomenon was also observed in the
HSI color model, where hue and saturation components could be separated at 46 and 53 DAP
with minimal overlap, but not at 60 and 67 DAP. This also means that HSI color distribution
cannot be used to distinguish melons at 60 and 67 DAP, because they all grouped together in
one group.
To overcome the problem with direct separation using color distribution as explained
above, Discriminant Analysis (DA) was applied as a new approach for ripeness separation. In
applying DA, each group member was analyzed to check any non-collinearity between
members. Tolerance values for the green and blue components of the RGB color model were
larger than 0.1 (0.6876 for green and 0.7553 for blue), while for HSI only intensity was larger
than 0.1 (0.5439). It means that green and blue of the RGB color model and intensity of the HSI
color model can be further analyzed, while the rest color components from the two color models
cannot. The VIF values were less than 10.0 for all three components as shown in Table 1,
indicate that all the three components are suitable for DA since there is no collinearity betwen
them. In general, the basic idea is to compute the theoretically expected value for each data
point based on the distribution.
Table 1. Results of multicolinearity statistical analysis
Parameter G B I
Tolerance 0.6876 0.7553 0.5439
VIF 1.4543 1.3239 1.8386
The Fisher’s box test shows that the p-value (0.0001) was less than alpha (0.05),
meaning that the data for the variables are not homogenous; a requirement for data processing
using by DA method. And finally, the Fisher discriminant function coefficients, as shown in
Table 2, were used to calculate the probability values to demonstrate and show that the
covariance matrices of green, blue, and intensity data were not homogenous.
Table 2. Fisher classification functions
Parameter Harvesting age (DAP)
46 53 60 67
Intercept -39086.97 -19823.05 -14491.43 -8252.95
G 131703.46 78743.26 60142.31 34292.33
B 60612.89 8402.87 9157.88 2943.39
I 78.56 27.49 10.62 8.74
G2
-113163.37 -81225.57 -63294.62 -37307.71
G*B -96686.05 -8729.54 -13655.13 2265.27
G*I -123.74 -41.75 -20.99 -14.77
B2
-31923.70 -13854.83 -14906.64 -28350.57
B*I -56.22 -4.70 5.02 23.63
I2
-0.06 -0.03 -0.01 -0.02
Four equations were developed using variables and coefficients determined before. Then the
four equations were tested by inputting the values of G, B, and I extracted from each image,
with the resulting values ranging from a 0 to 1 probability. The four equations are:
(1)
(2)
(3)
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(4)
where y46, y53, y60, and y67 are the groups of harvesting ages and B, G, and I are the values of
blue component, green component and intensity component obtained from image processing.
In every case, only one function among the four functions (equations 1 to 4) gave a
value of 1 or near to 1 as a result, and the other three functions gave 0 or near to zero values.
The melons were grouped into the group where the function gave a value of 1 or near to 1. The
results of harvesting ages classification using the four equations are shown in Table 3.
Table 3. Real Harvesting Age Groups and Predicted Groups using DA
Real
harvesting
age
Predicted harvesting age (DAP)
Total sampel
Accuracy
(%)46 53 60 67
46 55 0 0 0 55 100
53 0 53 2 0 55 96
60 0 1 35 19 55 63
67 0 0 8 47 55 85
Total 55 54 45 66 220 86
From Table 3, it is clear that DA could discriminate harvesting age into four groups with
an accuracy of 100%, 96%, 63% and 85% for the 46, 53, 60 and 67 DAP groups respectively.
As can be seen from these results, DA could accurately discriminate for the 46 and 53 DAP
groups, but not accurate for the 60 and 67 DAP groups. The reason for this is simply because
no clear change in melon skin color occurred at 60 and 67 DAP, and reflected in the relatively
smaller change in TSS during this period as shown in Figure 4. It suggests melons harvested at
60 DAP are already ripe and only a small increase in TSS value can be expected for later
harvest.
Another way to ensure the results of the Fisher classication method is with F1, F2,
and F3. To find what function will be usefull for DA, the discrimination functions were plotted as
shown in Figure 6a. This shows that F1 alone is enough for discrimintaion, with a slight increase
to 99.86% discrimination when F2 was included, while F3 was remain unsed. Another criterion
is the bi-plot correlation as shown in Figure 6b, which shows the three variables (green, blue,
and intensity) are not inline each other for the two functions (F1 and F2), indicating the variables
are eligible for discrimination. Canonical discriminant function coefficients as shown in Table 4
were used to develop a Canonical discriminant function, the results of which are plotted and
shown in Figure 7.
(a) (b)
Figure 6. Principal components for discrimination functions; a) Fisher classication method is with
F1, F2, and F3, and b) bi-plot correlation using F1 and F2
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Table 4. Canonical discriminant function coefficients
Parameter
Discriminant function
F1 F2 F3
Intercept -62.3984 -123.2831 -79.6778
G 97.9426 280.1924 124.0657
B 172.8325 -16.5531 30.3893
I -0.0424 0.0168 0.1626
Figure 7. Plot of discrimination function to predict a group of samples
All methods of data processing show that ripening at Golden Apollo melon used as
samples in this study happened at 60 days after planting. Functions developed using DA can be
used to discriminate that ripeness state from other two groups which are not ripen (46 and 53
DAP). Harvesting after 60 DAP might not suitable because increasing in TSS and color was not
significant, while wasting time and increasing risk of any damage of melon by insect and
diseases. If that the case, there is no necessity to distinguish 60 and 67 DAP since farmers are
suggested to harvest the fruits when they entered the ripening stage at 60 DAP.
4. Conclusion
From total soluble solid content, Golden Apollo melon fruits started to ripen since 60
days after planting, with total soluble solids increasing slightly after that. Color image analysis of
the melons in combination with discriminant analysis could be used to distiguish between
harvesting ages of 46, 53, 60, and 67 days after planting with an average accuracy of 86%.
However, the developed method could not distinguish melons harvested at 60 and 67 days after
planting with a high accuracy, as both groups of melons had already entered the ripening stage.
Acknowledgement
The author would like to thank The Sumitomo Foundation for funding this research
through the Fiscal Year 2014 Grant For Japan-Related Research Projects. Thanks are
extended to Garry Piller, a visiting professor at Kyoto University, Japan, and Dr. Sathivel
Subramaniam, Food Engineering Department, Louisiana State University, for their English
editing and proofreading of the manuscript.
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