In the area of machine learning performance analysis is the major task in order to get a better performance both in training and testing model. In addition, performance analysis of machine learning techniques helps to identify how the machine is performing on the given input and also to find any improvements needed to make on the learning model. Feed-forward neural network (FFNN) has different area of applications, but the epoch convergences of the network differs from the usage of transfer function. In this study, to build the model for classification and moisture prediction of soil, rectified linear units (ReLU), Sigmoid, hyperbolic tangent (Tanh) and Gaussian transfer function of feed-forward neural network had been analyzed to identify an appropriate transfer function. Color, texture, shape and brisk local feature descriptor are used as a feature vector of FFNN in the input layer and 4 hidden layers were considered in this study. In each hidden layer 26 neurons are used. From the experiment, Gaussian transfer function outperforms than ReLU, sigmoid and tanh transfer function. But the convergence rate of Gaussian transfer function took more epoch than ReLU, Sigmoid and tanh.
Ananas comosus crown image thresholding and crop counting using a colour spac...TELKOMNIKA JOURNAL
The implementation of unmanned aerial vehicle (UAV) technology having image processing capabilities provides an alternative way to observe pineapple crowns captured from aerial images. In the majority of pineapple plantations, an agricultural officer will physically count the crop yield prior to harvesting the Ananas Comosus, also known as pineapple. This process is particularly evident in large plantation areas to accurately identify pineapple numbers. To alleviate this issue, given it is both time-consuming and arduous, automating the process using image processing is suggested. In this study, the possibilities and comparisons between two techniques associated with an image thresholding scheme known as HSV and L*A*B* colour space schemes were implemented. This was followed by determining the threshold by applying an automatic counting (AC) method to count the crop yield. The results of the study found that by applying colour thresholding for segmentation, it improved the low contrast image due to different heights and illumination levels on the acquired colour image. The images that were acquired using a UAV revealed that the best distance for capturing the images was at the height of three (3) metres above ground level. The results also confirm that the HSV colour space provides a more efficient approach with an average error increment of 47.6% when compared to the L*A*B*colour space.
Image Analysis using Color Co-occurrence Matrix Textural Features for Predict...TELKOMNIKA JOURNAL
This study aimed to determine the nitrogen content of spinach leaves by using computer imaging
technology. The application of Color Co-occurrence Matrix (CCM) texture analysis was used to recognize
the pattern of nitrogen content in spinach leaves. The texture analysis consisted of 40 CCM textural
features constructed from RGB and grey colors. From the 40 textural features, the best features-subset
was selected by using features selection method. Features selection method can increase the accuracy of
image analysis using ANN model to predict nitrogen content of spinach leaves. The combination of ANN
with Ant Colony Optimization resulted in the most optimal modelling with mean square error validation
value of 0.0000083 and the R2 testing-set data = 0.99 by using 10 CCM textural features as the input of
ANN. The computer vision method using ANN model which has been developed can be used as
non-invasive sensing device to predict nitrogen content of spinach and for guiding farmers in the accurate
application of their nitrogen fertilization strategies using low cost computer imaging technology.
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
Ananas comosus crown image thresholding and crop counting using a colour spac...TELKOMNIKA JOURNAL
The implementation of unmanned aerial vehicle (UAV) technology having image processing capabilities provides an alternative way to observe pineapple crowns captured from aerial images. In the majority of pineapple plantations, an agricultural officer will physically count the crop yield prior to harvesting the Ananas Comosus, also known as pineapple. This process is particularly evident in large plantation areas to accurately identify pineapple numbers. To alleviate this issue, given it is both time-consuming and arduous, automating the process using image processing is suggested. In this study, the possibilities and comparisons between two techniques associated with an image thresholding scheme known as HSV and L*A*B* colour space schemes were implemented. This was followed by determining the threshold by applying an automatic counting (AC) method to count the crop yield. The results of the study found that by applying colour thresholding for segmentation, it improved the low contrast image due to different heights and illumination levels on the acquired colour image. The images that were acquired using a UAV revealed that the best distance for capturing the images was at the height of three (3) metres above ground level. The results also confirm that the HSV colour space provides a more efficient approach with an average error increment of 47.6% when compared to the L*A*B*colour space.
Image Analysis using Color Co-occurrence Matrix Textural Features for Predict...TELKOMNIKA JOURNAL
This study aimed to determine the nitrogen content of spinach leaves by using computer imaging
technology. The application of Color Co-occurrence Matrix (CCM) texture analysis was used to recognize
the pattern of nitrogen content in spinach leaves. The texture analysis consisted of 40 CCM textural
features constructed from RGB and grey colors. From the 40 textural features, the best features-subset
was selected by using features selection method. Features selection method can increase the accuracy of
image analysis using ANN model to predict nitrogen content of spinach leaves. The combination of ANN
with Ant Colony Optimization resulted in the most optimal modelling with mean square error validation
value of 0.0000083 and the R2 testing-set data = 0.99 by using 10 CCM textural features as the input of
ANN. The computer vision method using ANN model which has been developed can be used as
non-invasive sensing device to predict nitrogen content of spinach and for guiding farmers in the accurate
application of their nitrogen fertilization strategies using low cost computer imaging technology.
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
Evaluation of image segmentation and filtering with ann in the papaya leafijcsit
Precision agriculture is area with lack of cheap technology. The refinement of the production system brings
large advantages to the producer and the use of images makes the monitoring a more cheap methodology.
Macronutrients monitoring can to determine the health and vulnerability of the plant in specific stages. In
this paper is analyzed the method based on computational intelligence to work with image segmentation in
the identification of symptoms of plant nutrient deficiency. Artificial neural networks are evaluated for
image segmentation and filtering, several variations of parameters and insertion impulsive noise were
evaluated too. Satisfactory results are achieved with artificial neural for segmentation same with high
noise levels.
Entomology has been deeply rooted in various cultures since prehistoric times for the purpose of agriculture. Nowadays, many scientists are interested in the field of biodiversity in order to maintain the diversity of species within our ecosystem. Out of 1.3 million known species on this earth, insects account
for more than two thirds of these known species. Since 400 million years ago, there have been various kinds of interactions between humans and insects. There have been several attempts to create a method to perform insect identification accurately. Great knowledge and experience on entomology are required for accurate insect identification. Automation of insect identification is required because there is a shortage of
skilled entomologists. This paper provides a review of the past literature in vision-based insect recognition and classifications. Over the past decades, automatic insect recognition and classification has been given extra attention especially in term of crop pest and disease control. This paper details advances in insect recognition, discussing representative works from different types of method and classifiers algorithm. Among the method used in the previous research includes color histogram, edge detection and feature extraction (SIFT vector). We provides discussion on the state-of-the-art and provides perspective on future research direction in insect recognition and classification problem.
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.
Entomology has been deeply rooted in various cultures since prehistoric times for the purpose of
agriculture. Nowadays, many scientists are interested in the field of biodiversity in order to maintain the
diversity of species within our ecosystem. Out of 1.3 million known species on this earth, insects account
for more than two thirds of these known species. Since 400 million years ago, there have been various
kinds of interactions between humans and insects. There have been several attempts to create a method to
perform insect identification accurately. Great knowledge and experience on entomology are required for
accurateinsect identification. Automation of insect identification is required because there is a shortage of
skilled entomologists. This paper provides a review of the past literature in vision-based insect recognition
and classifications. Over the past decades, automatic insect recognition and classification has been given
extra attention especially in term of crop pest and disease control. This paper details advances in insect
recognition, discussing representative works from different types of method and classifiers
algorithm.Among the method used in the previous research includes color histogram, edge detection and
feature extraction (SIFT vector). We provides discussion on the state-of-the-art and provides perspective
on future research direction in insect recognition and classification problem.
Analysis of Fungus in Plant Using Image Processing Techniquespaperpublications3
Abstract: The present work proposes a methodology for analysis of fungus in plant, using image processing techniques. The fungus kills the young seedling; it spread by air and can also infect plant. Therefore it is very important to monitor the leaf at regular intervals so as to keep track on quality of growth of plant. For analysis of fungus is focused on technique using MATLAB 7.0. The image are captured by digital camera mobile and processed using image growing, then the part of the leaf spot has been used for the classification purpose of the trait and test. The acquired image are in jpeg format and are converted to gray scale image. The gray scale images are enhanced and make noise free. The Ostu algorithm is applied to get threshold image. The pixel neighborhood is applied to enhance the pixel of leaf to show clearly the fungus area. Clustering is applied to get infected part of the leaf. RGB image is then segmented for analysis of fungus in plant. Comparative analyses of Image Edge Detection techniques are presented. It has been observed that the Canny Edge Detection algorithm is computationally more expensive compared to Sobel Edge Detection technique. The fungus infected area is 24.5951%.
Soil Characterization and Classification: A Hybrid Approach of Computer Visio...IJECEIAES
This paper presents soil characterization and classification using computer vision & sensor network approach. Gravity Analog Soil Moisture Sensor with arduino-uno and image processing is considered for classification and characterization of soils. For the data sets, Amhara regions and Addis Ababa city of Ethiopia are considered for this study. In this research paper the total of 6 group of soil and each having 90 images are used. That is, form these 540 images were captured. Once the dataset is collected, pre-processing and noise filtering steps are performed to achieve the goal of the study through MATLAB, 2013. Classification and characterization is performed through BPNN (Back-propagation neural network), the neural network consists of 7 inputs feature vectors and 6 neurons in its output layer to classify soils. 89.7% accuracy is achieved when back-propagation neural network (BPNN) is used.
IoT Based Intelligent Management System for Agricultural Applicationijtsrd
The growth of technology in any sector is not there in agriculture and this is a problem for India. The government has struggled to do anything for the farming sector which is in an exceptionally deplorable state. The pause in decision making also has led to Indias high rate of unemployment owing to the quality of the economy. The applications in well developed countries involve robotics, aircraft, and artificial intelligence, but they can raise the cost of running and sustain. Currently, operating drones such as these is difficult. In India, only a few farmers can afford to employ such high tech machinery to farm owing to financial constraints. The project is aimed at developing an affordable quad copter for farmers to use on their crops, with the goal of growing their output. We are developing core a framework with support of Raspberry Pi and OpenCV that can help predict crops yield with the help of inputs from numerous different sensor packages. Venkat. P. Patil | Umakant B. Gohatre | Anushka Mhaskar | Akash Jadhav | Prajwal Shetty | Yash Jadhav "IoT Based Intelligent Management System for Agricultural Application" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38578.pdf Paper Url: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/38578/iot-based-intelligent-management-system-for-agricultural-application/venkat-p-patil
Agricultural research has strengthened the optimized
economical profit, internationally and is very vast and
important field to gain more benefits.
In future agriculture is the only scope for all the people. But
today number of people having land, but they don’t know how
to yield the crops.
So many of people are doing useless agriculture by
cultivating the crop on improper soil. To implement the
application to identify the types of soil,water source of that
land whether that land is based on rain or bore water. And
suggest what of crop is suitable for that soil. So through this
application provide application for the people to know about
the agriculture. There is no any application to know about the
cultivation. However, it can be enhanced by the use of different
technological resources, tool, and procedures. Predict the type
of crop which one is suitable for that particular soil, weather
condition, temperature and so on. So for, using machine
learning with the set of data set are identified the crop for the
corresponding soil.
Predictive fertilization models for potato crops using machine learning techn...IJECEIAES
Given the influence of several factors, including weather, soils, land management, genotypes, and the severity of pests and diseases, prescribing adequate nutrient levels is difficult. A potato’s performance can be predicted using machine learning techniques in cases when there is enough data. This study aimed to develop a highly precise model for determining the optimal levels of nitrogen, phosphorus, and potassium required to achieve both high-quality and high-yield potato crops, taking into account the impact of various environmental factors such as weather, soil type, and land management practices. We used 900 field experiments from Kaggle as part of a data set. We developed, evaluated, and compared prediction models of k-nearest neighbor (KNN), linear support vector machine (SVM), naive Bayes (NB) classifier, decision tree (DT) regressor, random forest (RF) regressor, and eXtreme gradient boosting (XGBoost). We used measures such as mean average error (MAE), mean squared error (MSE), R-Squared (RS), and R 2 Root mean squared error (RMSE) to describe the model’s mistakes and prediction capacity. It turned out that the XGBoost model has the greatest R 2 , MSE and MAE values. Overall, the XGBoost model outperforms the other machine learning models. In the end, we suggested a hardware implementation to help farmers in the field.
Implemented various classification models using R language to identify which one performs best for prediction of soil fertility and which properties are important in defining the fertility of soil.
Hybrid features and ensembles of convolution neural networks for weed detectionIJECEIAES
Weeds compete with plants for sunlight, nutrients and water. Conventional weed management involves spraying of herbicides to the entire crop which increases the cost of cultivation, decreasing the quality of the crop, in turn affecting human health. Precise automatic spraying of the herbicides on weeds has been in research and use. This paper discusses automatic weed detection using hybrid features which is generated by extracting the deep features from convolutional neural network (CNN) along with the texture and color features. The color and texture features are extracted by color moments, gray level co-occurrence matrix (GLCM) and Gabor wavelet transform. The proposed hybrid features are classified by Bayesian optimized support vector machine (BO-SVM) classifier. The experimental results read that the proposed hybrid features yield a maximum accuracy of 95.83%, higher precision, sensitivity and F-score. A performance analysis of the proposed hybrid features with BO-SVM classifier in terms of the evaluation parameters is made using the images from crop weed field image dataset.
Analysis of crop yield prediction using data mining techniqueseSAT Journals
Abstract
Agrarian sector in India is facing rigorous problem to maximize the crop productivity. More than 60 percent of the crop still depends on monsoon rainfall. Recent developments in Information Technology for agriculture field has become an interesting research area to predict the crop yield. The problem of yield prediction is a major problem that remains to be solved based on available data. Data Mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. This paper presents a brief analysis of crop yield prediction using Multiple Linear Regression (MLR) technique and Density based clustering technique for the selected region i.e. East Godavari district of Andhra Pradesh in India.
Keywords: Agrarian Sector, Crop Production, Data Mining, Density based clustering, Information Technology, Multiple Linear Regression, Yield Prediction.
Evaluation of image segmentation and filtering with ann in the papaya leafijcsit
Precision agriculture is area with lack of cheap technology. The refinement of the production system brings
large advantages to the producer and the use of images makes the monitoring a more cheap methodology.
Macronutrients monitoring can to determine the health and vulnerability of the plant in specific stages. In
this paper is analyzed the method based on computational intelligence to work with image segmentation in
the identification of symptoms of plant nutrient deficiency. Artificial neural networks are evaluated for
image segmentation and filtering, several variations of parameters and insertion impulsive noise were
evaluated too. Satisfactory results are achieved with artificial neural for segmentation same with high
noise levels.
Entomology has been deeply rooted in various cultures since prehistoric times for the purpose of agriculture. Nowadays, many scientists are interested in the field of biodiversity in order to maintain the diversity of species within our ecosystem. Out of 1.3 million known species on this earth, insects account
for more than two thirds of these known species. Since 400 million years ago, there have been various kinds of interactions between humans and insects. There have been several attempts to create a method to perform insect identification accurately. Great knowledge and experience on entomology are required for accurate insect identification. Automation of insect identification is required because there is a shortage of
skilled entomologists. This paper provides a review of the past literature in vision-based insect recognition and classifications. Over the past decades, automatic insect recognition and classification has been given extra attention especially in term of crop pest and disease control. This paper details advances in insect recognition, discussing representative works from different types of method and classifiers algorithm. Among the method used in the previous research includes color histogram, edge detection and feature extraction (SIFT vector). We provides discussion on the state-of-the-art and provides perspective on future research direction in insect recognition and classification problem.
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.
Entomology has been deeply rooted in various cultures since prehistoric times for the purpose of
agriculture. Nowadays, many scientists are interested in the field of biodiversity in order to maintain the
diversity of species within our ecosystem. Out of 1.3 million known species on this earth, insects account
for more than two thirds of these known species. Since 400 million years ago, there have been various
kinds of interactions between humans and insects. There have been several attempts to create a method to
perform insect identification accurately. Great knowledge and experience on entomology are required for
accurateinsect identification. Automation of insect identification is required because there is a shortage of
skilled entomologists. This paper provides a review of the past literature in vision-based insect recognition
and classifications. Over the past decades, automatic insect recognition and classification has been given
extra attention especially in term of crop pest and disease control. This paper details advances in insect
recognition, discussing representative works from different types of method and classifiers
algorithm.Among the method used in the previous research includes color histogram, edge detection and
feature extraction (SIFT vector). We provides discussion on the state-of-the-art and provides perspective
on future research direction in insect recognition and classification problem.
Analysis of Fungus in Plant Using Image Processing Techniquespaperpublications3
Abstract: The present work proposes a methodology for analysis of fungus in plant, using image processing techniques. The fungus kills the young seedling; it spread by air and can also infect plant. Therefore it is very important to monitor the leaf at regular intervals so as to keep track on quality of growth of plant. For analysis of fungus is focused on technique using MATLAB 7.0. The image are captured by digital camera mobile and processed using image growing, then the part of the leaf spot has been used for the classification purpose of the trait and test. The acquired image are in jpeg format and are converted to gray scale image. The gray scale images are enhanced and make noise free. The Ostu algorithm is applied to get threshold image. The pixel neighborhood is applied to enhance the pixel of leaf to show clearly the fungus area. Clustering is applied to get infected part of the leaf. RGB image is then segmented for analysis of fungus in plant. Comparative analyses of Image Edge Detection techniques are presented. It has been observed that the Canny Edge Detection algorithm is computationally more expensive compared to Sobel Edge Detection technique. The fungus infected area is 24.5951%.
A developed algorithm for automating the multiple bands multiple endmember se...
Similar to The effects of multiple layers feed-forward neural network transfer function in digital based ethiopian soil classification and moisture prediction
Soil Characterization and Classification: A Hybrid Approach of Computer Visio...IJECEIAES
This paper presents soil characterization and classification using computer vision & sensor network approach. Gravity Analog Soil Moisture Sensor with arduino-uno and image processing is considered for classification and characterization of soils. For the data sets, Amhara regions and Addis Ababa city of Ethiopia are considered for this study. In this research paper the total of 6 group of soil and each having 90 images are used. That is, form these 540 images were captured. Once the dataset is collected, pre-processing and noise filtering steps are performed to achieve the goal of the study through MATLAB, 2013. Classification and characterization is performed through BPNN (Back-propagation neural network), the neural network consists of 7 inputs feature vectors and 6 neurons in its output layer to classify soils. 89.7% accuracy is achieved when back-propagation neural network (BPNN) is used.
IoT Based Intelligent Management System for Agricultural Applicationijtsrd
The growth of technology in any sector is not there in agriculture and this is a problem for India. The government has struggled to do anything for the farming sector which is in an exceptionally deplorable state. The pause in decision making also has led to Indias high rate of unemployment owing to the quality of the economy. The applications in well developed countries involve robotics, aircraft, and artificial intelligence, but they can raise the cost of running and sustain. Currently, operating drones such as these is difficult. In India, only a few farmers can afford to employ such high tech machinery to farm owing to financial constraints. The project is aimed at developing an affordable quad copter for farmers to use on their crops, with the goal of growing their output. We are developing core a framework with support of Raspberry Pi and OpenCV that can help predict crops yield with the help of inputs from numerous different sensor packages. Venkat. P. Patil | Umakant B. Gohatre | Anushka Mhaskar | Akash Jadhav | Prajwal Shetty | Yash Jadhav "IoT Based Intelligent Management System for Agricultural Application" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38578.pdf Paper Url: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/38578/iot-based-intelligent-management-system-for-agricultural-application/venkat-p-patil
Agricultural research has strengthened the optimized
economical profit, internationally and is very vast and
important field to gain more benefits.
In future agriculture is the only scope for all the people. But
today number of people having land, but they don’t know how
to yield the crops.
So many of people are doing useless agriculture by
cultivating the crop on improper soil. To implement the
application to identify the types of soil,water source of that
land whether that land is based on rain or bore water. And
suggest what of crop is suitable for that soil. So through this
application provide application for the people to know about
the agriculture. There is no any application to know about the
cultivation. However, it can be enhanced by the use of different
technological resources, tool, and procedures. Predict the type
of crop which one is suitable for that particular soil, weather
condition, temperature and so on. So for, using machine
learning with the set of data set are identified the crop for the
corresponding soil.
Predictive fertilization models for potato crops using machine learning techn...IJECEIAES
Given the influence of several factors, including weather, soils, land management, genotypes, and the severity of pests and diseases, prescribing adequate nutrient levels is difficult. A potato’s performance can be predicted using machine learning techniques in cases when there is enough data. This study aimed to develop a highly precise model for determining the optimal levels of nitrogen, phosphorus, and potassium required to achieve both high-quality and high-yield potato crops, taking into account the impact of various environmental factors such as weather, soil type, and land management practices. We used 900 field experiments from Kaggle as part of a data set. We developed, evaluated, and compared prediction models of k-nearest neighbor (KNN), linear support vector machine (SVM), naive Bayes (NB) classifier, decision tree (DT) regressor, random forest (RF) regressor, and eXtreme gradient boosting (XGBoost). We used measures such as mean average error (MAE), mean squared error (MSE), R-Squared (RS), and R 2 Root mean squared error (RMSE) to describe the model’s mistakes and prediction capacity. It turned out that the XGBoost model has the greatest R 2 , MSE and MAE values. Overall, the XGBoost model outperforms the other machine learning models. In the end, we suggested a hardware implementation to help farmers in the field.
Implemented various classification models using R language to identify which one performs best for prediction of soil fertility and which properties are important in defining the fertility of soil.
Hybrid features and ensembles of convolution neural networks for weed detectionIJECEIAES
Weeds compete with plants for sunlight, nutrients and water. Conventional weed management involves spraying of herbicides to the entire crop which increases the cost of cultivation, decreasing the quality of the crop, in turn affecting human health. Precise automatic spraying of the herbicides on weeds has been in research and use. This paper discusses automatic weed detection using hybrid features which is generated by extracting the deep features from convolutional neural network (CNN) along with the texture and color features. The color and texture features are extracted by color moments, gray level co-occurrence matrix (GLCM) and Gabor wavelet transform. The proposed hybrid features are classified by Bayesian optimized support vector machine (BO-SVM) classifier. The experimental results read that the proposed hybrid features yield a maximum accuracy of 95.83%, higher precision, sensitivity and F-score. A performance analysis of the proposed hybrid features with BO-SVM classifier in terms of the evaluation parameters is made using the images from crop weed field image dataset.
Analysis of crop yield prediction using data mining techniqueseSAT Journals
Abstract
Agrarian sector in India is facing rigorous problem to maximize the crop productivity. More than 60 percent of the crop still depends on monsoon rainfall. Recent developments in Information Technology for agriculture field has become an interesting research area to predict the crop yield. The problem of yield prediction is a major problem that remains to be solved based on available data. Data Mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. This paper presents a brief analysis of crop yield prediction using Multiple Linear Regression (MLR) technique and Density based clustering technique for the selected region i.e. East Godavari district of Andhra Pradesh in India.
Keywords: Agrarian Sector, Crop Production, Data Mining, Density based clustering, Information Technology, Multiple Linear Regression, Yield Prediction.
Crop yield prediction using ridge regression.pdfssuserb22f5a
Crop yield prediction using deep neural networks with data mining concepts by applying multi model ensembles using ridge regression to increase accuracy, precision, recall,and f measure. Combining neural networks with regression increase high satisfactory crop yield prediction.the support vector regression is slow convergence , stuck in local minima. But ridge regression analyse multicollinearity in multiple regression.
Similar to The effects of multiple layers feed-forward neural network transfer function in digital based ethiopian soil classification and moisture prediction (20)
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
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About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
2. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 4, August 2020 : 4073 - 4079
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vision. In the areas of machine learning, human operator efforts are being replaced with automated systems,
this is due to, as human operations are usually inconsistent and non efficient. Automated systems in most
cases are faster and more precise than the traditional human efforts. However, there are some basic
infrastructures that must necessarily be in place in automation [3].
In [4], due to sensor nodes have decreased in size and are much cheaper, it can be appliecd in
the emergence of many new civilian applications from environment monitoring to vehicular and body sensor
networks. Inspites the authors have used sensor for monitoring environments and achieved promising result.
They didn’t shown the effects on transfer function in related with soil classiofication and and moisture
prediction. In addition, the authors stated in [5], sensor-based technologies can be applied in construction
safty management. But there is still a gap of model to identify a better transfer function for classification of
soil and moisture predeiction.
A study conducted by [6] to estimate soil crack for moisture analysis, from the experiment 72.7% is
archived. In the study, they have used texture and color as a feature vector and support vector machine to
estimate crack. Even though, they achieved 72.7%, kerenel function of SVM had not been experimented to
increase the performance. According to [7], they stated that to avoid agricultural product both quality and
quantity loss, soil characteristics identification and classification is crutial task.
Soil characterization and classification using computer vision & sensor network approach has been
studied. In their study the authors have used Gravity Analog Soil Moisture Sensor with arduino-uno and
image processing as techniques to achieve the objective [8]. But, there is a gap to identify suitable learning
function of neural network for better convergence and accuracy.
In [9], the authors have used computer vision for limestone rock-type classification using
probabilistic neural network. In this paper, a computer vision based rock type classification system is
proposed without human intervention using the probabilistic neural network (PNN). In this research paper
the authors are used the color histogram features as an input. In the paper the color image histogram based
features includes weighted mean, skewness and kurtosis features are extracted for all three color space red,
green, and blue. In this paper, a total nine features are used as input for the PNN classification model.
Then they found out the error rate for identification is below 6%. In [10], the authors have presented soil
image segmentation and texture analysis using computer vision approach. The author proposed joint image
segmentation methods for soil images and feature measurements.
Soil texture classification algorithm using RGB characteristics of soil images has been done.
The authors found that soil texture has traditionally been determined in the laboratory using pipette and
hydrometer methods that require a considerable amount of time, labor, and expense. In the paper, soil texture
classification using RGB histograms was investigated to achieve the goal of the study. In this paper, when
soils were classified using USDA soil texture classification, the laboratory method and image processing
method produced the same results for 48% of the samples [11]. In [12], the authors have shown that detection
of soil pore structure using an image segmentation approach. In this study, a density based clustering method
on tomography sections of soil is considered.
In [13], the authors have studied determination of Soil pH by using Digital Image Processing
Technique. In Agriculture sector the parameters like quantity and quality of product are the important
measures from the farmers’ point of view. The soil is recognized as one of the most valuable natural resource
whose soil pH property used to describe the degree of acidity or basicity which affects nutrient availability
and ultimately plant growth.
Image texture analysis and neural networks for characterization of uniform soils are studied.
Supervised back-propagation neural network is used for this study. The authors have tested neural network
with considerable accuracy [14]. Here, there is a gap to tune the learning function of neural network for
analyzing the performance of the model.
In [15], the authors presented “Testing of Agriculture Soil by Digital Image Processing”. This paper
helps to determine the amount of fertilizer and pH of soil that must be applied. 80 soil samples their pH value
tested in government soil testing Lab are considered in this study. In their work, when the software is tested
the software gives 60-70% accuracy.
Wavelet analysis of soil reflectance for the characterization of soil properties has been studied.
The authors have used Wavelet analysis, hyper spectral near-infrared (NIR) and mid-infrared (MIR)
reflectance spectra of soil material to characterize the given soil [16]. In [17], the authors have used a novel
method for 2-D Agricultural soil roughness characterization based on a laser scanning technique” presented
laser profiler the determination of agricultural soil roughness. When tested with the RMS height S and
correlation length L in 1 m x 0,3 m parcels with a 20-30% error in heights and 1- 10% error in horizontal lengths.
The application of image processing and analysis in plant root systems in soil has been done using
imageJ plat form. ImageJ is a java version that is used to perform image analysis. The authors have used
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x-ray tomography 3D images [18]. In [19], they studied detection of soil liquefaction areas in case of Kantou
region of Japan. In this paper, multi-temporal PALSAR coherence data is considered.
According to [20], moisture content in soil is one of the main component which plays important role
in yield of crops. In this paper the authors focused on software development for soil moisture assessment.
The main objective of the authors was to turn the manual process to a software application using image
processing technique. Image of the soil with different moisture content are collected and preprocessed to
remove the noise of source image. The authors have used color and texture feature vector as an input in soil
moisture assessment software.
In [21], the authors stated that the farmers are suffering from the lack of rains and scarcity of water.
In this paper, the main objective was to provide an automatic irrigation system thereby saving time, money
and power of the farmer. In this work moisture sensors are considered anad installed on the field. Whenever
there is a change in water content of soil these sensors sense the change gives an interrupt signal to
the micro-controller For capturing the images, the phone camera is used and after processing the captured
image the PH value of the soil is determined and accordingly crops or plants are suggested that can be grown
in that field.
In [22], the authors focused on an urban road materials vision system using narrow band near
infrared imaging. This paper proposed imaging indexes evaluation from experiment results to identify those
urban road materials. The proposed multi-spectral imaging indexes were able to show the potential to classify
the selected urban road materials, another approach may need to clearly distinguish between concrete
and aggregates.
Data mining approach for soil moisture prediction has been studied. In this paper, the authors have
used five different algorithms i.e. KNN, SVM, Logistic regression neural network and rule induction.
Form the experiment the authors have found that KNN has a better performance than SVM and
neural network [22].
In [23], they present an artificial neural network framework for predicting earth block performance
prediction. In their study, they stated that earth block performance depends heavily on the qualities of soil
used and it is important to identify the qualities of soil. But in this paper they have not seen soil classification
and moisture level prediction.
In [24], the authors have pointed that the application of data mining towards to learning and
classifying agricultural soil types. In their study, they have used naïve bayes, J48 and JRip techniques for
classification of soil and predicting of PH values. Finally, form the experiment they have found that JRip has
better performance than the other data mining techniques.
The combined approach of SOM and KNN for classifying agricultural soil has been studied.
In the study, the authors have used color and texture feature vectors. From the experiment they achieved
79.8% result when SOM and KNN are used [25].
In [26], in this study, they have used Artificial neural network for predicting soil types. During their
work a database consists of 120 soil samples which were collected from Shahrekord, Chahar Mahal and
Bakhtiari province. Finally they have tested their model using neural network. The neural network used in
this study has an input layer, an output layer and a hidden layer. The input layer consists of 7 variables and
the output layer has only one parameter for predicting soil type and the hidden layer has 15 neurons.
The number of nodes in the hidden layer was determined by trial and error.
In [27], they conducted a study related with moisture and organic matter prediction based on NIR
reflectance sensor. In the study, the authors used SOM (self organizing map) for learning the pattern and
prediction. From the experiment they have found that Standard errors of prediction for organic matter and
soil moisture were 0.62 and 5.31%, respectively.
In [28], they studied the application of machine learning techniques in soil classification.
They consider only three type of soil (sand, clay, peat) and for machine learning side the authors have
considered Artificial neural network (ANN), Decision tree (DT) and support vector machine (SVM) and
WEKA software is used for classifying to their Corresponding class. From the experiment the authors have
shown that ANN is better than DT and SVM. This study is limited in three classes and still there is a gap to
show the moisture level.
Prediction of rainfall using image processing” the authors have used digital cloud images to predict
rainfall. From this paper they stated that it is better to predict rainfall from digital cloud images rather than
satellite images based on the cost factors and security issues. They have used wavelet and Cloud Mask
Algorithm to capture the image for clustering K- means is used [29].
In [30], they studied about soil classification and quantitatively predict the temperature and moisture
of the soil based on satellite image. In this research paper the authors have used 3 types of soils from
the satellite. For classification and moisture prediction BPNN and LM were used. From the experiment they
concluded that neural networks can be used as a paradigm in soil classification as well as in predicting
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the quantity of soil moisture and temperature accurately, using remotely sensed microwave data, and thus
helps achieve a proper crop management.
Detection of nutritional deficiencies in plants has been researched in [31]. In the paper they have
used color and shape as feature vector and as a classifier KNN, SVM and deep learning has been considered.
In [32], the authors have proposed soil characterization and classification using computer vision & sensor
network approach. BPNN has been used as a classifier and from the experiment they have got 89.7%.
The application of Back Propagation Neural Network to wards to breast cancer and lung cancer has been
studied in [33]. In their work to enhance the accuracy of the classifer, genetic algorithms was used and
a better result has been achieved.
In the area of machine learning performance analysis is the major task in order to get a better
performance both in training and testing model. In addition, performance analysis of machine learning
techniques helps to determine how the machine is performing and whether any improvments needed to made.
Artificial neural network can be applied in different areas of sector but, their speed of learning and network
complexity may differ from transfer function. The neural networks may learn patterns, but the inner ability to
learn quickly the given pattern requires flexible transfer functions that are suitable for the problem to be
solved. Different techniques have been proposed related with soil classification and moisture
characterization. However, according to the litratures and to the best of our knowledge, no research has been
conducted related to the performance analysis of transfer function in Ethiopian soil classification and
moisture level prediction. Therefore, the main goal of this study is to find the suitable transfer function for
Ethiopian Soil classification and moisture level prediction. In this study, rectified linear units (ReLU),
sigmoid, Gaussian and hyperbolic tangent (tanh) transfer functions are tested to achieve the objective of this research.
2. MODEL DESIGN
As shown in Figure 1 soil classification and moisture level prediction model consists of three basic
parts: image aquizition, Feature extraction and performance evaluation.
Figure 1. Model design (adopted from [8])
2.1. Image acquisition
In computer vision, image acquisition is the first part. In this study we used smart phone camera to
the images in the form of JPEG (Joint Photographers Expert Groups) file format. In addition, 256 by 256
images size is considered for this study. The total of 6 group of soil and each having 90 images are
considered for this study. That is, form these 540 images were captured at different places of Amhara regions
of Ethiopia.
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2.2. Image preprocessing
Image processing is a technique that manipulates images in various ways to enhance image quality.
In prreprocessing stage takes raw image as input and produce enhanced image as an output. In this study,
there are different preprocessing algorithms used for image resizing, equalization, and noise removal.
Histogram equalization, resising and noise filtering steps are performed so as to get the enhanced image.
2.3. Eature extraction and performance evaluation
Form enhanced image color features are extracted and from gray scale image texture and brisk local
feature descriptor feature vectors are extracted. Once the representing features are extracted modeling of feed
forward neural network had been done. In network modeling ReLU, sigmoid, tanh and gaussian transfer
function had been used as a parameter for analyzing the performance. Finally, the performance of
feed-forward neural network are tested. As shown Figure 2 the images are collected in day and night time to
enhance and get different colors and finally they were resized in 256 by 256 pixel.
Figure 2. Sample images of soil classification
3. RESULTS AND DISCUSSIONS
An artificial neural network consists of three layers: the input layer (I), the hidden layer (H), and
the output layer (O). The input layer relies on as many neurons as input features. Input neurons just propagate
input features to the hidden layer and the hidden layers process the input data. If there are errors while
processing or learning patterns the errors are propagated to the hidden layers and computed again. Finally if
the network is converged, the output is generated.
As indicated in Figure 3, the network needs color, texture, and brisk local feature as an input vector
of the combined feature vectors and 4 hidden layes. In the hidden layers 26 neurons are used. The hidden
layer has 26 neurons. This number was picked by trial and error methods, if the network has trouble of
learning capabilities, and then neurons can be added to this layer. There is a significant change when we
increase the number of hidden layers neurons until 21, 24 and 26 but there is no change when the number of
hidden layer neurons increases above 26. In the output layers 9 neurons are considered to classify soils as
Clay, Clay Sand, Silt Sand, Peat, Clay peat and moisture prediction as wet, dry and water. The next
experiment is finding suitable transfer function for FFNN.
To achieve the goal of this study, the experimental scenario is conducted by considering the four
transfer function of FFNN. In sigmoid transfer function, it maps the input to a value between 0 and 1.
Besides, the sigmoid’s natural threshold is 0.5, meaning that any input that maps to a value above 0.5 will be
considered as 1. The training time to converge the network as well as the accuracy while considering sigmoid
function is lower than the other due to the range of transfer function. Since the range to map the input to is
between -1 and 1 a better result has been achieve when tanh function is considered. Here, as shown in
Figure 4, both the confergence rate and accuracy are better that sigmoid. In ReLU, there is a positive bias in
the network for the layers in FFNN, as the mean activation is larger than zero. From the experiment we
noticed that ReLU has better convergence and accuracy than sigmoid and tanh transfer function. In Gaussian
transfer function the output is interpreted based on the degree of membership which contains a continuous
degree of membership. The main reason that Gaussian transfer function has a better result is, it contains
the continuous value for the 6 types of soil. The degree of membership of soil type 0 (clay soil) is between 0
and 1; for soil type 1(clay sand) the degree of membership is between 1 and 2. When you see this the degree
of membership is continuous and contains wider range. Finally from the experiment 97.4 % accuracy is
achieved. But, it has lower convergence rate than ReLU.
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Figure 3. FFNN
Figure 4. Result
4. CONCLUSION
The aim of this research paper is to identify suitable transfer function for classification and moisture
level prediction using FFNN. In this paper, papametrs of FFNN are tested and the accuracy of the system is
presented and the results of FFNN were discussed. The work can also be seen in depth and researched to
analyze the other learninig function and using different machine learning techniques like deep learning in
connection with soil in agriculture.
ACKNOWLEDGEMENTS
We gratefully acknowledge Bahir Dar University, Bahir Dar institute of Technology.
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