Travis Kunnen, Gan Moodley, Deborah Roberston-Andersson. Presented at the ninth Scientific Symposium of the Western Indian Ocean Marine Science Association (WIOMSA) 2015
This document presents a study on using color texture feature analysis to detect surface defects on pomegranates. The researchers developed a method involving cropping images of pomegranates, converting them to HSI color space, generating SGDM matrices to extract 18 texture features for each image, and using support vector machines (SVM) classification to identify the best features for detecting infections. The optimal features identified were cluster shade, product moment, and mean intensity, achieving classification accuracy of 99.88%, 99.88%, and 99.81% respectively.
This document summarizes an approach for automatically detecting plant diseases from leaf images. It involves preprocessing the images, segmenting the leaves from the background, extracting disease regions, calculating the percent infection, and grading the disease using fuzzy logic. The goal is to develop a system that can help monitor large fields and provide timely treatment advice to address diseases. Key steps include image filtering, color segmentation to isolate disease spots, calculating total leaf and disease areas to determine percent infection, and using these metrics as inputs to a fuzzy inference system for grading. Automatic detection could help address issues like loss of crops and famines caused by periodic disease outbreaks.
Plant Leaf Disease Analysis using Image Processing Technique with Modified SV...Tarun Kumar
In this computing era, image processing has
spread its wings in human life upto the extent that image
has become an integral part of their life. There are various
applications of image processing in the field of commerce,
engineering, graphic design, journalism, architecture and
historical research. In this research work, Image
processing is considered for the analysis of plant leaf
diseases. Plant leaf diseases can be detected based on the
disease symptoms. Here, dataset of disease affected leaves
is considered for experimentation. This dataset contains
the plant leaves suffered from the
AlternariaAlternata,Cercospora Leaf Spot, Anthracnose
andBacterial Blight along with some healthy leaf images.
For this analysis, an autonomous approach of modified
SVM-CS is introduces. Here, concept of cuckoo search is
considered to optimize the classification parameters. These
parameters further help to find more accurate solutions.
This autonomous approach also extracts the healthy
portion and disease affected leaf portion along with the
accuracy of results.
An Exploration on the Identification of Plant Leaf Diseases using Image Proce...Tarun Kumar
From the ancient years, humans and other
social species directly & indirectly dependent on Plants.
Plants play an enormous role in human life by providing
them food for living, wood for houses and other resources
to live life.So, human should take care of plants and
agricultural crops. But apart from the human, various
natural factors are there that are responsible for
destroying the growth of plants like unavailability of
accurate plant resources, deficiency of sunlight, weather
conditions, lack of expert knowledge for the accurate use
of pesticides. The major factor responsible for this
destruction of plant growth is diseases. Early detection
and accurate identification of diseases can control the
spread of infection.In the earlier days, it was not easy to
identify the plant diseases but with the advancements of
digital technology, it becomes easy to identify plant disease
with image processing techniques. In this paper, an
exploration is made on the exiting approaches of plant leaf
disease detection using image processing approach. Also a
discussion is made on the major disease types like fungal,
bacterial and viral diseases. Different authors have
presented the different approaches for the identification of
leaf diseases for the different plant types.
This document describes a plant disease identification system that uses image processing techniques. The system captures images of leaves using a digital camera, then performs feature extraction and classification using MATLAB. Features like color, texture, and intensity are extracted and used to classify leaves as healthy or diseased, and to identify specific diseases, using a support vector machine approach. The goal is to develop an automated system to help farmers and agronomists identify plant diseases faster and more accurately than current manual methods.
IRJET- Detection and Classification of Leaf DiseasesIRJET Journal
This document presents a method for detecting and classifying leaf diseases using image processing techniques. The method involves 4 main phases: 1) Image preprocessing including noise removal and color space transformation. 2) Image segmentation using k-means clustering to separate healthy and infected tissue. 3) Feature extraction of texture characteristics. 4) Classification of the disease using a support vector machine model. The results diagnose the disease name and percentage of leaf area infected to help farmers quickly identify and respond to plant diseases.
IRJET - Disease Detection in Plant using Machine LearningIRJET Journal
This document discusses using machine learning and image processing techniques to detect diseases in plants. The proposed system utilizes convolutional neural networks (CNNs) to classify plant images as either healthy or diseased based on features extracted from the images. The system architecture includes preprocessing the images, extracting color and texture features, running the features through a CNN model for classification training and testing, and outputting whether plants are normal or abnormal. The goal is to help farmers automatically detect plant diseases early on by analyzing images of plant leaves.
This document presents a study on using color texture feature analysis to detect surface defects on pomegranates. The researchers developed a method involving cropping images of pomegranates, converting them to HSI color space, generating SGDM matrices to extract 18 texture features for each image, and using support vector machines (SVM) classification to identify the best features for detecting infections. The optimal features identified were cluster shade, product moment, and mean intensity, achieving classification accuracy of 99.88%, 99.88%, and 99.81% respectively.
This document summarizes an approach for automatically detecting plant diseases from leaf images. It involves preprocessing the images, segmenting the leaves from the background, extracting disease regions, calculating the percent infection, and grading the disease using fuzzy logic. The goal is to develop a system that can help monitor large fields and provide timely treatment advice to address diseases. Key steps include image filtering, color segmentation to isolate disease spots, calculating total leaf and disease areas to determine percent infection, and using these metrics as inputs to a fuzzy inference system for grading. Automatic detection could help address issues like loss of crops and famines caused by periodic disease outbreaks.
Plant Leaf Disease Analysis using Image Processing Technique with Modified SV...Tarun Kumar
In this computing era, image processing has
spread its wings in human life upto the extent that image
has become an integral part of their life. There are various
applications of image processing in the field of commerce,
engineering, graphic design, journalism, architecture and
historical research. In this research work, Image
processing is considered for the analysis of plant leaf
diseases. Plant leaf diseases can be detected based on the
disease symptoms. Here, dataset of disease affected leaves
is considered for experimentation. This dataset contains
the plant leaves suffered from the
AlternariaAlternata,Cercospora Leaf Spot, Anthracnose
andBacterial Blight along with some healthy leaf images.
For this analysis, an autonomous approach of modified
SVM-CS is introduces. Here, concept of cuckoo search is
considered to optimize the classification parameters. These
parameters further help to find more accurate solutions.
This autonomous approach also extracts the healthy
portion and disease affected leaf portion along with the
accuracy of results.
An Exploration on the Identification of Plant Leaf Diseases using Image Proce...Tarun Kumar
From the ancient years, humans and other
social species directly & indirectly dependent on Plants.
Plants play an enormous role in human life by providing
them food for living, wood for houses and other resources
to live life.So, human should take care of plants and
agricultural crops. But apart from the human, various
natural factors are there that are responsible for
destroying the growth of plants like unavailability of
accurate plant resources, deficiency of sunlight, weather
conditions, lack of expert knowledge for the accurate use
of pesticides. The major factor responsible for this
destruction of plant growth is diseases. Early detection
and accurate identification of diseases can control the
spread of infection.In the earlier days, it was not easy to
identify the plant diseases but with the advancements of
digital technology, it becomes easy to identify plant disease
with image processing techniques. In this paper, an
exploration is made on the exiting approaches of plant leaf
disease detection using image processing approach. Also a
discussion is made on the major disease types like fungal,
bacterial and viral diseases. Different authors have
presented the different approaches for the identification of
leaf diseases for the different plant types.
This document describes a plant disease identification system that uses image processing techniques. The system captures images of leaves using a digital camera, then performs feature extraction and classification using MATLAB. Features like color, texture, and intensity are extracted and used to classify leaves as healthy or diseased, and to identify specific diseases, using a support vector machine approach. The goal is to develop an automated system to help farmers and agronomists identify plant diseases faster and more accurately than current manual methods.
IRJET- Detection and Classification of Leaf DiseasesIRJET Journal
This document presents a method for detecting and classifying leaf diseases using image processing techniques. The method involves 4 main phases: 1) Image preprocessing including noise removal and color space transformation. 2) Image segmentation using k-means clustering to separate healthy and infected tissue. 3) Feature extraction of texture characteristics. 4) Classification of the disease using a support vector machine model. The results diagnose the disease name and percentage of leaf area infected to help farmers quickly identify and respond to plant diseases.
IRJET - Disease Detection in Plant using Machine LearningIRJET Journal
This document discusses using machine learning and image processing techniques to detect diseases in plants. The proposed system utilizes convolutional neural networks (CNNs) to classify plant images as either healthy or diseased based on features extracted from the images. The system architecture includes preprocessing the images, extracting color and texture features, running the features through a CNN model for classification training and testing, and outputting whether plants are normal or abnormal. The goal is to help farmers automatically detect plant diseases early on by analyzing images of plant leaves.
Pest Control in Agricultural Plantations Using Image ProcessingIOSR Journals
Abstract: Monocropped plantations are unique to India and a handful of countries throughout the globe. Essentially, the FOREST approach of growing coffee along with in India has enabled the plantation to fight many outbreaks of pests and diseases. Mono cropped Plantations are under constant threat of pest and disease incidence because it favours the build up of pest population. To cope with these problems, an automatic pest detection algorithm using image processing techniques in MATLAB has been proposed in this paper. Image acquisition devices are used to acquire images of plantations at regular intervals. These images are then subjected to pre-processing, transformation and clustering.
Identification of Disease in Leaves using Genetic Algorithmijtsrd
Plant disease is an impairment of normal state of a plant that interrupts or modifies its vital functions. Many leaf diseases are caused by pathogens. Agriculture is the mains try of the Indian economy. Perception of human eye is not so much stronger so as to observe minute variation in the infected part of leaf. In this paper, we are providing software solution to automatically detect and classify plant leaf diseases. In this we are using image processing techniques to classify diseases and quickly diagnosis can be carried out as per disease. This approach will enhance productivity of crops. It includes image processing techniques starting from image acquisition, preprocessing, testing, and training. K. Beulah Suganthy ""Identification of Disease in Leaves using Genetic Algorithm"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22901.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/22901/identification-of-disease-in-leaves-using-genetic-algorithm/k-beulah-suganthy
This document describes a proposed system to detect plant diseases using machine learning and provide remedial measures. It will use a mobile app to classify plant leaf images using a TensorFlow Lite model trained with InceptionV3. The model will identify the disease and fetch details like treatment from a database to display to the user. This aims to make plant disease detection and treatment advice more easily accessible compared to existing computer-based systems.
IRJET- Plant Leaf Disease Detection using Image ProcessingIRJET Journal
This document discusses a technique for early detection of plant diseases through image processing. The technique involves preprocessing leaf images through color space conversion and enhancement. The region of interest (disease area) is segmented and features are extracted. A minimum distance classifier compares the features to a database of known plant diseases and identifies the disease. The methodology achieves over 90% accuracy in detecting diseases. The system could help farmers monitor crops efficiently and apply treatments early to reduce losses from diseases. Future work may involve integrating audio cues and recommending specific treatments to increase productivity and reduce costs and pollution.
A Novel Approach for Tomato Diseases Classification Based on Deep Convolution...Mohammad Shakirul islam
This document summarizes Mohammad Shakirul Islam's research paper on classifying tomato plant diseases using deep convolutional neural networks. The paper includes sections on motivation, literature review, proposed methodology, results discussion, and future work. The proposed methodology uses a dataset of 3000 images across 6 tomato disease classes. A convolutional neural network model with 5 convolution layers, 5 max pooling layers, and 2 dense layers is trained on 80% of the data and tested on the remaining 20% for classification performance. Results show the model achieved high training and validation accuracy for identifying different tomato diseases.
Segmentation of unhealthy region of plant leaf using image processing techniqueseSAT Journals
Abstract A segmentation technique is used to segment the diseased portion of a leaf. Based on the segmented area texture and color feature, disease can be identified by classification technique. There are many segmentation techniques such as Edge detection, Thresholding, K-Means clustering, Fuzzy C-Means clustering, Penalized Fuzzy C-Means, Unsupervised segmentation. Segmentation of diseased area of a plant leaf is the first step in disease detection and identification which plays crucial role in agriculture research. This paper provides different segmentation techniques that are used to segment diseased leaf of a plant. Keywords: Fuzzy C-Means, K-Means, Penalized FCM, Unsupervised Fuzzy Clustering
IRJET- Leaf Disease Detecting using CNN TechniqueIRJET Journal
This document describes a proposed system for detecting leaf diseases using convolutional neural network (CNN) techniques. The system uses image acquisition, pre-processing including cropping, resizing and filtering, segmentation using k-means clustering, feature extraction of color, texture and shape features, and classification using CNN. The system is tested on images of mango, pomegranate, guava and sapota leaves to automatically identify diseases and recommend appropriate control methods, providing an improvement over manual identification methods.
Classify Rice Disease Using Self-Optimizing Models and Edge Computing with A...Damian R. Mingle, MBA
This document summarizes research on classifying rice diseases using self-optimizing machine learning models and edge computing. The researchers used an automated machine learning platform to build models for identifying 3 classes of rice diseases from images. They extracted features from the images using a deep learning network and then used those features to train traditional machine learning models like ExtraTrees Classifier and Stochastic Gradient Descent. The goal was to develop an end-to-end solution for rice farmers to easily and accurately detect diseases in the field and receive treatment recommendations in real-time.
LEAF DISEASE DETECTION USING IMAGE PROCESSING AND SUPPORT VECTOR MACHINE (SVM)Journal For Research
in the study on leaf disease detection can be a helpful aspect in keeping an eye on huge area of fields of crops, but it’s important to detect the disease as early as possible. This paper gives a method to detect the disease caused to the leaf calculating the RGB and HSV values. Primarily the image is blurred in order reduce noise. Then the image is converted from RGB to HSV form, after this color thresholding is done. After thresholding foreground or background detection is performed. Background detection leads to feature extractions of the leaf. Then k-means algorithm is applied which can help to clot the clusters. The following system is a software based solution for detecting the disease with which the leaf is infected. In order to detect the disease some steps are to be followed using image processing and support vector machine. Improving the quality and production of agricultural products detection of the leaf disease can be useful.
Regularized Weighted Ensemble of Deep Classifiers ijcsa
Ensemble of classifiers increases the performance of the classification since the decision of many experts
are fused together to generate the resultant decision for prediction making. Deep learning is a classification algorithm where along with the basic learning technique, fine tuning learning is done for improved precision of learning. Deep classifier ensemble learning is having a good scope of research.Feature subset selection is another for creating individual classifiers to be fused for ensemble learning. All these ensemble techniques faces ill posed problem of overfitting. Regularized weighted ensemble of deep support vector machine performs the prediction analysis on the three UCI repository problems IRIS,Ionosphere and Seed data set, thereby increasing the generalization of the boundary plot between the
classes of the data set. The singular value decomposition reduced norm 2 regularization with the two level
deep classifier ensemble gives the best result in our experiments.
This document discusses methods for collecting and analyzing statistical data. It describes primary and secondary data sources, as well as direct, indirect, registration, and experimental data collection methods. The document also covers determining sample size using Slovin's formula and margin of error. Various sampling techniques are outlined, including probability methods like simple random sampling, systematic random sampling, stratified random sampling, cluster sampling, and multi-stage sampling. Non-probability sampling methods such as convenience sampling, quota sampling, purposive sampling, and snowball sampling are also discussed.
This document evaluates several supervised machine learning algorithms for classifying gene expression data from microarray experiments. It describes analyzing two gene expression datasets, the leukemia and DLBCL datasets, using k-nearest neighbors, naive Bayes, decision trees, and support vector machines with and without feature selection. The results show that support vector machines achieved the best performance overall, and that feature selection improved the accuracy of all the algorithms.
Plant disease detection and classification using deep learning JAVAID AHMAD WANI
This document describes a project on plant disease detection and classification using deep learning. The objectives are to automatically detect plant diseases as early as symptoms appear on leaves in order to increase crop productivity. Deep learning techniques like convolutional neural networks (CNNs) are implemented using libraries like TensorFlow and Keras. Two CNN models, VGG16 and VGG19, are compared for classifying diseases in a dataset of 38 classes and 87k images of 14 crop species. The system achieved over 95% accuracy on validation. Future work involves developing a mobile app and integrating disease recommendations to help farmers.
Wheat leaf disease detection using image processingIJLT EMAS
India is a agricultural based county where approx 70%
of population depend on agriculture. Now a days the plant
disease detection is very important because agriculture is the
backbone of the county like india. Farmer is not aware what type
of disease plant having and how to prevent them from these
diseases. To overcome from these we are going to develop a
technique in which we can able to detect plant disease using
image processing technique. This includes following steps: image
acquisition image pre-processing, feature extraction and at last
we apply a classifier know as neural network.
Classification of Apple diseases through machine learningMuqaddas Bin Tahir
This presentation describes a research work in which constitutional neural network is used for fruit’s classification and recognizing their diseases. CNN is the popular , advanced and powerful architecture of Neural Network. The method describe in this presentation perform better than other classification and recognition techniques on various datasets and it is not affected by illumination, translation and occlusion problems.
The classification of different types of tumors is of great importance in cancer diagnosis and its drug discovery. Cancer classification via gene expression data is known to contain the keys for solving the fundamental problems relating to the diagnosis of cancer. The recent advent of DNA microarray technology has made rapid monitoring of thousands of gene expressions possible. With this large quantity of gene expression data, scientists have started to explore the opportunities of classification of cancer using a gene expression dataset. To gain a profound understanding of the classification of cancer, it is necessary to take a closer look at the problem, the proposed solutions, and the related issues altogether. In this research thesis, I present a new way for Leukemia classification using the latest AI technique of Deep learning using Google TensorFlow on gene expression data.
Crop Leaf Disease Diagnosis using Convolutional Neural Networkijtsrd
This document summarizes a research paper that proposes a system for detecting crop leaf diseases using convolutional neural networks. The system can identify diseases in five major crops: corn, sugarcane, wheat, grape, and rice. It uses a MobileNet CNN model trained on a dataset of leaf images. Experiments show the system can accurately classify leaf diseases with 97.33% precision. The system automatically diagnoses leaf diseases and recommends pesticides, helping farmers detect and address issues early.
New Feature Selection Model Based Ensemble Rule Classifiers Method for Datase...ijaia
Feature selection and classification task are an essential process in dealing with large data sets that
comprise numerous number of input attributes. There are many search methods and classifiers that have
been used to find the optimal number of attributes. The aim of this paper is to find the optimal set of
attributes and improve the classification accuracy by adopting ensemble rule classifiers method. Research
process involves 2 phases; finding the optimal set of attributes and ensemble classifiers method for
classification task. Results are in terms of percentage of accuracy and number of selected attributes and
rules generated. 6 datasets were used for the experiment. The final output is an optimal set of attributes
with ensemble rule classifiers method. The experimental results conducted on public real dataset
demonstrate that the ensemble rule classifiers methods consistently show improve classification accuracy
on the selected dataset. Significant improvement in accuracy and optimal set of attribute selected is
achieved by adopting ensemble rule classifiers method.
This document describes research using deep learning to classify plant diseases from leaf images. The researchers:
1) Trained convolutional neural networks on a dataset of 54,306 images of plant leaves with 14 crop species and 26 diseases to classify the crop/disease in each image.
2) Achieved up to 99.35% accuracy when testing the best model on images not used for training, demonstrating the feasibility of this approach.
3) Found that models using transferred learning from ImageNet and color images performed best, but models still achieved over 85% accuracy on grayscale images, showing they learned the diseases rather than biases in the dataset.
IRJET - A Review on Identification and Disease Detection in Plants using Mach...IRJET Journal
This document reviews machine learning techniques for identifying and detecting plant diseases. It discusses how techniques like artificial neural networks, support vector machines, K-nearest neighbors classification and fuzzy c-means clustering have been applied to identify diseases in crops like rice, potatoes, cucumbers and grapes. The techniques analyze images of plant leaves to extract features and classify whether the plant has a disease or not. The document also outlines the common stages of disease identification using machine learning, which include preprocessing images, segmentation, feature extraction, classification and disease identification.
La Ley 84 de 1989 busca proteger a los animales en Colombia. Esta ley establece normas para el bienestar y cuidado apropiado de los animales, así como para prevenir y sancionar el maltrato. El objetivo es asegurar que todos los animales reciban un trato humano y digno.
The top news stories from New York - August 22, 2014
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Visit http://www.headlines.am to learn more.
Pest Control in Agricultural Plantations Using Image ProcessingIOSR Journals
Abstract: Monocropped plantations are unique to India and a handful of countries throughout the globe. Essentially, the FOREST approach of growing coffee along with in India has enabled the plantation to fight many outbreaks of pests and diseases. Mono cropped Plantations are under constant threat of pest and disease incidence because it favours the build up of pest population. To cope with these problems, an automatic pest detection algorithm using image processing techniques in MATLAB has been proposed in this paper. Image acquisition devices are used to acquire images of plantations at regular intervals. These images are then subjected to pre-processing, transformation and clustering.
Identification of Disease in Leaves using Genetic Algorithmijtsrd
Plant disease is an impairment of normal state of a plant that interrupts or modifies its vital functions. Many leaf diseases are caused by pathogens. Agriculture is the mains try of the Indian economy. Perception of human eye is not so much stronger so as to observe minute variation in the infected part of leaf. In this paper, we are providing software solution to automatically detect and classify plant leaf diseases. In this we are using image processing techniques to classify diseases and quickly diagnosis can be carried out as per disease. This approach will enhance productivity of crops. It includes image processing techniques starting from image acquisition, preprocessing, testing, and training. K. Beulah Suganthy ""Identification of Disease in Leaves using Genetic Algorithm"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22901.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/22901/identification-of-disease-in-leaves-using-genetic-algorithm/k-beulah-suganthy
This document describes a proposed system to detect plant diseases using machine learning and provide remedial measures. It will use a mobile app to classify plant leaf images using a TensorFlow Lite model trained with InceptionV3. The model will identify the disease and fetch details like treatment from a database to display to the user. This aims to make plant disease detection and treatment advice more easily accessible compared to existing computer-based systems.
IRJET- Plant Leaf Disease Detection using Image ProcessingIRJET Journal
This document discusses a technique for early detection of plant diseases through image processing. The technique involves preprocessing leaf images through color space conversion and enhancement. The region of interest (disease area) is segmented and features are extracted. A minimum distance classifier compares the features to a database of known plant diseases and identifies the disease. The methodology achieves over 90% accuracy in detecting diseases. The system could help farmers monitor crops efficiently and apply treatments early to reduce losses from diseases. Future work may involve integrating audio cues and recommending specific treatments to increase productivity and reduce costs and pollution.
A Novel Approach for Tomato Diseases Classification Based on Deep Convolution...Mohammad Shakirul islam
This document summarizes Mohammad Shakirul Islam's research paper on classifying tomato plant diseases using deep convolutional neural networks. The paper includes sections on motivation, literature review, proposed methodology, results discussion, and future work. The proposed methodology uses a dataset of 3000 images across 6 tomato disease classes. A convolutional neural network model with 5 convolution layers, 5 max pooling layers, and 2 dense layers is trained on 80% of the data and tested on the remaining 20% for classification performance. Results show the model achieved high training and validation accuracy for identifying different tomato diseases.
Segmentation of unhealthy region of plant leaf using image processing techniqueseSAT Journals
Abstract A segmentation technique is used to segment the diseased portion of a leaf. Based on the segmented area texture and color feature, disease can be identified by classification technique. There are many segmentation techniques such as Edge detection, Thresholding, K-Means clustering, Fuzzy C-Means clustering, Penalized Fuzzy C-Means, Unsupervised segmentation. Segmentation of diseased area of a plant leaf is the first step in disease detection and identification which plays crucial role in agriculture research. This paper provides different segmentation techniques that are used to segment diseased leaf of a plant. Keywords: Fuzzy C-Means, K-Means, Penalized FCM, Unsupervised Fuzzy Clustering
IRJET- Leaf Disease Detecting using CNN TechniqueIRJET Journal
This document describes a proposed system for detecting leaf diseases using convolutional neural network (CNN) techniques. The system uses image acquisition, pre-processing including cropping, resizing and filtering, segmentation using k-means clustering, feature extraction of color, texture and shape features, and classification using CNN. The system is tested on images of mango, pomegranate, guava and sapota leaves to automatically identify diseases and recommend appropriate control methods, providing an improvement over manual identification methods.
Classify Rice Disease Using Self-Optimizing Models and Edge Computing with A...Damian R. Mingle, MBA
This document summarizes research on classifying rice diseases using self-optimizing machine learning models and edge computing. The researchers used an automated machine learning platform to build models for identifying 3 classes of rice diseases from images. They extracted features from the images using a deep learning network and then used those features to train traditional machine learning models like ExtraTrees Classifier and Stochastic Gradient Descent. The goal was to develop an end-to-end solution for rice farmers to easily and accurately detect diseases in the field and receive treatment recommendations in real-time.
LEAF DISEASE DETECTION USING IMAGE PROCESSING AND SUPPORT VECTOR MACHINE (SVM)Journal For Research
in the study on leaf disease detection can be a helpful aspect in keeping an eye on huge area of fields of crops, but it’s important to detect the disease as early as possible. This paper gives a method to detect the disease caused to the leaf calculating the RGB and HSV values. Primarily the image is blurred in order reduce noise. Then the image is converted from RGB to HSV form, after this color thresholding is done. After thresholding foreground or background detection is performed. Background detection leads to feature extractions of the leaf. Then k-means algorithm is applied which can help to clot the clusters. The following system is a software based solution for detecting the disease with which the leaf is infected. In order to detect the disease some steps are to be followed using image processing and support vector machine. Improving the quality and production of agricultural products detection of the leaf disease can be useful.
Regularized Weighted Ensemble of Deep Classifiers ijcsa
Ensemble of classifiers increases the performance of the classification since the decision of many experts
are fused together to generate the resultant decision for prediction making. Deep learning is a classification algorithm where along with the basic learning technique, fine tuning learning is done for improved precision of learning. Deep classifier ensemble learning is having a good scope of research.Feature subset selection is another for creating individual classifiers to be fused for ensemble learning. All these ensemble techniques faces ill posed problem of overfitting. Regularized weighted ensemble of deep support vector machine performs the prediction analysis on the three UCI repository problems IRIS,Ionosphere and Seed data set, thereby increasing the generalization of the boundary plot between the
classes of the data set. The singular value decomposition reduced norm 2 regularization with the two level
deep classifier ensemble gives the best result in our experiments.
This document discusses methods for collecting and analyzing statistical data. It describes primary and secondary data sources, as well as direct, indirect, registration, and experimental data collection methods. The document also covers determining sample size using Slovin's formula and margin of error. Various sampling techniques are outlined, including probability methods like simple random sampling, systematic random sampling, stratified random sampling, cluster sampling, and multi-stage sampling. Non-probability sampling methods such as convenience sampling, quota sampling, purposive sampling, and snowball sampling are also discussed.
This document evaluates several supervised machine learning algorithms for classifying gene expression data from microarray experiments. It describes analyzing two gene expression datasets, the leukemia and DLBCL datasets, using k-nearest neighbors, naive Bayes, decision trees, and support vector machines with and without feature selection. The results show that support vector machines achieved the best performance overall, and that feature selection improved the accuracy of all the algorithms.
Plant disease detection and classification using deep learning JAVAID AHMAD WANI
This document describes a project on plant disease detection and classification using deep learning. The objectives are to automatically detect plant diseases as early as symptoms appear on leaves in order to increase crop productivity. Deep learning techniques like convolutional neural networks (CNNs) are implemented using libraries like TensorFlow and Keras. Two CNN models, VGG16 and VGG19, are compared for classifying diseases in a dataset of 38 classes and 87k images of 14 crop species. The system achieved over 95% accuracy on validation. Future work involves developing a mobile app and integrating disease recommendations to help farmers.
Wheat leaf disease detection using image processingIJLT EMAS
India is a agricultural based county where approx 70%
of population depend on agriculture. Now a days the plant
disease detection is very important because agriculture is the
backbone of the county like india. Farmer is not aware what type
of disease plant having and how to prevent them from these
diseases. To overcome from these we are going to develop a
technique in which we can able to detect plant disease using
image processing technique. This includes following steps: image
acquisition image pre-processing, feature extraction and at last
we apply a classifier know as neural network.
Classification of Apple diseases through machine learningMuqaddas Bin Tahir
This presentation describes a research work in which constitutional neural network is used for fruit’s classification and recognizing their diseases. CNN is the popular , advanced and powerful architecture of Neural Network. The method describe in this presentation perform better than other classification and recognition techniques on various datasets and it is not affected by illumination, translation and occlusion problems.
The classification of different types of tumors is of great importance in cancer diagnosis and its drug discovery. Cancer classification via gene expression data is known to contain the keys for solving the fundamental problems relating to the diagnosis of cancer. The recent advent of DNA microarray technology has made rapid monitoring of thousands of gene expressions possible. With this large quantity of gene expression data, scientists have started to explore the opportunities of classification of cancer using a gene expression dataset. To gain a profound understanding of the classification of cancer, it is necessary to take a closer look at the problem, the proposed solutions, and the related issues altogether. In this research thesis, I present a new way for Leukemia classification using the latest AI technique of Deep learning using Google TensorFlow on gene expression data.
Crop Leaf Disease Diagnosis using Convolutional Neural Networkijtsrd
This document summarizes a research paper that proposes a system for detecting crop leaf diseases using convolutional neural networks. The system can identify diseases in five major crops: corn, sugarcane, wheat, grape, and rice. It uses a MobileNet CNN model trained on a dataset of leaf images. Experiments show the system can accurately classify leaf diseases with 97.33% precision. The system automatically diagnoses leaf diseases and recommends pesticides, helping farmers detect and address issues early.
New Feature Selection Model Based Ensemble Rule Classifiers Method for Datase...ijaia
Feature selection and classification task are an essential process in dealing with large data sets that
comprise numerous number of input attributes. There are many search methods and classifiers that have
been used to find the optimal number of attributes. The aim of this paper is to find the optimal set of
attributes and improve the classification accuracy by adopting ensemble rule classifiers method. Research
process involves 2 phases; finding the optimal set of attributes and ensemble classifiers method for
classification task. Results are in terms of percentage of accuracy and number of selected attributes and
rules generated. 6 datasets were used for the experiment. The final output is an optimal set of attributes
with ensemble rule classifiers method. The experimental results conducted on public real dataset
demonstrate that the ensemble rule classifiers methods consistently show improve classification accuracy
on the selected dataset. Significant improvement in accuracy and optimal set of attribute selected is
achieved by adopting ensemble rule classifiers method.
This document describes research using deep learning to classify plant diseases from leaf images. The researchers:
1) Trained convolutional neural networks on a dataset of 54,306 images of plant leaves with 14 crop species and 26 diseases to classify the crop/disease in each image.
2) Achieved up to 99.35% accuracy when testing the best model on images not used for training, demonstrating the feasibility of this approach.
3) Found that models using transferred learning from ImageNet and color images performed best, but models still achieved over 85% accuracy on grayscale images, showing they learned the diseases rather than biases in the dataset.
IRJET - A Review on Identification and Disease Detection in Plants using Mach...IRJET Journal
This document reviews machine learning techniques for identifying and detecting plant diseases. It discusses how techniques like artificial neural networks, support vector machines, K-nearest neighbors classification and fuzzy c-means clustering have been applied to identify diseases in crops like rice, potatoes, cucumbers and grapes. The techniques analyze images of plant leaves to extract features and classify whether the plant has a disease or not. The document also outlines the common stages of disease identification using machine learning, which include preprocessing images, segmentation, feature extraction, classification and disease identification.
La Ley 84 de 1989 busca proteger a los animales en Colombia. Esta ley establece normas para el bienestar y cuidado apropiado de los animales, así como para prevenir y sancionar el maltrato. El objetivo es asegurar que todos los animales reciban un trato humano y digno.
The top news stories from New York - August 22, 2014
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La tauromaquia, como la corrida de toros, es una práctica cruel que debe ser prohibida. Los toros son sometidos a un sufrimiento innecesario y a una muerte violenta solo para el entretenimiento de los espectadores. Debemos poner fin a este tipo de diversión basada en el maltrato animal.
La ciudad de Cúcuta en el departamento de Norte de Santander en Colombia prohibió las corridas de toros en su jurisdicción, poniendo fin a una tradición cultural que había existido por generaciones pero que era criticada por grupos de derechos de los animales.
The document discusses waiting on God's timing. It notes that God has a plan and timing that should not be ignored. It uses the example of David, who was anointed king at a young age but had to wait 14 years and hide from King Saul before becoming king, to illustrate God's plan and timing. The document also includes several Bible verses about waiting on God from Psalms. It states that God requires waiting to receive direction, keep people in step with his timing, test and strengthen faith, and bring people through to his timing. Finally, it recommends waiting patiently, with trust, expectation, steadfastness, and standing on God's word.
The document summarizes the burgeoning Korean startup ecosystem. It describes how the Korean government is supporting startups through initiatives like startup incubators and accelerators. It also notes the growth of co-working spaces and networking events in Seoul to support entrepreneurs. Specifically, it highlights the concentration of accelerators and startups in the Teheran-ro area of Gangnam and how Korea aims to become a startup hub in Asia, showing promising signs like an influx of talent and overseas investment.
El documento habla sobre la generosidad de Dios al entregar a su Hijo Jesús para salvar a la humanidad. También cita varios pasajes bíblicos que enseñan sobre la generosidad, incluyendo la historia de Zaqueo que pasó de ser avaro a dar la mitad de sus bienes a los pobres. Finalmente, describe algunos beneficios de ser generoso como ser exaltado, prosperar y tener una cosecha generosa.
O documento discute os conceitos e implantação do programa 5S. Ele explica que a implantação efetiva dos 5S promove mudança de atitudes e hábitos, o que não é uma tarefa simples. Também destaca que a implantação deve ser sistematizada e planejada para garantir a longevidade da mudança, e que o sucesso depende do clima organizacional e das relações entre as pessoas.
Los fenicios eran expertos navegantes y comerciantes que comerciaban una variedad de productos como madera, tintes, tejidos y cerámica entre el Mediterráneo oriental, África y España a través de sus colonias y factorías. Desarrollaron industrias para transformar materias primas como arcilla en cerámica y aceitunas en aceite. Su legado incluye la estrategia colonial, los modelos comerciales marítimos y el alfabeto fenicio, una de las bases del alfabeto moderno.
Los puentes de red interconectan segmentos de red operando en la capa 2 del modelo OSI. Funcionan almacenando las direcciones MAC de los dispositivos y reenviando los paquetes entre segmentos. Existen puentes homogéneos e heterogéneos, y puentes locales y remotos. Los puentes dividen las redes en segmentos para aislar dominios de colisión y mejorar el rendimiento, aunque introducen algunos retardos.
This document discusses BGP flow specification phase 2 which focuses on BGP persistence. It describes the problem that current BGP flowspec policies are withdrawn if the route reflector or controller fails, leaving the network vulnerable. BGP persistence aims to keep filters and policies active for a configurable time like hours or days until the route reflector or controller returns. The configuration allows setting a stale time on a per address family basis to control how long policies persist after a failure.
Edexcell Biology;
Most year 10 & 11 syllabus points by ppt.
Used in lessons to scaffold class teaching and as a revision resource for students
These resources are from many sources
El documento habla sobre los inicios y las actividades futuras de una organización llamada CISLUSS, incluyendo asistencia a eventos nacionales e internacionales sobre software libre, reuniones temáticas, capacitaciones, coorganización de eventos académicos sobre software libre y privativo, y la organización de su propio evento llamado el II Encuentro de Usuarios de Linux. Finaliza con un llamado a estar preparados para más eventos sobre software libre.
Un millonario ofrece sus posesiones a quien atraviese su piscina llena de cocodrilos. Un hombre logra salir de la piscina con arañazos y hematomas. Aunque el millonario le ofrece recompensas, el hombre solo quiere encontrar a quien lo empujó a la piscina. La historia enseña que un empujón puede llevarnos a logros inesperados.
The Association of German Chambers of Commerce and Industry (DIHK) is the central organization representing 79 local Chambers of Commerce and Industry (IHKs) in Germany. By law, all German companies except for small businesses, free professions, and farms must join their local IHK. With over three million members across big companies and small businesses, the DIHK has considerable political influence speaking on behalf of German commercial enterprises regardless of size.
A Study on MRI Liver Image Segmentation using Fuzzy Connected and Watershed T...IIRindia
A comparison study between automatic and interactive methods for liver segmentation from contrast-enhanced MRI images is ocean. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to refer five error measures that highlight different aspects of segmentation accuracy. The measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods like Fuzzy Connected and Watershed Methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques. In this paper only Fuzzy Connected and Watershed Methods are discussed.
a. The authors developed a 2D spatial filter to isolate nuclei from images of cardiac cells for the purpose of accurately quantifying cellular density. They tested different filtering methods and found that Gaussian edge detection produced counts most consistent with manual user counts.
b. Statistical analysis of the results showed that obtaining more trained user counts could help optimize the algorithms. While some image counts were within 5% error of user counts, not all images produced statistically significant results, suggesting the need for further algorithm improvements.
c. The authors' nuclear detection method shows promise but still requires optimization, such as improving user training protocols, binary conversion methods, and Gaussian filter parameters to produce more consistent counts across images.
For the agriculture sector, detecting and identifying plant diseases at an early stage is extremely important and
still very challenging. Machine learning is an application of AI that helps us achieve this purpose effectively. It
uses a group of algorithms to analyze and interpret data, learn from it, and using it, smart decisions can be
made. For accomplishing this project, a dataset that contains a set of healthy & diseased plant leaf images are
used then using image processing we extract the features of the image. Then we model this dataset with
different machine learning algorithms like Random Forest, Support Vector Machine, Naïve Bayes etc. The aim is
to hold out a comparative study to spot which of those algorithm can predict diseases with the at most
accuracy. We compare factors like precision, accuracy, error rates as well as prediction time of different
machine learning algorithms. After all these comparison, valuable conclusions can be made for this project.
Predicting disease at an early stage becomes critical, and the most difficult challenge is to predict it correctly along with the sickness. The prediction happens based on the symptoms of an individual. The model presented can work like a digital doctor for disease prediction, which helps to timely diagnose the disease and can be efficient for the person to take immediate measures. The model is much more accurate in the prediction of potential ailments. The work was tested with four machine learning algorithms and got the best accuracy with Random Forest.
Live and Dead Cells Counting from Microscopic Trypan Blue Staining Images usi...IJECEIAES
Cell counting is a required procedure in biomedical experiments and drug testing. Manual cell counting performed with a hemocytometer is time consuming and individual dependence. This study reportedthe development of a computer-assisted program for trypan blue stained-cell counting using digital image analysis. Images of trypan blue-stained breast cancer cells line were obtained by a microscope with a digital camera. Undesired noise and debris were removed by applying a guided image filter. Color space HSV (Hue, Saturation and Value)conversion and grayscale conversion were performed for distinguishing between live and dead cells. Image thresholding and morphological operators were applied for image segmentation. Live and dead cells were counted after image segmentation and the results were compared with manual counting by three well-experienced counters. The computer-assisted cell counting from thirty-six trypan blue-stained microscopic images had a high correlation coefficient with the live cell results of the experts (r=0.99). The correlation coefficient of the number of dead cells comparing the computer-assisted count and the experts’ count was 0.74. Our approach offers high accuracy (>85%)on counting live cells compared with the experts’ counting. This automated cell counting approach can assist biomedical researchers for both live and dead cells counting.
MitoGame: Gamification Method for Detecting Mitosis from Histopathological I...IRJET Journal
This document proposes a method called MitoGame that uses gamification and crowdsourcing to detect mitosis in histopathological breast cancer images. Convolutional neural networks (CNNs) are trained on expert-annotated images to generate ground truth labels. Non-expert crowds then annotate images through an online game for mitosis detection. The crowd annotations are aggregated and used to retrain the CNNs, improving their ability to detect mitosis. This allows large datasets to be annotated without relying solely on medical experts. Analysis shows crowds can perform as well as experts at this task when guided by a game interface and CNN predictions. The goal is to leverage crowdsourcing to help train accurate CNN models for automated mitosis detection and breast cancer
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.
Preprocessing Techniques for Image Mining on Biopsy ImagesIJERA Editor
This document discusses preprocessing techniques for image mining on biopsy images. It begins with an introduction to biomedical imaging and image mining. The key steps in image mining are described as image retrieval, preprocessing, feature extraction, data mining, and interpretation. Various preprocessing techniques are then evaluated on biopsy images, including interpolation, thresholding, and segmentation. Bicubic interpolation and Otsu thresholding produced good results for enhancing renal biopsy images. Overall, the document evaluates different preprocessing methods and their effects on biopsy images to help extract meaningful features for disease detection through image mining.
BFO – AIS: A Framework for Medical Image Classification Using Soft Computing ...ijsc
Medical images provide diagnostic evidence/information about anatomical pathology. The growth in database is enormous as medical digital image equipment’s like Magnetic Resonance Images (MRI), Computed Tomography (CT), and Positron Emission Tomography CT (PET-CT) are part of clinical work. CT images distinguish various tissues according to gray levels to help medical diagnosis. Ct is more reliable for early tumours and haemorrhages detection as it provides anatomical information to plan radio therapy. Medical information systems goals are to deliver information to right persons at the right time and place to improve care process quality and efficiency. This paper proposes an Artificial Immune System (AIS) classifier and proposed feature selection based on hybrid Bacterial Foraging Optimization (BFO) with Local Search (LS) for medical image classification.
BFO – AIS: A FRAME WORK FOR MEDICAL IMAGE CLASSIFICATION USING SOFT COMPUTING...ijsc
Medical images provide diagnostic evidence/information about anatomical pathology. The growth in
database is enormous as medical digital image equipment’s like Magnetic Resonance Images (MRI),
Computed Tomography (CT), and Positron Emission Tomography CT (PET-CT) are part of clinical work.
CT images distinguish various tissues according to gray levels to help medical diagnosis. Ct is more
reliable for early tumours and haemorrhages detection as it provides anatomical information to plan radio
therapy. Medical information systems goals are to deliver information to right persons at the right time and
place to improve care process quality and efficiency. This paper proposes an Artificial Immune System
(AIS) classifier and proposed feature selection based on hybrid Bacterial Foraging Optimization (BFO)
with Local Search (LS) for medical image classification.
IRJET- IoT based Preventive Crop Disease Model using IP and CNNIRJET Journal
1. The document proposes an IoT-based system using image processing and convolutional neural networks to detect and prevent crop diseases.
2. It involves taking images of crop leaves, extracting features using color filtering and segmentation, training a CNN model on the images, and using the model to identify diseases and provide remedies to farmers.
3. The system aims to help farmers detect diseases early without needing an expert, in order to reduce crop losses and improve agricultural productivity.
IRJET-A Novel Approach for MRI Brain Image Classification and DetectionIRJET Journal
This document proposes a new approach for classifying and detecting brain tumors in MRI images. The method uses discrete wavelet transform for feature extraction, support vector machine for classification, and incremental supervised neural network and invariant moments for tumor detection. MRI brain images are first classified as normal or tumorous. For images detected as tumorous, the method then segments the image and uses moments to determine the symmetry axis and detect any asymmetry which would indicate the location of the tumor. The approach is evaluated on a dataset of 60 MRI images, achieving 98.33% classification accuracy in distinguishing normal and tumorous images.
Semi-automatic model to colony forming units countingIJECEIAES
Colony forming units counting is a conventional process carry out in bacteriological laboratories, and it is used to follow the behavior of bacteria in different conditions. Currently exist different systems, automatic or semiautomatic, to counting colony forming units exits, but, in general, many laboratories continue using manual counting, which consumes considerable time and effort from researchers and laboratory employees. This paper presents a mathematical model carry out to segment the colony forming units and, in this way, counting them from a digital image of the sample. The method uses the color space information of some points in the image and shows good behavior for images with many or few colony forming units in the sample, according to manual counting. The results show efficiencies close to 98% with MacConkey agar.
This document discusses a method for the early detection and classification of pests using image processing. Images of plant leaves affected by pests like whiteflies and aphids are captured using a digital camera. The images are preprocessed by converting to grayscale, resizing, and filtering. Features are then extracted from the images like standard deviation, entropy, and contrast. A support vector machine classifier is trained on these features to classify images as affected or unaffected, and to further classify the pest type. The proposed method aims to reduce pesticide usage through early detection of pests compared to traditional visual inspection methods.
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
Plant Disease Prediction Using Image ProcessingIRJET Journal
The document discusses using image processing techniques to predict plant diseases. It begins with an introduction describing the importance of identifying plant diseases early to reduce crop losses. It then discusses related work where researchers have used techniques like convolutional neural networks (CNNs) to classify plant leaf images with over 98% accuracy. The document outlines the proposed system's architecture, which involves preprocessing images, segmenting leaves, extracting features, and using CNNs for classification. It presents the methodology and experimental results, achieving high accuracy in detecting tomato plant diseases. In conclusion, it states that early detection of diseases using this approach can reduce costs and time compared to manual identification.
MLTDD : USE OF MACHINE LEARNING TECHNIQUES FOR DIAGNOSIS OF THYROID GLAND DIS...cscpconf
Machine learning algorithms are used to diagnosis for many diseases after very important improvements of classification algorithms as well as having large data sets and high performing computational units. All of these increased the accuracy of these methods. The diagnosis of thyroid gland disorders is one of the application for important classification problem. This study majorly focuses on thyroid gland medical diseases caused by underactive or overactive thyroid glands. The dataset used for the study was taken from UCI repository. Classification of this thyroid disease dataset was a considerable task using decision tree algorithm. The overall
prediction accuracy is 100% for training and in range between 98.7% and 99.8% for testing. In this study, we developed the Machine Learning tool for Thyroid Disease Diagnosis (MLTDD), an Intelligent thyroid gland disease prediction tool in Python, which can effectively help to make the right decision, has been designed using PyDev, which is python IDE for Eclipse.
This paper proposes the development of a software that performs the pre-diagnosis of malignant melanoma, spincellular carcinoma and basal-cell carcinoma. The software is divided into five modules, these being: digital imaging, analysis and processing, storage, feature extraction and classification by means of an Artificial Neural Network (ANN). The results shown the performance of the software for two different combination of activation functions in the network. With the use of spectroscopic techniques for the acquisition of images and the combination of non-linear and linear activation functions in the ANN, the software shows an effectiveness greater than 80%, concluding that it can be an effective tool as an aid in the diagnosis of cancer of skin.
Tomato disease detection using deep learning convolutional neural networkPriyanka Pradhan
1. The document presents a method for detecting and classifying diseases in tomato leaves using a convolutional neural network model.
2. The proposed CNN model achieved an overall accuracy of 96.26% on the PlantVillage tomato dataset, outperforming fine-tuned InceptionResNetV2 and InceptionV3 models.
3. The model consists of four convolutional layers, four max pooling layers, and three fully connected layers, and is able to detect different tomato diseases with good individual class accuracies.
This document summarizes a research paper that proposes using genetic programming to develop a multi-class classifier to detect diabetes. The classifier would classify patients as pre-diabetic, diabetic, or non-diabetic. Genetic programming is an artificial intelligence technique that uses principles of natural selection and evolution to solve problems. The proposed genetic programming classifier would help doctors more accurately diagnose diabetes and identify patients in the pre-diabetic phase, allowing early intervention. It reviews related work applying genetic programming and other methods to diabetes classification and outlines the proposed genetic programming approach, including the functions, fitness evaluation, and expected benefits of the multi-class classifier.
Similar to Can Computers count bacteria? Using macro-programming as a tool to improve speed and accuracy for bacterial counts (20)
This document discusses identifying local copepod species from the Mlalazi estuary in South Africa that can be used as live feed for fish larvae. Currently, South African fish hatcheries rely on imported live feed, but copepods are a desirable natural food source for fish larvae due to their nutrition. This study treated copepods from the Mlalazi estuary with different temperature and salinity levels to determine which species are most robust, as the first step to identifying suitable local copepods that can replace imported live feeds and improve fish larvae production.
S Pillay, Dr. A. J. Smit, Dr Deborah Robertson-Andersson. Submitted to the ninth Scientific Symposium of the Western Indian Ocean Marine Science (WIOMSA) 2015.
Kaveera Singh, Surina Singh, Gan Moodley, Deborah Robertson-Andersson. Presented at the ninth Scientific Symposium of the Western Indian Ocean Marine Science (WIOMSA) 2015.
This study investigated nitrogen pollution levels along the south coast of KwaZulu-Natal, South Africa. It examined three estuaries - Hibberdene (Mhlungwa Estuary), Margate (Kongweni Estuary with WWTP), and Port Edward (Umtamvuna Estuary) - to determine if each was eutrophic. Nitrogen levels were also analyzed at increasing distances from the estuary mouths in associated rocky shore habitats. Statistical analyses found significant differences in nitrogen pollution among the three sites, with rocky shores supplemented by nutrients from estuarine sources. The study recommends monitoring both estuaries and rocky shores to better understand impacts, and developing more wastewater treatment
Deborah Robertson-Andersson, Judy Mann-Lang, Monica Maroun, Shana Mian & Christa Panos. Presented at the Symposium of Contemporary Conservation Practice 2015.
This document discusses the use of rubrics to enhance student scientific writing skills. It provides examples of rubrics used to assess student work in biology, chemistry, geology, and physics. The document outlines benefits of using rubrics such as making learning criteria and standards visible to students. Data is presented comparing student performance and pass rates from 2013 to 2015, finding that use of rubrics corresponded with improved student marks and higher pass rates. However, strikes impacted student performance in 2015 and gains were not consistently observed that year. Overall, the document advocates for the use of rubrics in assessment as part of the teaching and learning process.
The document discusses using a detailed rubric to evaluate student scientific writing skills across four life science disciplines in a marine biology module. A rubric was created using Bloom's taxonomy and applied to assess student reports in 2013, which showed significant grader bias. In 2014, the same rubric was used and found to eliminate bias while students' writing improved with a 22% increase between first and last reports, leading to a 22% rise in pass rates overall compared to the previous year without a rubric.
Copepods are a desired live feed for fish larvae due to their high nutritional value and small size. This study identified robust sub-tropical copepod species from the Mlalazi estuary in South Africa that could be used to feed Dusky Kob larvae. Copepods were subjected to different temperatures from 10-40°C and salinities from 10-40 PSU over 48 hours to determine their tolerance ranges. Identifying local copepod species that can survive a wide range of conditions will help increase fish larvae production and nutrition while decreasing costs compared to imported feeds or those requiring enrichment.
1) The document discusses aquaculture and aquaponics systems for balancing food production, economic development, and environmental impact reduction. It outlines various challenges with aquaculture including institutional failures and lack of infrastructure.
2) Two honors students conducted a study comparing plant growth and fish mortality in goldfish vs koi aquaponics systems, finding koi systems produced greater growth. They also analyzed costs/benefits of the systems.
3) Aquaponics has potential as a "leapfrog technology" but depends on support from government, hatcheries, and a network of suppliers like the electric grid or mobile network.
Aquaponics systems often require production fish like tilapia as a protein source, but this increases costs and complexity. Using ornamental fish instead could increase project success by providing a cheap, easy to cultivate waste source. This study tested a backyard raft aquaponics system using goldfish or koi as the waste source under LED lights or sunlight. Koi fish supported better plant growth than goldfish, and LED lights did not significantly impact growth compared to sunlight. Using ornamental fish is an example of "leapfrog technology" that could make aquaponics more economically viable.
Travis Kunnen, Ursula Scharler, David Muir. Presented at the ninth Scientific Symposium of the Western Indian Ocean Marine Science Association (WIOMSA) 2015.
Refilwe Mofokeng, Gemma Gerber, Mathew Coote, Sipho Mkhize, Deborah Robertson-Andersson, Gan Moodley. Presented at the Symposium of Contemporary Conservation Practice 2015.
Raeesah Ameen, Gan Moodley, Deborah Robertson-Andersson. Presented at the ninth Scientific Symposium of the Western Indian Ocean Marine Science Association (WIOMSA) 2015.
Gemma Gerber, Thembani Mkhize, Deborah Robertson-Andersson, Gan Moodley. Presented the the ninth Scientific Symposium of the Western Indian Ocean Marine Science Association (WIOMSA) 2015.
Kaveera Singh, Surina Singh, Gan Moodley, Deborah Robertson-Andersson. Presented at the ninth Scientific Symposium of the Western Indian Ocean Marine Science Association (WIOMSA) 2015.
This document presents a novel methodology for separating microplastics (<500μm) from particulate organic matter (POM) in water samples. Current separation methods are inefficient at separating suspended microplastics and POM due to similarities in size and density. The developed method uses a two-phase separation where a non-polar solvent is added to draw microplastics into a separate immiscible layer that can be removed and analyzed. Testing recovered over 90% of fluorescent polyethylene terephthalate and polypropylene microplastics added but only 1% of denser polyethylene terephthalate microbeads. This accurate separation method allows for investigation of microplastic ingestion and effects in marine organisms and ecosystems.
1) The study aims to determine the effects of microplastic consumption and retention in marine fish by examining microplastic settlement times, gut retention times in various fish species, and the physiological impacts of prolonged microplastic consumption.
2) Preliminary results found that smaller microplastics remain bioavailable and are retained in fish guts longer than larger ones, and that microplastics can serve as a delivery mechanism for pollutants by remaining in fish guts for extended periods.
3) Future experiments will examine the impacts of prolonged microplastic exposure on fish physiology and determine if microplastics can pass through the gut lining into tissues.
Following on from a successful presentation to the Reciculation council members earlier in 2004, I was asked to make this presentation which should be titled why abalone farmers should grow seaweeds.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
TOPIC OF DISCUSSION: CENTRIFUGATION SLIDESHARE.pptxshubhijain836
Centrifugation is a powerful technique used in laboratories to separate components of a heterogeneous mixture based on their density. This process utilizes centrifugal force to rapidly spin samples, causing denser particles to migrate outward more quickly than lighter ones. As a result, distinct layers form within the sample tube, allowing for easy isolation and purification of target substances.
PPT on Alternate Wetting and Drying presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
BIRDS DIVERSITY OF SOOTEA BISWANATH ASSAM.ppt.pptxgoluk9330
Ahota Beel, nestled in Sootea Biswanath Assam , is celebrated for its extraordinary diversity of bird species. This wetland sanctuary supports a myriad of avian residents and migrants alike. Visitors can admire the elegant flights of migratory species such as the Northern Pintail and Eurasian Wigeon, alongside resident birds including the Asian Openbill and Pheasant-tailed Jacana. With its tranquil scenery and varied habitats, Ahota Beel offers a perfect haven for birdwatchers to appreciate and study the vibrant birdlife that thrives in this natural refuge.
PPT on Sustainable Land Management presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...Sérgio Sacani
Context. The observation of several L-band emission sources in the S cluster has led to a rich discussion of their nature. However, a definitive answer to the classification of the dusty objects requires an explanation for the detection of compact Doppler-shifted Brγ emission. The ionized hydrogen in combination with the observation of mid-infrared L-band continuum emission suggests that most of these sources are embedded in a dusty envelope. These embedded sources are part of the S-cluster, and their relationship to the S-stars is still under debate. To date, the question of the origin of these two populations has been vague, although all explanations favor migration processes for the individual cluster members. Aims. This work revisits the S-cluster and its dusty members orbiting the supermassive black hole SgrA* on bound Keplerian orbits from a kinematic perspective. The aim is to explore the Keplerian parameters for patterns that might imply a nonrandom distribution of the sample. Additionally, various analytical aspects are considered to address the nature of the dusty sources. Methods. Based on the photometric analysis, we estimated the individual H−K and K−L colors for the source sample and compared the results to known cluster members. The classification revealed a noticeable contrast between the S-stars and the dusty sources. To fit the flux-density distribution, we utilized the radiative transfer code HYPERION and implemented a young stellar object Class I model. We obtained the position angle from the Keplerian fit results; additionally, we analyzed the distribution of the inclinations and the longitudes of the ascending node. Results. The colors of the dusty sources suggest a stellar nature consistent with the spectral energy distribution in the near and midinfrared domains. Furthermore, the evaporation timescales of dusty and gaseous clumps in the vicinity of SgrA* are much shorter ( 2yr) than the epochs covered by the observations (≈15yr). In addition to the strong evidence for the stellar classification of the D-sources, we also find a clear disk-like pattern following the arrangements of S-stars proposed in the literature. Furthermore, we find a global intrinsic inclination for all dusty sources of 60 ± 20◦, implying a common formation process. Conclusions. The pattern of the dusty sources manifested in the distribution of the position angles, inclinations, and longitudes of the ascending node strongly suggests two different scenarios: the main-sequence stars and the dusty stellar S-cluster sources share a common formation history or migrated with a similar formation channel in the vicinity of SgrA*. Alternatively, the gravitational influence of SgrA* in combination with a massive perturber, such as a putative intermediate mass black hole in the IRS 13 cluster, forces the dusty objects and S-stars to follow a particular orbital arrangement. Key words. stars: black holes– stars: formation– Galaxy: center– galaxies: star formation
PPT on Direct Seeded Rice presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
Anti-Universe And Emergent Gravity and the Dark UniverseSérgio Sacani
Recent theoretical progress indicates that spacetime and gravity emerge together from the entanglement structure of an underlying microscopic theory. These ideas are best understood in Anti-de Sitter space, where they rely on the area law for entanglement entropy. The extension to de Sitter space requires taking into account the entropy and temperature associated with the cosmological horizon. Using insights from string theory, black hole physics and quantum information theory we argue that the positive dark energy leads to a thermal volume law contribution to the entropy that overtakes the area law precisely at the cosmological horizon. Due to the competition between area and volume law entanglement the microscopic de Sitter states do not thermalise at sub-Hubble scales: they exhibit memory effects in the form of an entropy displacement caused by matter. The emergent laws of gravity contain an additional ‘dark’ gravitational force describing the ‘elastic’ response due to the entropy displacement. We derive an estimate of the strength of this extra force in terms of the baryonic mass, Newton’s constant and the Hubble acceleration scale a0 = cH0, and provide evidence for the fact that this additional ‘dark gravity force’ explains the observed phenomena in galaxies and clusters currently attributed to dark matter.
ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...Advanced-Concepts-Team
Presentation in the Science Coffee of the Advanced Concepts Team of the European Space Agency on the 07.06.2024.
Speaker: Diego Blas (IFAE/ICREA)
Title: Gravitational wave detection with orbital motion of Moon and artificial
Abstract:
In this talk I will describe some recent ideas to find gravitational waves from supermassive black holes or of primordial origin by studying their secular effect on the orbital motion of the Moon or satellites that are laser ranged.
SDSS1335+0728: The awakening of a ∼ 106M⊙ black hole⋆Sérgio Sacani
Context. The early-type galaxy SDSS J133519.91+072807.4 (hereafter SDSS1335+0728), which had exhibited no prior optical variations during the preceding two decades, began showing significant nuclear variability in the Zwicky Transient Facility (ZTF) alert stream from December 2019 (as ZTF19acnskyy). This variability behaviour, coupled with the host-galaxy properties, suggests that SDSS1335+0728 hosts a ∼ 106M⊙ black hole (BH) that is currently in the process of ‘turning on’. Aims. We present a multi-wavelength photometric analysis and spectroscopic follow-up performed with the aim of better understanding the origin of the nuclear variations detected in SDSS1335+0728. Methods. We used archival photometry (from WISE, 2MASS, SDSS, GALEX, eROSITA) and spectroscopic data (from SDSS and LAMOST) to study the state of SDSS1335+0728 prior to December 2019, and new observations from Swift, SOAR/Goodman, VLT/X-shooter, and Keck/LRIS taken after its turn-on to characterise its current state. We analysed the variability of SDSS1335+0728 in the X-ray/UV/optical/mid-infrared range, modelled its spectral energy distribution prior to and after December 2019, and studied the evolution of its UV/optical spectra. Results. From our multi-wavelength photometric analysis, we find that: (a) since 2021, the UV flux (from Swift/UVOT observations) is four times brighter than the flux reported by GALEX in 2004; (b) since June 2022, the mid-infrared flux has risen more than two times, and the W1−W2 WISE colour has become redder; and (c) since February 2024, the source has begun showing X-ray emission. From our spectroscopic follow-up, we see that (i) the narrow emission line ratios are now consistent with a more energetic ionising continuum; (ii) broad emission lines are not detected; and (iii) the [OIII] line increased its flux ∼ 3.6 years after the first ZTF alert, which implies a relatively compact narrow-line-emitting region. Conclusions. We conclude that the variations observed in SDSS1335+0728 could be either explained by a ∼ 106M⊙ AGN that is just turning on or by an exotic tidal disruption event (TDE). If the former is true, SDSS1335+0728 is one of the strongest cases of an AGNobserved in the process of activating. If the latter were found to be the case, it would correspond to the longest and faintest TDE ever observed (or another class of still unknown nuclear transient). Future observations of SDSS1335+0728 are crucial to further understand its behaviour. Key words. galaxies: active– accretion, accretion discs– galaxies: individual: SDSS J133519.91+072807.4
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
Authoring a personal GPT for your research and practice: How we created the Q...
Can Computers count bacteria? Using macro-programming as a tool to improve speed and accuracy for bacterial counts
1. Kunnen, T.H., Moodley, G.K., Robertson-Andersson, D.V.
University of KwaZulu-Natal, School of Life Sciences
traviskunnen1982@gmail.com MACE Lab @
Can computers count bacteria?
Using macro-programming as a tool to
improve speed and accuracy for bacterial
counts
INTRODUCTION
The manual counting, sizing and analysis of thousands of individual bacterial cells
takes a long time (Figure 1 & 3).
To reduce time and effort, and to increase accuracy and repeatability, this can be
coded for using image and data analysis software macros.
Macro programming is a built-in function of many available software applications and
does not require extensive coding knowledge to utilize.
Macros function by (i) performing a pre-programmed set of instructions repeatedly;
(ii) potentially decreasing user bias and the influence of the halo effect (Figure 2)
and (iii) removing single cell perspectivism by using segmentation thresholding
(Figure 4).
MATERIALS AND METHODS
After a brief training session on Image Pro Plus v.6.2 (IPP), sixty random epifluorescent
bacterial images were supplied to volunteers (referred to as users).
Users manually counted and measured (length and width) each “object” that they
identified as a bacterial cell while timing themselves.
After a 2 - 3 day break, the same 10 randomly selected images from the original 60
images were given to the users to test their accuracy and reproducibility.
Directly after this, users re-counted all images (randomly mixed) using an automated
image analysis macro within IPP and again timed.
Gathered data was run through separate Microsoft Excel macros for analysis.
ANOVA’s were used to test for statistical differences in the data.
Figure 1: Image number 36 from the pool of 60.
4) Automated count (user vs. users)
Of all the tested variables, the level of segmentation used was of specific
interest.
User 4 showed the lowest segmentation levels with user 3 forming a
subset with the next highest segmentation, followed by user 5, 2 then 1.
Figure 2: Influence of the halo effect
and limits of resolution at 200%
magnification required to manually
count bacterial cells.
Figure 3: Excerpts of image 36 manually counted (A), cells = 254, time taken
=15:37 sec and automatically counted (B) using segmentation threshold
67, cells =197, time taken = 00:47.
Figure 4: Influence of three different segmentation thresholds (A = 27, B = 67 and C = 87) on data acquisition for automated analysis of the same image. The inserts show a reduced
image of Figure 2 and the influence of the selected segmentation setting on bacterial dimensional parameters.
Halo
DISCUSSION AND CONCLUSION
Automated macros as a tool for data gathering from bacterial images is both
feasible and applicable.
The large difference in time taken to assess an image greatly outweighs the
possible slight loss in bacterial cell count whilst maintaining an accurate
level of bacterial cell dimensions within a sample population.
Average total time taken for all 60 images for manual counting vs.
automated counting was 4.82 h vs. 26.66 min, respectively.
No difference in cell or population number counts between all users.
User 4 (who was colorblind) showed a significant difference in all the
dimensional parameters (Table 1).
User 4 preferentially chose a lower segmentation range (Figure 4A) to
assess the cells which greatly influenced their dimensional data without
affecting numbers.
We conclude:
Using a higher segmentation setting is more reliable, as there was no
significant difference in cells counted, but dimensional data become
more accurate, up to a critical segmentation point.
Data gathered by users 1, 2, 3 and 5 showed no statistical difference
between their manual and automated data.
Time spent by automated data gathering was dramatically reduced.
When estimating a bacterial population the loss or gain of cells across 10
fields of view (Massana et al. 1997) is negligible while bacterial
dimensions become more accurate.
ACKNOWLEDGEMENTS
We thank the NRF for funding this project, the volunteers who gave up their time for
counting and the members of MACE Lab for their support. We also thank Riaan
Rossouw for his original assistance on IPP macros, Kendyl Le Roux for images
and Theo van Zyl for his continued help on Microsoft Excel macros.
PRELIMINARY RESULTS
Tested parameters for ANOVA on IBM SPSS 23 included but were not limited to Summed Length and Width, Summed Bio-Volume, Cells, Population Numbers, Population Biomass,
Time per Cell, Total Time and Segmentation Threshold used. All data were ranked due to assumptions being violated.
Table 1: ANOVA p values for result # 3 to the left
1) Manual count (user vs. users)
Summed length and width data showed that users 1, 2 and 5 were significantly different from user
3, who was also different from user 4.
2) Accuracy and repeatability count (self vs. self)
For all users, there was no statistical difference between any of the tested parameters.
3) Automated count (self manual count
vs. self automated count)
A significant difference was found for all
users for time per cell and total time
taken, being much reduced during
automated counting (mean times were
1085.13% and 774.20% faster,
respectively).
For users 1, 2, 3 and 5 there was no
statistical difference for any of the data
gathered from measuring cells (Table 1).
User 4 showed a statistical difference for
all tested variables with the exception of
cells and numbers.
A = 27 B = 67 C = 87
REFERENCES
Massana, R., Gasol, J.M., Bjørnsen, P.K., Blackburn, N., Hagstrom, A., Hietanen, S., Hygum, B.H., Kuparinen, J. and Pedrós-Alió, C. 1997.
Measurement of bacterial size via image analysis of epifluorescence preparations: description of an inexpensive system and solutions to
some of the most common problems. Scienta Marina 61, 397-407.
A
B