The document describes a mobile application called Plant Disease Doctor App that uses convolutional neural networks and deep learning to identify plant diseases from images. The app allows users to take photos of diseased plant leaves or import images and receives a disease diagnosis along with management tips. The system was trained on a dataset of over 20,000 images of 15 plant species with 5 diseases each. It aims to make disease identification easier, faster and less reliant on experts physically examining plants. The application architecture involves users uploading images, a CNN model analyzing them and returning results, which are then matched to information from a database for display.
IRJET- An Expert System for Plant Disease Diagnosis by using Neural NetworkIRJET Journal
This document describes a proposed system to diagnose plant diseases using neural networks and image processing. The system would take an image of a plant leaf using a smartphone, extract features from the image like color, texture, and edges using preprocessing and segmentation algorithms. It would then use a support vector machine algorithm and the extracted features to predict the plant disease. It would also recommend pesticides and their costs to treat the predicted disease to help farmers identify effective treatment options. The goal is to develop an automated system to help identify plant diseases from images in order to benefit large-scale crop monitoring and disease detection.
IRJET- An Expert System for Plant Disease Diagnosis by using Neural NetworkIRJET Journal
This document describes a proposed system to diagnose plant diseases using neural networks and image processing. The system would take an image of a plant leaf using a smartphone, extract features from the image like color, texture, and edges using preprocessing and segmentation algorithms. It would then use a support vector machine algorithm and the extracted features to predict the plant disease. It would also recommend pesticides and their costs to treat the predicted disease to help farmers identify effective treatment options. The goal is to develop an automated system to help identify plant diseases from images in order to benefit large-scale crop monitoring and disease detection.
Plant Diseases Prediction Using Image ProcessingIRJET Journal
This document discusses a system for predicting plant diseases using image processing. Specifically, it focuses on predicting diseases that affect tomato plant leaves. The system uses convolutional neural network techniques to analyze images of tomato leaves and predict whether they have diseases like Late blight, bacterial, or viral infections. It discusses implementing various steps like pre-processing images, feature extraction, and using a CNN classifier to classify images as having a specific disease or being healthy. The goal is to help farmers quickly and accurately identify plant diseases from leaf images to improve crop management and reduce economic losses.
Leaf Disease Detection Using Image Processing and MLIRJET Journal
The document discusses using image processing and machine learning techniques like convolutional neural networks (CNNs) to detect plant leaf diseases. It proposes a system that uses CNNs to classify plant leaf images and detect diseases. The system would first preprocess leaf images, then extract features from them and feed them into a CNN model for classification. This could help farmers detect diseases early and improve crop productivity. The document reviews several related works applying CNNs and deep learning to tasks like mango leaf disease detection, tomato disease detection, and dragon fruit maturity detection with high accuracy. It outlines the proposed system architecture and algorithm and concludes CNNs can accurately detect plant diseases with reduced time and cost compared to manual methods.
EARLY BLIGHT AND LATE BLIGHT DISEASE DETECTION ON POTATO LEAVES USING CONVOLU...IRJET Journal
This document proposes a method to detect early blight and late blight diseases on potato leaves using convolutional neural networks. It involves preprocessing leaf images, extracting features, and training a classifier to identify disease patterns and provide predictions. The proposed system is evaluated on performance metrics like accuracy, precision, and recall. Convolutional neural networks have achieved high accuracy in plant disease classification compared to traditional methods by automating feature extraction from images. This technique allows for early detection of diseases, helping farmers prevent crop damage and losses.
Fruit Disease Detection And Fertilizer RecommendationIRJET Journal
This document discusses a proposed system for fruit disease detection and fertilizer recommendation using image processing and convolutional neural networks (CNNs). It begins with an introduction to the importance of detecting fruit diseases early to prevent economic losses. It then reviews several existing related works that use techniques like CNNs, k-nearest neighbors, support vector machines, and image processing methods. The proposed system would capture images using a camera, preprocess the images, train a CNN model on a dataset of diseased and healthy fruit images to classify new images, and provide fertilizer/pesticide recommendations. The system is broken down into modules for the frontend user interface, data collection and preprocessing, model building using CNNs, and a backend for analysis and recommendations.
IRJET- Detection of Leaf Diseases and Classifying them using Multiclass SVMIRJET Journal
This document presents a method for detecting and classifying leaf diseases using image processing techniques. The proposed method involves image acquisition, preprocessing using median filtering, segmentation using k-means clustering, feature extraction of texture features using GLCM, and classification using multiclass SVM. Median filtering is used for noise removal before segmentation. K-means clustering segments the leaf from the image. GLCM extracts statistical texture features from the segmented leaf images. These features are then classified using multiclass SVM to identify the disease, achieving an accuracy of 97%. The method provides a fast and accurate way to detect leaf diseases using digital image processing and machine learning techniques.
Plant Leaf Disease Detection using Deep Learning and CNNIRJET Journal
This document proposes using convolutional neural networks and deep learning to detect plant leaf diseases. It discusses how plant diseases can impact food supply and the economy. The proposed system would use a CNN model trained on labeled images of healthy and diseased leaves to automatically detect diseases. It describes preprocessing input images, the architecture of the CNN model with convolutional, pooling and fully connected layers, and training the model on labeled image data. The system is intended to provide a low-cost and accurate way to detect leaf diseases early and help farmers address issues. The model achieved 96.4% accuracy in testing.
IRJET- An Expert System for Plant Disease Diagnosis by using Neural NetworkIRJET Journal
This document describes a proposed system to diagnose plant diseases using neural networks and image processing. The system would take an image of a plant leaf using a smartphone, extract features from the image like color, texture, and edges using preprocessing and segmentation algorithms. It would then use a support vector machine algorithm and the extracted features to predict the plant disease. It would also recommend pesticides and their costs to treat the predicted disease to help farmers identify effective treatment options. The goal is to develop an automated system to help identify plant diseases from images in order to benefit large-scale crop monitoring and disease detection.
IRJET- An Expert System for Plant Disease Diagnosis by using Neural NetworkIRJET Journal
This document describes a proposed system to diagnose plant diseases using neural networks and image processing. The system would take an image of a plant leaf using a smartphone, extract features from the image like color, texture, and edges using preprocessing and segmentation algorithms. It would then use a support vector machine algorithm and the extracted features to predict the plant disease. It would also recommend pesticides and their costs to treat the predicted disease to help farmers identify effective treatment options. The goal is to develop an automated system to help identify plant diseases from images in order to benefit large-scale crop monitoring and disease detection.
Plant Diseases Prediction Using Image ProcessingIRJET Journal
This document discusses a system for predicting plant diseases using image processing. Specifically, it focuses on predicting diseases that affect tomato plant leaves. The system uses convolutional neural network techniques to analyze images of tomato leaves and predict whether they have diseases like Late blight, bacterial, or viral infections. It discusses implementing various steps like pre-processing images, feature extraction, and using a CNN classifier to classify images as having a specific disease or being healthy. The goal is to help farmers quickly and accurately identify plant diseases from leaf images to improve crop management and reduce economic losses.
Leaf Disease Detection Using Image Processing and MLIRJET Journal
The document discusses using image processing and machine learning techniques like convolutional neural networks (CNNs) to detect plant leaf diseases. It proposes a system that uses CNNs to classify plant leaf images and detect diseases. The system would first preprocess leaf images, then extract features from them and feed them into a CNN model for classification. This could help farmers detect diseases early and improve crop productivity. The document reviews several related works applying CNNs and deep learning to tasks like mango leaf disease detection, tomato disease detection, and dragon fruit maturity detection with high accuracy. It outlines the proposed system architecture and algorithm and concludes CNNs can accurately detect plant diseases with reduced time and cost compared to manual methods.
EARLY BLIGHT AND LATE BLIGHT DISEASE DETECTION ON POTATO LEAVES USING CONVOLU...IRJET Journal
This document proposes a method to detect early blight and late blight diseases on potato leaves using convolutional neural networks. It involves preprocessing leaf images, extracting features, and training a classifier to identify disease patterns and provide predictions. The proposed system is evaluated on performance metrics like accuracy, precision, and recall. Convolutional neural networks have achieved high accuracy in plant disease classification compared to traditional methods by automating feature extraction from images. This technique allows for early detection of diseases, helping farmers prevent crop damage and losses.
Fruit Disease Detection And Fertilizer RecommendationIRJET Journal
This document discusses a proposed system for fruit disease detection and fertilizer recommendation using image processing and convolutional neural networks (CNNs). It begins with an introduction to the importance of detecting fruit diseases early to prevent economic losses. It then reviews several existing related works that use techniques like CNNs, k-nearest neighbors, support vector machines, and image processing methods. The proposed system would capture images using a camera, preprocess the images, train a CNN model on a dataset of diseased and healthy fruit images to classify new images, and provide fertilizer/pesticide recommendations. The system is broken down into modules for the frontend user interface, data collection and preprocessing, model building using CNNs, and a backend for analysis and recommendations.
IRJET- Detection of Leaf Diseases and Classifying them using Multiclass SVMIRJET Journal
This document presents a method for detecting and classifying leaf diseases using image processing techniques. The proposed method involves image acquisition, preprocessing using median filtering, segmentation using k-means clustering, feature extraction of texture features using GLCM, and classification using multiclass SVM. Median filtering is used for noise removal before segmentation. K-means clustering segments the leaf from the image. GLCM extracts statistical texture features from the segmented leaf images. These features are then classified using multiclass SVM to identify the disease, achieving an accuracy of 97%. The method provides a fast and accurate way to detect leaf diseases using digital image processing and machine learning techniques.
Plant Leaf Disease Detection using Deep Learning and CNNIRJET Journal
This document proposes using convolutional neural networks and deep learning to detect plant leaf diseases. It discusses how plant diseases can impact food supply and the economy. The proposed system would use a CNN model trained on labeled images of healthy and diseased leaves to automatically detect diseases. It describes preprocessing input images, the architecture of the CNN model with convolutional, pooling and fully connected layers, and training the model on labeled image data. The system is intended to provide a low-cost and accurate way to detect leaf diseases early and help farmers address issues. The model achieved 96.4% accuracy in testing.
A Review: Plant leaf Disease Detection Using Convolution Neural Network in Ma...IRJET Journal
This document summarizes a research paper that reviews the use of convolutional neural networks (CNNs) for plant leaf disease detection. The research aims to classify images of tomato, potato, and pepper leaves to identify 15 common diseases with 97% accuracy. CNN algorithms are effective for image classification tasks like this one. The system allows farmers to easily upload leaf images and receive disease diagnoses and pesticide recommendations to prevent crop loss and increase yields. Deep learning approaches can help improve agricultural efficiency and productivity by automating disease identification.
A Review: Plant leaf Disease Detection Using Convolution Neural Network in Ma...IRJET Journal
This document summarizes a research paper that reviews the use of convolutional neural networks (CNNs) for plant leaf disease detection. The research aims to classify images of tomato, potato, and pepper leaves to identify 15 common diseases with 97% accuracy. CNN algorithms are effective for image classification tasks like this one. The system allows farmers to easily upload leaf images and receive disease diagnoses and pesticide recommendations to prevent crop loss and increase yields. Deep learning approaches can help improve agricultural efficiency and productivity by automating disease identification.
ORGANIC PRODUCT DISEASE DETECTION USING CNNIRJET Journal
This document discusses using convolutional neural networks to detect diseases in organic products. The researchers classify fruits and their diseases using a mixed deep neural network and contour feature-based technique. They train their model on a dataset of 6509 rice leaf photos and test on 2000 images, achieving 99.6% accuracy. Their proposed system uses pre-trained ResNet50 models to extract deep features and classify plant diseases, offering advantages of lower cost, scalability, and time savings compared to manual inspection methods.
LEAF DISEASE IDENTIFICATION AND REMEDY RECOMMENDATION SYSTEM USINGCNNIRJET Journal
This document presents a leaf disease identification and recommendation system using convolutional neural networks. The proposed system uses a dataset of 1500 plant images to train a CNN model to classify leaf diseases into 3 classes and identify recommended remedies. It involves data collection, preprocessing, training a CNN architecture with convolutional, activation and pooling layers to analyze images and detect diseases. The system is able to accurately identify different diseases of crops like cotton, sugarcane and wheat leaves. It provides a useful tool for farmers to detect diseases early and take appropriate treatment measures.
Plant Disease Detection Using InceptionV3IRJET Journal
This document summarizes a research paper that proposes using an InceptionV3 convolutional neural network (CNN) to detect diseases in cotton plant leaves. The paper first reviews existing methods for plant disease detection using digital image processing and machine learning algorithms. It then describes collecting a cotton disease dataset and preprocessing the images. Next, it explains using transfer learning with the InceptionV3 CNN model for feature extraction and disease recognition. The proposed method is implemented and tested on the cotton disease dataset, achieving accurate detection. Finally, the paper concludes that CNNs like InceptionV3 show promise for automated and reliable plant disease detection but that more research is still needed.
Plant disease detection system using image processingIRJET Journal
This document describes a study that used image processing and convolutional neural networks to develop a system for detecting plant diseases from images of plant leaves. The researchers created a model using the PlantVillage dataset of over 55,000 photos of leaves with 38 different disease labels. They augmented the data and used a CNN architecture with convolutional, pooling, ReLU, and fully connected layers to achieve 95.3% accuracy in classifying disease labels, outperforming conventional detection methods. The goal was to help farmers efficiently identify diseases early and apply appropriate treatments to prevent crop loss and economic impacts.
Android application for detection of leaf disease (Using Image processing and...IRJET Journal
This document describes an Android application for detecting leaf diseases using image processing and neural networks. The application uses a convolutional neural network (CNN) model trained on a dataset of images of healthy and unhealthy plant leaves. The CNN classifies leaf images uploaded by users to identify the disease and provide an accurate diagnosis. The application aims to help farmers and students quickly identify plant diseases to control their spread and reduce agricultural losses. It analyzes leaf images using techniques like preprocessing, augmentation, feature extraction, and classification with a CNN architecture. The trained model is integrated into an Android application using TensorFlow Lite to enable real-time disease detection from smartphone photos of leaves.
A Review Paper on Automated Plant Leaf Disease Detection TechniquesIRJET Journal
This paper reviews various techniques for automated plant leaf disease detection. It summarizes several papers that have used techniques such as machine learning algorithms, convolutional neural networks, image processing, and deep learning models to detect plant diseases from images of leaves. The paper finds that convolutional neural networks and deep learning methods generally provide higher accuracy compared to machine learning techniques alone. Transfer learning approaches with CNNs can train models with small datasets and achieve high accuracy for identifying plant diseases.
FRUIT DISEASE DETECTION AND CLASSIFICATION USING ARTIFICIAL INTELLIGENCEIRJET Journal
This document proposes a method to detect and classify diseases in fruits like banana, apple, and orange using artificial intelligence techniques. The method uses convolutional neural networks and k-means clustering. Fruit images are preprocessed, features like color, shape, and size are extracted, and k-means clustering is used to categorize the images into clusters. A convolutional neural network is then used to classify whether each fruit in the image is infected or not infected. The method achieved 95% accuracy in identifying diseases in banana, apple, and orange fruits.
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- Greensworth: A Step Towards Smart CultivationIRJET Journal
This document describes a mobile app called GreensWorth that aims to help crop growers through various smart cultivation features. The app includes disease detection using convolutional neural networks to identify plant diseases from images. It also uses decision tree regression for weather prediction based on multiple weather attributes. Additionally, the app provides a crops guide, disease library, and global community forum to help users with cultivation expertise and advice. The developers believe the comprehensive guidance from these tools will encourage more interest in gardening and agriculture.
IRJET- Crop Leaf Disease Diagnosis using Convolutional Neural NetworkIRJET Journal
This document describes a system for diagnosing crop leaf diseases using convolutional neural networks. The system can identify diseases in five major crops: corn, rice, wheat, sugarcane, and grapes. It uses a MobileNet model and CNN architecture trained on datasets of images of healthy and diseased leaves. The system achieves 97.33% accuracy in diagnosing diseases in grape leaves. It aims to help farmers detect diseases early and determine the appropriate pesticides.
RICE LEAF DISEASES CLASSIFICATION USING CNN WITH TRANSFER LEARNINGIRJET Journal
The document presents a study on classifying rice leaf diseases using convolutional neural networks (CNN) with transfer learning. Key points:
- The study developed a CNN model based on VGG-16 architecture to classify rice leaf diseases like blast, blight, and brown spot from a dataset of 1649 disease leaf images and 507 healthy leaf images.
- Transfer learning was used by keeping the earlier layers of pre-trained VGG-16 unchanged and fine-tuning the later layers for the new dataset, since the dataset was small.
- The proposed CNN model with transfer learning achieved a test accuracy of 92.46%, while a CNN model developed from scratch without transfer learning achieved only 74% accuracy, highlighting the benefit of
A Novel Machine Learning Based Approach for Detection and Classification of S...IRJET Journal
This document presents a novel machine learning approach for detecting and classifying sugarcane plant diseases using discrete wavelet transform (DWT). Existing methods use MATLAB and support vector machines with limited accuracy. The proposed method uses DWT for image segmentation to identify affected plant regions. It acquires images, pre-processes for noise reduction, segments using edge detection, extracts features from regions of interest, classifies diseases, and detects diseases based on image analysis and classification. The goal is to more accurately detect diseases early to control crop damage and losses. A dataset of 13 sugarcane diseases is used to evaluate the approach.
A deep learning-based approach for early detection of disease in sugarcane pl...IAESIJAI
In many regions of the nation, agriculture serves as the primary industry. The farming environment now faces a number of challenges to farmers. One of the major concerns, and the focus of this research, is disease prediction. A methodology is suggested to automate a process for identifying disease in plant growth and warning farmers in advance so they can take appropriate action. Disease in crop plants has an impact on agricultural production. In this work, a novel DenseNet-support vector machine: explainable artificial intelligence (DNet-SVM: XAI) interpretation that combines a DenseNet with support vector machine (SVM) and local interpretable model-agnostic explanation (LIME) interpretation has been proposed. DNet-SVM: XAI was created by a series of modifications to DenseNet201, including the addition of a support vector machine (SVM) classifier. Prior to using SVM to identify if an image is healthy or un-healthy, images are first feature extracted using a convolution network called DenseNet. In addition to offering a likely explanation for the prediction, the reasoning is carried out utilizing the visual cue produced by the LIME. In light of this, the proposed approach, when paired with its determined interpretability and precision, may successfully assist farmers in the detection of infected plants and recommendation of pesticide for the identified disease.
IRJET- A Review on Machine Learning Algorithm Used for Crop Monitoring System...IRJET Journal
1. The document discusses using machine learning algorithms to monitor crops through a crop monitoring system. Sensors collect data on factors like humidity, temperature, wind speed, and sunlight, which can impact crop growth and cause diseases.
2. Machine learning techniques like artificial neural networks are applied to the sensor data for prediction. The results can help farmers make decisions to prevent diseases and optimize profit.
3. Specifically, the proposed system uses a multi-layer perceptron neural network technique on the sensor data compared to a dataset of past experiences to generate predictions on potential crop diseases based on current field conditions.
The document describes a plant disease detection system that uses machine learning and computer vision techniques to identify and classify plant diseases from images of affected plants. It discusses how traditional visual inspection methods are time-consuming and inaccurate. The proposed system uses algorithms like VGG19 and CNN to analyze images of diseased plants captured by devices like drones and smartphones. It trains models on datasets of images labeled with diseases. The system is shown to accurately detect diseases with over 75% accuracy. It has the potential to help farmers and gardeners identify diseases early and improve plant health and agricultural productivity. Future work may include expanding the dataset and exploring additional deep learning models.
The document describes a plant disease detection system that uses machine learning and computer vision techniques to identify and classify plant diseases from images of affected plants. It discusses how traditional visual inspection methods are time-consuming and inaccurate. The proposed system applies algorithms like VGG19 and CNN to analyze images of diseased plants captured using devices like drones and smartphones. It then evaluates the images to detect disease indicators and identify the specific disease. The system achieved 75.4% accuracy in testing and can help farmers and gardeners quickly and easily monitor plant health to treat diseases early. This can help improve agricultural productivity and sustainability. The document also reviews related works and provides details of the proposed system's methodology, algorithms, evaluation process and conclusions.
Leaf Disease Detection and Selection of Fertilizers using Artificial Neural N...IRJET Journal
This document proposes a system for detecting leaf diseases and selecting appropriate fertilizers using artificial neural networks. The system involves image acquisition, preprocessing, feature extraction using gray level co-occurrence matrix (GLCM) and statistical moments, training an artificial neural network, classifying diseases, and identifying the disease name and recommended fertilizer. It is intended to provide farmers with preventative treatment recommendations. The system is tested on mango and lemon leaves with two diseases each. If implemented, it could help farmers identify diseases early and apply the correct fertilizers to improve crop quality and yields.
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.
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A Review: Plant leaf Disease Detection Using Convolution Neural Network in Ma...IRJET Journal
This document summarizes a research paper that reviews the use of convolutional neural networks (CNNs) for plant leaf disease detection. The research aims to classify images of tomato, potato, and pepper leaves to identify 15 common diseases with 97% accuracy. CNN algorithms are effective for image classification tasks like this one. The system allows farmers to easily upload leaf images and receive disease diagnoses and pesticide recommendations to prevent crop loss and increase yields. Deep learning approaches can help improve agricultural efficiency and productivity by automating disease identification.
A Review: Plant leaf Disease Detection Using Convolution Neural Network in Ma...IRJET Journal
This document summarizes a research paper that reviews the use of convolutional neural networks (CNNs) for plant leaf disease detection. The research aims to classify images of tomato, potato, and pepper leaves to identify 15 common diseases with 97% accuracy. CNN algorithms are effective for image classification tasks like this one. The system allows farmers to easily upload leaf images and receive disease diagnoses and pesticide recommendations to prevent crop loss and increase yields. Deep learning approaches can help improve agricultural efficiency and productivity by automating disease identification.
ORGANIC PRODUCT DISEASE DETECTION USING CNNIRJET Journal
This document discusses using convolutional neural networks to detect diseases in organic products. The researchers classify fruits and their diseases using a mixed deep neural network and contour feature-based technique. They train their model on a dataset of 6509 rice leaf photos and test on 2000 images, achieving 99.6% accuracy. Their proposed system uses pre-trained ResNet50 models to extract deep features and classify plant diseases, offering advantages of lower cost, scalability, and time savings compared to manual inspection methods.
LEAF DISEASE IDENTIFICATION AND REMEDY RECOMMENDATION SYSTEM USINGCNNIRJET Journal
This document presents a leaf disease identification and recommendation system using convolutional neural networks. The proposed system uses a dataset of 1500 plant images to train a CNN model to classify leaf diseases into 3 classes and identify recommended remedies. It involves data collection, preprocessing, training a CNN architecture with convolutional, activation and pooling layers to analyze images and detect diseases. The system is able to accurately identify different diseases of crops like cotton, sugarcane and wheat leaves. It provides a useful tool for farmers to detect diseases early and take appropriate treatment measures.
Plant Disease Detection Using InceptionV3IRJET Journal
This document summarizes a research paper that proposes using an InceptionV3 convolutional neural network (CNN) to detect diseases in cotton plant leaves. The paper first reviews existing methods for plant disease detection using digital image processing and machine learning algorithms. It then describes collecting a cotton disease dataset and preprocessing the images. Next, it explains using transfer learning with the InceptionV3 CNN model for feature extraction and disease recognition. The proposed method is implemented and tested on the cotton disease dataset, achieving accurate detection. Finally, the paper concludes that CNNs like InceptionV3 show promise for automated and reliable plant disease detection but that more research is still needed.
Plant disease detection system using image processingIRJET Journal
This document describes a study that used image processing and convolutional neural networks to develop a system for detecting plant diseases from images of plant leaves. The researchers created a model using the PlantVillage dataset of over 55,000 photos of leaves with 38 different disease labels. They augmented the data and used a CNN architecture with convolutional, pooling, ReLU, and fully connected layers to achieve 95.3% accuracy in classifying disease labels, outperforming conventional detection methods. The goal was to help farmers efficiently identify diseases early and apply appropriate treatments to prevent crop loss and economic impacts.
Android application for detection of leaf disease (Using Image processing and...IRJET Journal
This document describes an Android application for detecting leaf diseases using image processing and neural networks. The application uses a convolutional neural network (CNN) model trained on a dataset of images of healthy and unhealthy plant leaves. The CNN classifies leaf images uploaded by users to identify the disease and provide an accurate diagnosis. The application aims to help farmers and students quickly identify plant diseases to control their spread and reduce agricultural losses. It analyzes leaf images using techniques like preprocessing, augmentation, feature extraction, and classification with a CNN architecture. The trained model is integrated into an Android application using TensorFlow Lite to enable real-time disease detection from smartphone photos of leaves.
A Review Paper on Automated Plant Leaf Disease Detection TechniquesIRJET Journal
This paper reviews various techniques for automated plant leaf disease detection. It summarizes several papers that have used techniques such as machine learning algorithms, convolutional neural networks, image processing, and deep learning models to detect plant diseases from images of leaves. The paper finds that convolutional neural networks and deep learning methods generally provide higher accuracy compared to machine learning techniques alone. Transfer learning approaches with CNNs can train models with small datasets and achieve high accuracy for identifying plant diseases.
FRUIT DISEASE DETECTION AND CLASSIFICATION USING ARTIFICIAL INTELLIGENCEIRJET Journal
This document proposes a method to detect and classify diseases in fruits like banana, apple, and orange using artificial intelligence techniques. The method uses convolutional neural networks and k-means clustering. Fruit images are preprocessed, features like color, shape, and size are extracted, and k-means clustering is used to categorize the images into clusters. A convolutional neural network is then used to classify whether each fruit in the image is infected or not infected. The method achieved 95% accuracy in identifying diseases in banana, apple, and orange fruits.
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- Greensworth: A Step Towards Smart CultivationIRJET Journal
This document describes a mobile app called GreensWorth that aims to help crop growers through various smart cultivation features. The app includes disease detection using convolutional neural networks to identify plant diseases from images. It also uses decision tree regression for weather prediction based on multiple weather attributes. Additionally, the app provides a crops guide, disease library, and global community forum to help users with cultivation expertise and advice. The developers believe the comprehensive guidance from these tools will encourage more interest in gardening and agriculture.
IRJET- Crop Leaf Disease Diagnosis using Convolutional Neural NetworkIRJET Journal
This document describes a system for diagnosing crop leaf diseases using convolutional neural networks. The system can identify diseases in five major crops: corn, rice, wheat, sugarcane, and grapes. It uses a MobileNet model and CNN architecture trained on datasets of images of healthy and diseased leaves. The system achieves 97.33% accuracy in diagnosing diseases in grape leaves. It aims to help farmers detect diseases early and determine the appropriate pesticides.
RICE LEAF DISEASES CLASSIFICATION USING CNN WITH TRANSFER LEARNINGIRJET Journal
The document presents a study on classifying rice leaf diseases using convolutional neural networks (CNN) with transfer learning. Key points:
- The study developed a CNN model based on VGG-16 architecture to classify rice leaf diseases like blast, blight, and brown spot from a dataset of 1649 disease leaf images and 507 healthy leaf images.
- Transfer learning was used by keeping the earlier layers of pre-trained VGG-16 unchanged and fine-tuning the later layers for the new dataset, since the dataset was small.
- The proposed CNN model with transfer learning achieved a test accuracy of 92.46%, while a CNN model developed from scratch without transfer learning achieved only 74% accuracy, highlighting the benefit of
A Novel Machine Learning Based Approach for Detection and Classification of S...IRJET Journal
This document presents a novel machine learning approach for detecting and classifying sugarcane plant diseases using discrete wavelet transform (DWT). Existing methods use MATLAB and support vector machines with limited accuracy. The proposed method uses DWT for image segmentation to identify affected plant regions. It acquires images, pre-processes for noise reduction, segments using edge detection, extracts features from regions of interest, classifies diseases, and detects diseases based on image analysis and classification. The goal is to more accurately detect diseases early to control crop damage and losses. A dataset of 13 sugarcane diseases is used to evaluate the approach.
A deep learning-based approach for early detection of disease in sugarcane pl...IAESIJAI
In many regions of the nation, agriculture serves as the primary industry. The farming environment now faces a number of challenges to farmers. One of the major concerns, and the focus of this research, is disease prediction. A methodology is suggested to automate a process for identifying disease in plant growth and warning farmers in advance so they can take appropriate action. Disease in crop plants has an impact on agricultural production. In this work, a novel DenseNet-support vector machine: explainable artificial intelligence (DNet-SVM: XAI) interpretation that combines a DenseNet with support vector machine (SVM) and local interpretable model-agnostic explanation (LIME) interpretation has been proposed. DNet-SVM: XAI was created by a series of modifications to DenseNet201, including the addition of a support vector machine (SVM) classifier. Prior to using SVM to identify if an image is healthy or un-healthy, images are first feature extracted using a convolution network called DenseNet. In addition to offering a likely explanation for the prediction, the reasoning is carried out utilizing the visual cue produced by the LIME. In light of this, the proposed approach, when paired with its determined interpretability and precision, may successfully assist farmers in the detection of infected plants and recommendation of pesticide for the identified disease.
IRJET- A Review on Machine Learning Algorithm Used for Crop Monitoring System...IRJET Journal
1. The document discusses using machine learning algorithms to monitor crops through a crop monitoring system. Sensors collect data on factors like humidity, temperature, wind speed, and sunlight, which can impact crop growth and cause diseases.
2. Machine learning techniques like artificial neural networks are applied to the sensor data for prediction. The results can help farmers make decisions to prevent diseases and optimize profit.
3. Specifically, the proposed system uses a multi-layer perceptron neural network technique on the sensor data compared to a dataset of past experiences to generate predictions on potential crop diseases based on current field conditions.
The document describes a plant disease detection system that uses machine learning and computer vision techniques to identify and classify plant diseases from images of affected plants. It discusses how traditional visual inspection methods are time-consuming and inaccurate. The proposed system uses algorithms like VGG19 and CNN to analyze images of diseased plants captured by devices like drones and smartphones. It trains models on datasets of images labeled with diseases. The system is shown to accurately detect diseases with over 75% accuracy. It has the potential to help farmers and gardeners identify diseases early and improve plant health and agricultural productivity. Future work may include expanding the dataset and exploring additional deep learning models.
The document describes a plant disease detection system that uses machine learning and computer vision techniques to identify and classify plant diseases from images of affected plants. It discusses how traditional visual inspection methods are time-consuming and inaccurate. The proposed system applies algorithms like VGG19 and CNN to analyze images of diseased plants captured using devices like drones and smartphones. It then evaluates the images to detect disease indicators and identify the specific disease. The system achieved 75.4% accuracy in testing and can help farmers and gardeners quickly and easily monitor plant health to treat diseases early. This can help improve agricultural productivity and sustainability. The document also reviews related works and provides details of the proposed system's methodology, algorithms, evaluation process and conclusions.
Leaf Disease Detection and Selection of Fertilizers using Artificial Neural N...IRJET Journal
This document proposes a system for detecting leaf diseases and selecting appropriate fertilizers using artificial neural networks. The system involves image acquisition, preprocessing, feature extraction using gray level co-occurrence matrix (GLCM) and statistical moments, training an artificial neural network, classifying diseases, and identifying the disease name and recommended fertilizer. It is intended to provide farmers with preventative treatment recommendations. The system is tested on mango and lemon leaves with two diseases each. If implemented, it could help farmers identify diseases early and apply the correct fertilizers to improve crop quality and yields.
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.
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.