This document presents research on developing a machine learning model to classify endangered bird species using images. The researchers created a dataset of over 7,000 images from 20 endangered bird species and trained convolutional neural network (CNN) models on the data. They tested various hyperparameters and techniques, such as data augmentation, to improve the model's performance. Their best model achieved a promising accuracy of 98% on the test dataset. The researchers conclude that automated bird species identification using machine learning can help conservation efforts by aiding population monitoring and tracking, which supports endangered bird preservation.
IRJET- Bird Species Identification using Image Mining & CNN AlgorithmIRJET Journal
This document describes a study that uses image mining and a convolutional neural network (CNN) algorithm to identify bird species from images. The study uses the Caltech-UCSD Birds-200-2011 dataset to train and test the model. The CNN algorithm converts images to grayscale and generates multiple comparison nodes. It then compares the nodes to the test dataset and predicts the bird species with the highest score. The algorithm achieves an accuracy of 80-90% on the dataset. The study was conducted using a TensorFlow library in a Windows application to accurately identify bird names and provide additional information about each bird species.
Visual and Acoustic Identification of Bird SpeciesIRJET Journal
This paper proposes a system to identify bird species using both visual and acoustic features. Convolutional neural networks (CNNs) are used to extract features from bird images and audio clips to classify birds. The system is trained on the BirdCLEF 2022 dataset for audio and BIRDS 400 dataset for images. A pre-trained CNN model achieves 80% accuracy on a test set of 500 images for visual identification. The paper outlines the system architecture, datasets used, and concludes the combined approach can help identify bird species more accurately than single-modal methods. The system is intended to make bird identification easier and increase awareness of bird watching.
This document discusses using machine learning to help conserve endangered species. It begins by outlining the threats facing endangered species from climate change, habitat loss, and human activity. Machine learning can help overcome challenges to monitoring endangered species by analyzing large datasets from camera traps, satellite imagery, and acoustic recordings to identify and track species. Temperature change also significantly impacts animal populations, so the proposed system will compile temperature and species count data to predict how populations may change in future years. The document reviews several papers applying techniques like CNNs, YOLO, SSD to identify species in camera trap images with high accuracy to help conservation efforts.
Birds Identification System using Deep LearningIRJET Journal
This document describes a bird identification system using deep learning. The system was developed to classify bird species from images using a convolutional neural network (CNN) with ResNet-18 transfer learning. The researchers used the Caltech-UCSD Birds 200 dataset to train and test the model on Google Colab using PyTorch. Transfer learning improved the model's accuracy, achieving a test accuracy of 78.8% at classifying bird species from images.
INSECT SHAPE DETECTION AND CLASSIFICATION USING DIGITAL IMAGE PROCESSINGIRJET Journal
1) The document presents a method for insect shape detection and classification in digital images using image processing techniques and machine learning.
2) It involves preprocessing images, segmenting them using edge detection, extracting features, and classifying insect shapes using KNN machine learning.
3) The method was tested on images of various crop pests like click beetles, moths, and sward grass insects, and showed it could accurately detect shapes and classify the insect images.
Identification of Bird Species using Automation ToolIRJET Journal
The document describes a study that developed an automated tool for identifying bird species using their vocalizations and machine learning. The researchers used a convolutional neural network (CNN) model to classify bird sounds after preprocessing audio recordings with techniques like framing and generating spectrograms. The CNN was trained on a dataset of bird vocalizations and achieved 97% accuracy on validation data, demonstrating it could successfully identify bird species. The goal was to create a tool that could help monitor bird populations and behaviors over time without extensive human effort or intervention.
Detection and classification of animals using Machine Learning and Deep LearningIRJET Journal
This document presents a proposed animal detection and classification system using machine learning and deep learning techniques. The system aims to detect and identify animals from camera trap images to address the problem of human-animal conflict. It will first generate region proposals of animal objects from images and then use techniques like XGBoost, PSO and CNN to detect and classify the animals. These techniques will determine if the region proposals contain true animals or background patches and then identify the animal species. The system is intended to be used for applications like detecting animal intrusions, preventing animal-vehicle collisions, and monitoring agriculture fields. It discusses related work on existing animal detection methods and their limitations. The proposed system architecture includes modules for image collection, preprocessing, dataset creation,
Animal Repellent System for Smart Farming Using AI and Deep LearningIRJET Journal
This document summarizes a research paper on developing an animal repellent system for smart farming using artificial intelligence and deep learning. The system uses a camera to collect animal data, which is then classified using a deep convolutional neural network model trained on the data. It can identify different animal species in real-time. When an animal is detected, it sends an alert message and produces the appropriate ultrasonic frequency to repel that species. Testing on an animal dataset showed the CNN achieved over 98% accuracy in identifying animals. The system provides a real-time monitoring solution using AI to help farmers prevent crop damage from animals.
IRJET- Bird Species Identification using Image Mining & CNN AlgorithmIRJET Journal
This document describes a study that uses image mining and a convolutional neural network (CNN) algorithm to identify bird species from images. The study uses the Caltech-UCSD Birds-200-2011 dataset to train and test the model. The CNN algorithm converts images to grayscale and generates multiple comparison nodes. It then compares the nodes to the test dataset and predicts the bird species with the highest score. The algorithm achieves an accuracy of 80-90% on the dataset. The study was conducted using a TensorFlow library in a Windows application to accurately identify bird names and provide additional information about each bird species.
Visual and Acoustic Identification of Bird SpeciesIRJET Journal
This paper proposes a system to identify bird species using both visual and acoustic features. Convolutional neural networks (CNNs) are used to extract features from bird images and audio clips to classify birds. The system is trained on the BirdCLEF 2022 dataset for audio and BIRDS 400 dataset for images. A pre-trained CNN model achieves 80% accuracy on a test set of 500 images for visual identification. The paper outlines the system architecture, datasets used, and concludes the combined approach can help identify bird species more accurately than single-modal methods. The system is intended to make bird identification easier and increase awareness of bird watching.
This document discusses using machine learning to help conserve endangered species. It begins by outlining the threats facing endangered species from climate change, habitat loss, and human activity. Machine learning can help overcome challenges to monitoring endangered species by analyzing large datasets from camera traps, satellite imagery, and acoustic recordings to identify and track species. Temperature change also significantly impacts animal populations, so the proposed system will compile temperature and species count data to predict how populations may change in future years. The document reviews several papers applying techniques like CNNs, YOLO, SSD to identify species in camera trap images with high accuracy to help conservation efforts.
Birds Identification System using Deep LearningIRJET Journal
This document describes a bird identification system using deep learning. The system was developed to classify bird species from images using a convolutional neural network (CNN) with ResNet-18 transfer learning. The researchers used the Caltech-UCSD Birds 200 dataset to train and test the model on Google Colab using PyTorch. Transfer learning improved the model's accuracy, achieving a test accuracy of 78.8% at classifying bird species from images.
INSECT SHAPE DETECTION AND CLASSIFICATION USING DIGITAL IMAGE PROCESSINGIRJET Journal
1) The document presents a method for insect shape detection and classification in digital images using image processing techniques and machine learning.
2) It involves preprocessing images, segmenting them using edge detection, extracting features, and classifying insect shapes using KNN machine learning.
3) The method was tested on images of various crop pests like click beetles, moths, and sward grass insects, and showed it could accurately detect shapes and classify the insect images.
Identification of Bird Species using Automation ToolIRJET Journal
The document describes a study that developed an automated tool for identifying bird species using their vocalizations and machine learning. The researchers used a convolutional neural network (CNN) model to classify bird sounds after preprocessing audio recordings with techniques like framing and generating spectrograms. The CNN was trained on a dataset of bird vocalizations and achieved 97% accuracy on validation data, demonstrating it could successfully identify bird species. The goal was to create a tool that could help monitor bird populations and behaviors over time without extensive human effort or intervention.
Detection and classification of animals using Machine Learning and Deep LearningIRJET Journal
This document presents a proposed animal detection and classification system using machine learning and deep learning techniques. The system aims to detect and identify animals from camera trap images to address the problem of human-animal conflict. It will first generate region proposals of animal objects from images and then use techniques like XGBoost, PSO and CNN to detect and classify the animals. These techniques will determine if the region proposals contain true animals or background patches and then identify the animal species. The system is intended to be used for applications like detecting animal intrusions, preventing animal-vehicle collisions, and monitoring agriculture fields. It discusses related work on existing animal detection methods and their limitations. The proposed system architecture includes modules for image collection, preprocessing, dataset creation,
Animal Repellent System for Smart Farming Using AI and Deep LearningIRJET Journal
This document summarizes a research paper on developing an animal repellent system for smart farming using artificial intelligence and deep learning. The system uses a camera to collect animal data, which is then classified using a deep convolutional neural network model trained on the data. It can identify different animal species in real-time. When an animal is detected, it sends an alert message and produces the appropriate ultrasonic frequency to repel that species. Testing on an animal dataset showed the CNN achieved over 98% accuracy in identifying animals. The system provides a real-time monitoring solution using AI to help farmers prevent crop damage from animals.
Animal Breed Classification And Prediction Using Convolutional Neural Network...Allison Thompson
This document describes a study that uses a convolutional neural network (CNN) to classify and predict breeds of primates using a dataset of 10 monkey species images. The CNN model was trained on the image dataset and achieved 80.5% accuracy on the training set and 73.53% accuracy on the validation set after 20 epochs of training. The trained model was able to accurately predict the primate breeds in the dataset. The researchers aim to use this type of automated primate breed identification to help conservation efforts and protect endangered species from extinction.
Vision based entomology how to effectively exploit color and shape featurescseij
This document proposes an automatic insect identification framework using color and shape features. It extracts RGB color features and shape features from grasshopper and butterfly images. A support vector machine (SVM) classifier is trained on the extracted features to classify insects. The preliminary results demonstrate the effectiveness of using color and shape features for automatic insect identification of two insect classes. The framework could potentially be extended to identify other insect species.
This document discusses developing a pet care application using machine learning. It aims to use CNNs to predict dog breeds from photos and decision trees to predict diseases. Accurately identifying breeds and diseases early could help pet owners provide better care and save pet lives. The document reviews related work using deep learning for tasks like image classification. It proposes a system to first use a CNN for breed prediction from images then evaluate algorithms like decision trees for disease prediction from pet data. The goal is to integrate machine learning into veterinary healthcare to optimize treatment and enable early diagnosis.
This document presents research on developing an automated system for fish species detection using deep learning and the MobileNetV2 architecture. The researchers assembled a large dataset of fish photos and used MobileNetV2 for image classification. MobileNetV2 is efficient and effective for this task as it balances accuracy and computational performance. Through training and evaluation, the researchers demonstrated that their method achieves accurate fish species detection and categorization while maintaining computational efficiency compared to other techniques. Their proposed application aims to streamline aquatic ecosystem monitoring and conservation efforts by providing a precise and efficient means of assessing fish populations.
Enviro-Insight provides biological and environmental consulting services to various industries. They combine traditional field techniques with advanced technology to conduct botanical, zoological, and ecological surveys. Their services include biodiversity assessments, environmental impact assessments, monitoring programs, and training. They have experience working with government agencies and private companies on projects throughout South Africa.
The document discusses monitoring cattle grazing behavior and intrusion using GPS and virtual fencing technology. It begins by reviewing previous challenges with monitoring cattle, including issues with habituation to frightening devices. It then introduces GPS and virtual fencing as recent technologies that can help track cattle locations and set virtual boundaries. The document examines how GPS collars and a cloud-based monitoring system allow herders to track cattle in real-time and receive alerts about intrusions or other issues.
The document discusses monitoring cattle grazing behavior and intrusion using GPS and virtual fencing technology. It begins by reviewing previous challenges with monitoring cattle, including issues with habituation to frightening devices. It then introduces GPS and virtual fencing as recent technologies that can help track cattle locations and set virtual boundaries. The document examines how GPS collars and a cloud-based monitoring system allow herders to track cattle in real-time and receive alerts about intrusions or other issues.
IRJET- Hybrid Approach to Reduce Energy Utilization in Wireless Sensor Networ...IRJET Journal
This document discusses using bio-inspired techniques to reduce energy utilization in wireless sensor networks. It begins by introducing wireless sensor networks and describing energy consumption as a major challenge. It then discusses several bio-inspired algorithms that could be applied for routing in wireless sensor networks, including ant colony optimization, artificial bee colony optimization, genetic algorithms, and particle swarm optimization. The document reviews related work applying these algorithms to optimize various wireless sensor network performance metrics like energy efficiency. It proposes developing a new routing algorithm based on swarm intelligence techniques to improve wireless sensor network performance.
Monitoring Cattle Grazing Behavior and Intrusion Using Global Positioning Sys...BRNSS Publication Hub
The inadaptability of the frightening devices to the behavioral-change exhibited by grazing animals has been a great challenge in developing animal detection and recognition system that can prevent animal intrusion to a prohibited area. Animal distribution is something that is challenging and that does not have an immediate answer to. In fact, literature shows that just in the last few years, more than 68 different strategies have been used trying to affect animal distribution. These include putting a fence in, developing drinking water in a new location, putting supplemental feed at different locations, changing the times feed is put out, putting in artificial shade so that animals would move to that location, using identification means such as ear tags, radio frequency identification, tattooing, marking, branding, and biometrics. There are a host of frightening strategies that have been used to scare animals from intruding prohibited area; these include installing frightening devices such as explosive materials, acoustics and bioacoustics gadgets, and so on. Moreover, they all work under certain conditions; some of them work even better when they are used synergistically. Sooner or later, these animals become accustomed to most of the frightening techniques put in place to prevent them from going beyond their boundaries or intruding the prohibited area. Virtual fencing (VF) and global positioning system (GPS) are the recent technology developed to handle the challenges that come with animal grazing behavior. Recent advances in GPS and VF technology have allowed the development of free-range and lightweight GPS collar tools suitable for monitoring animal behavioral changes
Using AI to Recommend Pesticides for Effective Management of Multiple Plant D...IRJET Journal
This document discusses using artificial intelligence to recommend pesticides for effective plant disease management. It presents a methodology using computer vision and machine learning, specifically convolutional neural networks (CNNs), to develop a system for detecting plant diseases. The system would analyze leaf images using CNNs and provide fertilizer recommendations to help farmers more easily and quickly identify diseases affecting their crops. This could help reduce excessive pesticide use and environmental damage while improving crop yields. The paper reviews several related works applying CNNs and other machine learning methods to identify diseases from images. It discusses acquiring and preprocessing leaf image datasets to train models for large-scale disease detection, which could support more sustainable and data-driven agricultural decision making.
Livestock are farm animals who are raised to generate profit. They are used for the commodities such as meat, eggs, milk, fur, leather and wool. Livestock animals usually distribute in remote areas, with relatively poor condition of disease diagnosis. Generally, it is difficult to carry out disease diagnosis rapidly and accurately.
Livestock diseases often pose a risk to public health and even affects the economy at large extent as we are quite dependent on the essential commodities we procure from the livestock. It is necessary to detect the disease outcome in the livestock to take the precautionary measures in order to avoid spread amongst them. So, there is a need for a system which can help in predicting the diseases among livestock on the basis of symptoms and suggest the precautionary measures to be taken with respect to the disease predicted. Our proposed system will predict the livestock (Cow, Sheep and Goat) disease using SVC (Support Vector Classifier) multi-class classification algorithm based on the symptoms and also provide the precautionary measures on the basis of disease predicted. There are some diseases which can prove to be fatal. So, our system will also alert the livestock owner if the predicted disease may cause a sudden death.
The document proposes developing a system called the Layer Bird Vaccination Monitoring & Disease Detection System. This system would help small-scale layer poultry farmers in Zimbabwe track vaccinations, monitor treatments, and detect diseases early using data visualization and machine learning models. The system aims to address challenges small-scale farmers face like a lack of record keeping, monitoring of bird health, and limited access to veterinary support. It would allow farmers to enter bird symptom data and get recommendations to prevent losses from diseases.
Final Year Project CHP 1& 2 CHENAI MAKOKO.docxChenaiMartha
The document proposes developing a model for early detection of layer bird diseases for layer poultry farmers. It discusses challenges small-scale farmers face in detecting diseases early due to limited access to veterinary support. Existing systems for disease detection include expert systems using certainty factors, deep learning models for detecting diseases from fecal images, and IoT-based frameworks. However, these systems either focus on expert diagnosis, rely on large datasets, or require specialized hardware. The proposed model aims to allow farmers to enter symptoms and receive recommendations to aid early disease detection.
Plant Leaf Recognition Using Machine Learning: A ReviewIRJET Journal
This document reviews various machine learning and deep learning algorithms that have been used for plant leaf recognition and classification. It summarizes 11 academic papers describing methods using support vector machines, convolutional neural networks, random forests, K-nearest neighbors, and other algorithms to classify plant species from leaf images with different levels of accuracy. The document concludes that these papers have suggested techniques to optimize accuracy, including preprocessing, feature extraction, and algorithm optimization, but that deep learning techniques like VGGNet often achieve the highest accuracy rates.
An Automatic Identification of Agriculture Pest Insects and Pesticide Control...paperpublications3
Abstract: Monitoring agriculture pest insects is currently a key issue in crop protection. Detection of pests in the farms is a major challenge in the field of agriculture; therefore effective measures should be developed to fight the infestation while minimizing the use of pesticides. The techniques of image analysis are extensively applied to agricultural science, and it provides maximum protection to crops, which can ultimately lead to better crop management and production. At farm level it is generally operated by repeated surveys by a human operator of adhesive traps, through the field. This is a labor- and time-consuming activity, and it would be of great advantage for farmers to have an automatic system doing this task. This project is a system based on identification of insects and to determine the quantity of pesticides to be provided according to the growth of the pest insect. The system will determine the quantity of pesticides according to the lifespan of the insect of common pests and will suggest methods of controlling. The proposed system classify the pest insects according to their categories using SVM classifier. This system is thus beneficial to farmers for providing pesticides in correct proportion.
This document describes a proposed AI-based crop identification webapp. The system would use a convolutional neural network (CNN) to identify crop species from images. Users could upload photos of farm yields through a mobile app. The CNN model would be trained on a dataset of labeled plant images. Key aspects of the proposed system include:
1. A training module to develop the CNN model using labeled example images.
2. A testing module to evaluate the trained model's accuracy at identifying crops.
3. An output module allowing users to upload single images for prediction by the CNN model.
The system aims to help farmers identify crops more easily through an automated image recognition system, improving yields and farm management. Experimental results
Recent Trends in Nematode Management Practices: The Indian ContextIRJET Journal
1) Nematodes pose a serious threat to crop production worldwide, causing over $100 billion in damages annually.
2) The document discusses recent trends in nematode management practices in India, including the use of crop rotations, nematicides, and developing resistant plant varieties.
3) It emphasizes the need for more sustainable and environmentally-friendly approaches like using naturally occurring nematicides, biological control, and integrated pest management systems to control nematodes while reducing environmental impacts.
This guide provides methods for monitoring climate change impacts on forest birds in the Albertine Rift region of Africa. It was created by a team of researchers led by David Ochanda and funded by the MacArthur Foundation. The guide outlines ethical data collection methods including selecting sites, conducting bird censuses and collecting habitat and climate data. It also describes how to analyze data using species distribution modeling to understand changes in species ranges from climate change. The goal is to help conservation managers monitor impacts and plan adaptive actions to increase forest bird resilience in the Albertine Rift.
An Innovative Approach for Tomato Leaf Disease Identification and its Benefic...IRJET Journal
This document summarizes an innovative approach for identifying diseases in tomato leaves using image processing and machine learning techniques. Specifically, a Convolutional Neural Network (CNN) model is developed and trained on a dataset of tomato leaf images showing various disease symptoms. Through testing and validation, the proposed approach achieves high accuracy in classifying different types of tomato leaf diseases. Integrating this method could enable timely disease detection, reduce crop losses, and optimize resource allocation for more sustainable agricultural practices. The research contributes a practical solution for automating tomato leaf disease detection to enhance disease management and food security.
Snake Detection in Agricultural Fields using IoTIRJET Journal
1. The document discusses a system to detect snakes in agricultural fields using IoT technology and deep learning techniques. Sensors placed around field borders can detect snake movements and capture images, which are then analyzed using convolutional neural networks to identify if the snake is venomous.
2. If a venomous snake is detected, farmers would be alerted using a buzzer to take precautions. The system aims to reduce snake bite deaths among farmers and conserve snake populations in the area.
3. The document reviews several related works involving using sensors and deep learning to detect animals harming agriculture, monitor home intrusions, and identify snake species for medical treatment.
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
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Enviro-Insight provides biological and environmental consulting services to various industries. They combine traditional field techniques with advanced technology to conduct botanical, zoological, and ecological surveys. Their services include biodiversity assessments, environmental impact assessments, monitoring programs, and training. They have experience working with government agencies and private companies on projects throughout South Africa.
The document discusses monitoring cattle grazing behavior and intrusion using GPS and virtual fencing technology. It begins by reviewing previous challenges with monitoring cattle, including issues with habituation to frightening devices. It then introduces GPS and virtual fencing as recent technologies that can help track cattle locations and set virtual boundaries. The document examines how GPS collars and a cloud-based monitoring system allow herders to track cattle in real-time and receive alerts about intrusions or other issues.
The document discusses monitoring cattle grazing behavior and intrusion using GPS and virtual fencing technology. It begins by reviewing previous challenges with monitoring cattle, including issues with habituation to frightening devices. It then introduces GPS and virtual fencing as recent technologies that can help track cattle locations and set virtual boundaries. The document examines how GPS collars and a cloud-based monitoring system allow herders to track cattle in real-time and receive alerts about intrusions or other issues.
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This document discusses using bio-inspired techniques to reduce energy utilization in wireless sensor networks. It begins by introducing wireless sensor networks and describing energy consumption as a major challenge. It then discusses several bio-inspired algorithms that could be applied for routing in wireless sensor networks, including ant colony optimization, artificial bee colony optimization, genetic algorithms, and particle swarm optimization. The document reviews related work applying these algorithms to optimize various wireless sensor network performance metrics like energy efficiency. It proposes developing a new routing algorithm based on swarm intelligence techniques to improve wireless sensor network performance.
Monitoring Cattle Grazing Behavior and Intrusion Using Global Positioning Sys...BRNSS Publication Hub
The inadaptability of the frightening devices to the behavioral-change exhibited by grazing animals has been a great challenge in developing animal detection and recognition system that can prevent animal intrusion to a prohibited area. Animal distribution is something that is challenging and that does not have an immediate answer to. In fact, literature shows that just in the last few years, more than 68 different strategies have been used trying to affect animal distribution. These include putting a fence in, developing drinking water in a new location, putting supplemental feed at different locations, changing the times feed is put out, putting in artificial shade so that animals would move to that location, using identification means such as ear tags, radio frequency identification, tattooing, marking, branding, and biometrics. There are a host of frightening strategies that have been used to scare animals from intruding prohibited area; these include installing frightening devices such as explosive materials, acoustics and bioacoustics gadgets, and so on. Moreover, they all work under certain conditions; some of them work even better when they are used synergistically. Sooner or later, these animals become accustomed to most of the frightening techniques put in place to prevent them from going beyond their boundaries or intruding the prohibited area. Virtual fencing (VF) and global positioning system (GPS) are the recent technology developed to handle the challenges that come with animal grazing behavior. Recent advances in GPS and VF technology have allowed the development of free-range and lightweight GPS collar tools suitable for monitoring animal behavioral changes
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Livestock are farm animals who are raised to generate profit. They are used for the commodities such as meat, eggs, milk, fur, leather and wool. Livestock animals usually distribute in remote areas, with relatively poor condition of disease diagnosis. Generally, it is difficult to carry out disease diagnosis rapidly and accurately.
Livestock diseases often pose a risk to public health and even affects the economy at large extent as we are quite dependent on the essential commodities we procure from the livestock. It is necessary to detect the disease outcome in the livestock to take the precautionary measures in order to avoid spread amongst them. So, there is a need for a system which can help in predicting the diseases among livestock on the basis of symptoms and suggest the precautionary measures to be taken with respect to the disease predicted. Our proposed system will predict the livestock (Cow, Sheep and Goat) disease using SVC (Support Vector Classifier) multi-class classification algorithm based on the symptoms and also provide the precautionary measures on the basis of disease predicted. There are some diseases which can prove to be fatal. So, our system will also alert the livestock owner if the predicted disease may cause a sudden death.
The document proposes developing a system called the Layer Bird Vaccination Monitoring & Disease Detection System. This system would help small-scale layer poultry farmers in Zimbabwe track vaccinations, monitor treatments, and detect diseases early using data visualization and machine learning models. The system aims to address challenges small-scale farmers face like a lack of record keeping, monitoring of bird health, and limited access to veterinary support. It would allow farmers to enter bird symptom data and get recommendations to prevent losses from diseases.
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1. A training module to develop the CNN model using labeled example images.
2. A testing module to evaluate the trained model's accuracy at identifying crops.
3. An output module allowing users to upload single images for prediction by the CNN model.
The system aims to help farmers identify crops more easily through an automated image recognition system, improving yields and farm management. Experimental results
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1) Nematodes pose a serious threat to crop production worldwide, causing over $100 billion in damages annually.
2) The document discusses recent trends in nematode management practices in India, including the use of crop rotations, nematicides, and developing resistant plant varieties.
3) It emphasizes the need for more sustainable and environmentally-friendly approaches like using naturally occurring nematicides, biological control, and integrated pest management systems to control nematodes while reducing environmental impacts.
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This document summarizes an innovative approach for identifying diseases in tomato leaves using image processing and machine learning techniques. Specifically, a Convolutional Neural Network (CNN) model is developed and trained on a dataset of tomato leaf images showing various disease symptoms. Through testing and validation, the proposed approach achieves high accuracy in classifying different types of tomato leaf diseases. Integrating this method could enable timely disease detection, reduce crop losses, and optimize resource allocation for more sustainable agricultural practices. The research contributes a practical solution for automating tomato leaf disease detection to enhance disease management and food security.
Snake Detection in Agricultural Fields using IoTIRJET Journal
1. The document discusses a system to detect snakes in agricultural fields using IoT technology and deep learning techniques. Sensors placed around field borders can detect snake movements and capture images, which are then analyzed using convolutional neural networks to identify if the snake is venomous.
2. If a venomous snake is detected, farmers would be alerted using a buzzer to take precautions. The system aims to reduce snake bite deaths among farmers and conserve snake populations in the area.
3. The document reviews several related works involving using sensors and deep learning to detect animals harming agriculture, monitor home intrusions, and identify snake species for medical treatment.
Similar to Endangered Bird Species Classification Using Machine Learning Techniques (20)
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.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.