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Advances in aerial sensing and imaging / / edited by Sandeep Kumar [and five others]
| Advances in aerial sensing and imaging / / edited by Sandeep Kumar [and five others] |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Hoboken, NJ : , : John Wiley & Sons, Inc. |
| Descrizione fisica | 1 online resource (xiv, 415 pages) : illustrations, charts |
| Soggetto topico |
Imaging systems
Drone aircraft in remote sensing Drone aircraft |
| ISBN |
1-394-17551-5
1-394-17550-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 A Systematic Study on Aerial Images of Various Domains: Competences, Applications, and Futuristic Scope -- 1.1 Introduction -- 1.2 Literature Work -- 1.2.1 Based on Camera Axis -- 1.2.2 Based on Scale -- 1.2.3 Based on Sensor -- 1.3 Challenges of Object Detection and Classification in Aerial Images -- 1.4 Applications of Aerial Imaging in Various Domains -- 1.5 Conclusions and Future Scope -- 1.5.1 Conclusions -- 1.5.2 Future Scope -- References -- Chapter 2 Oriental Method to Predict Land Cover and Land Usage Using Keras with VGG16 for Image Recognition -- 2.1 Introduction -- 2.2 Literature Review -- 2.3 Materials and Methods -- 2.3.1 Dataset -- 2.3.2 Model Implemented -- 2.4 Discussion -- 2.5 Result Analysis -- 2.6 Conclusion -- References -- Chapter 3 Aerial Imaging Rescue and Integrated System for Road Monitoring Based on AI/ML -- 3.1 Introduction -- 3.2 Related Work -- 3.3 Number of Accidents, Fatalities, and Injuries: 2016-2022 -- 3.3.1 Accidents Statistics in India -- 3.3.2 Accidents Statistics in Haryana -- 3.4 Proposed Methodology -- 3.4.1 ROI and Line Selection -- 3.4.2 Motion Detection -- 3.4.3 Single-Stage Clustering -- 3.4.4 Feature Fusion Process -- 3.4.5 Second-Stage Clustering -- 3.4.6 Tracking Objects -- 3.4.7 Classification -- 3.5 Result Analysis -- 3.6 Conclusion -- References -- Chapter 4 A Machine Learning Approach for Poverty Estimation Using Aerial Images -- 4.1 Introduction -- 4.2 Background and Literature Review -- 4.3 Proposed Methodology -- 4.3.1 Data Acquisition -- 4.3.2 Pre-Processing -- 4.3.3 Feature Extraction -- 4.3.4 Data Integration -- 4.3.5 Model Development -- 4.3.6 Validation -- 4.3.7 Visualization and Analysis -- 4.3.8 Policy and Program Development -- 4.4 Result and Discussion -- 4.5 Conclusion and Future Scope -- References.
Chapter 5 Agriculture and the Use of Unmanned Aerial Vehicles (UAVs): Current Practices and Prospects -- 5.1 Introduction -- 5.2 UAVs Classification -- 5.2.1 Comparison of Various UAVs -- 5.3 Agricultural Use of UAVs -- 5.4 UAVs in Livestock Farming -- 5.5 Challenges -- 5.6 Conclusion -- References -- Chapter 6 An Introduction to Deep Learning-Based Object Recognition and Tracking for Enabling Defense Applications -- 6.1 Introduction -- 6.2 Related Work -- 6.2.1 Importance of Object Monitoring and Surveillance in Defense -- 6.2.2 Need for Object Monitoring and Surveillance in Defense -- 6.2.3 Object Detection Techniques -- 6.2.4 Object Tracking Techniques -- 6.3 Experimental Methods -- 6.3.1 Experimental Setup and Dataset -- 6.3.2 DataSetVISdrone 2019 -- 6.3.3 Experimental Setup -- 6.4 Results and Outcomes -- 6.4.1 Comparison Results -- 6.4.2 Training Results -- 6.5 Conclusion -- 6.6 Future Scope -- References -- Chapter 7 A Robust Machine Learning Model for Forest Fire Detection Using Drone Images -- 7.1 Introduction -- 7.2 Literature Review -- 7.3 Proposed Methodology -- 7.4 Result and Discussion -- 7.5 Conclusion and Future Scope -- References -- Chapter 8 Semantic Segmentation of Aerial Images Using Pixel Wise Segmentation -- 8.1 Introduction -- 8.2 Related Work -- 8.3 Proposed Method -- 8.3.1 Pixelwise Classification Method -- 8.3.2 Morphological Processing -- 8.4 Datasets -- 8.5 Results and Discussion -- 8.5.1 Analysis of the Proposed Method -- 8.6 Conclusion -- References -- Chapter 9 Implementation Analysis of Ransomware and Unmanned Aerial Vehicle Attacks: Mitigation Methods and UAV Security Recommendations -- 9.1 Introduction -- 9.2 Types of Ransomwares -- 9.3 History of Ransomware -- 9.4 Notable Ransomware Strains and Their Impact -- 9.4.1 CryptoLocker (2013) -- 9.4.2 CryptoWall (2014) -- 9.4.3 TeslaCrypt (2015) -- 9.4.4 Locky (2016). 9.4.5 WannaCry (2017) -- 9.4.6 NotPetya (2017) -- 9.4.7 Ryuk (2018) -- 9.4.8 REvil (2019) -- 9.4.9 Present-Day Ransomware Families -- 9.5 Mitigation Methods for Ransomware Attacks -- 9.6 Cybersecurity in UAVs (Unmanned Aerial Vehicles) -- 9.6.1 Introduction on FANETS -- 9.6.2 Network Security Concerning FANETs -- 9.6.3 UAV Security Enhancement -- 9.6.4 Limitations in UAVs -- 9.6.5 Future Scope -- 9.7 Experimental analysis of Wi-Fi Attack on Ryze Tello UAVs -- 9.7.1 Introduction -- 9.7.2 Methodology -- 9.8 Results and Discussion -- 9.9 Conclusion and Future Scope -- References -- Chapter 10 A Framework for Detection of Overall Emotional Score of an Event from the Images Captured by a Drone -- 10.1 Introduction -- 10.1.1 Need for Emotion Recognition -- 10.1.2 Applications of Drones in Deep Learning -- 10.2 Literature Review -- 10.3 Proposed Work -- 10.3.1 Extraction of Images from a Drone -- 10.3.2 Proposed CNN Model -- 10.4 Experimentation and Results -- 10.4.1 Dataset Description -- 10.5 Future Work and Conclusion -- References -- Chapter 11 Drone-Assisted Image Forgery Detection Using Generative Adversarial Net-Based Module -- 11.1 Introduction -- 11.2 Literature Survey -- 11.3 Proposed System -- 11.3.1 Common Forged Feature Network -- 11.3.2 Features Extraction -- 11.3.3 Features Classification and Classification Network -- 11.3.4 Label Prediction -- 11.3.5 Contrastive Learning -- 11.3.6 Binary Cross-Entropy Loss -- 11.4 Results -- 11.4.1 Experimental Settings -- 11.4.2 Performance Comparison -- 11.4.3 LBP Visualized Results -- 11.4.4 Training Convergence -- 11.5 Conclusion -- References -- Chapter 12 Optimizing the Identification and Utilization of Open Parking Spaces Through Advanced Machine Learning -- 12.1 Introduction -- 12.2 Proposed Framework Optimized Parking Space Identifier (OPSI) -- 12.2.1 Framework Components. 12.2.2 Learning Module: Adaptive Prediction of Parking Space Availability -- 12.2.3 System Design -- 12.2.4 Tools and Usage -- 12.2.5 Architecture -- 12.2.6 Implementation Techniques and Algorithms -- 12.2.7 Existing Methods and Workflow Model -- 12.2.8 Hyperparameter for OPSI -- 12.3 Potential Impact -- 12.3.1 Claims for the Accurate Detection of Fatigue -- 12.3.2 Similar Study and Results Analysis -- 12.4 Application and Results -- 12.4.1 Algorithm and Results -- 12.4.2 Implementation Using Python Modules -- 12.5 Discussion and Limitations -- 12.5.1 Discussion -- 12.5.2 Limitations -- 12.6 Future Work -- 12.6.1 Integration with Autonomous Vehicles -- 12.6.2 Real-Time Data Analysis -- 12.6.3 Integration with Smart Cities -- 12.7 Conclusion -- References -- Chapter 13 Graphical Password Authentication Using Python for Aerial Devices/Drones -- 13.1 Introduction -- 13.2 Literature Review -- 13.3 Methodology -- 13.4 A Brief Overview of a Drone and Authentication -- 13.4.1 Password Authentication -- 13.4.2 Types of Password Authentication Systems -- 13.4.3 Graphical Password Authentication -- 13.4.4 Advantages and Disadvantages of Graphical Passwords -- 13.5 Password Cracking -- 13.6 Data Analysis -- 13.7 Discussion -- 13.8 Conclusion and Future Scope -- References -- Chapter 14 A Study Centering on the Data and Processing for Remote Sensing Utilizing from Annoyed Aerial Vehicles -- 14.1 Introduction -- 14.2 An Acquisition Method for 3D Data Utilising Annoyed Aerial Vehicles -- 14.3 Background and Literature of Review -- 14.4 Research Gap -- 14.5 Methodology -- 14.6 Discussion -- 14.7 Conclusion -- References -- Chapter 15 Satellite Image Classification Using Convolutional Neural Network -- 15.1 Introduction -- 15.2 Literature Review -- 15.3 Objectives of this Research Work -- 15.3.1 Novelty of the Research Work -- 15.4 Description of the Dataset. 15.5 Theoretical Framework -- 15.6 Implementation and Results -- 15.6.1 Data Visualization -- 15.6.1.1 Class-Wise Data Count -- 15.6.1.2 Class-Wise Augmented Data Count -- 15.6.2 Implementation of MobileNetV3 -- 15.6.2.1 Visualization of a Sample of Training Images -- 15.6.2.2 Visualization of Executed Codes of MobileNetV3 -- 15.6.2.3 Training Results of MobileNetV3 -- 15.6.2.4 Classifications of Errors on Test Sets of MobileNetV3 -- 15.6.2.5 Confusion Matrix of MobileNetV3 -- 15.6.2.6 Classification Report of MobileNetV3 -- 15.6.3 Implementation of EfficientNetB0 -- 15.6.3.1 Visualization of a Sample of Training Images -- 15.6.3.2 Visualization of Executed Codes of EfficientNetB0 -- 15.6.3.3 Training Results of EfficientNetB0 -- 15.6.3.4 Classifications of Errors on Test Sets of EfficientNetB0 -- 15.6.3.5 Confusion Matrix of EfficientNetB0 -- 15.6.3.6 Classification Report of EfficientNetB0 -- 15.7 Conclusion and Future Scope -- References -- Chapter 16 Edge Computing in Aerial Imaging - A Research Perspective -- 16.1 Introduction -- 16.1.1 Edge Computing and Aerial Imaging -- 16.2 Research Applications of Aerial Imaging -- 16.2.1 Vehicle Imaging -- 16.2.2 Precision Agriculture -- 16.2.3 Environment Monitoring -- 16.2.4 Urban Planning and Development -- 16.2.5 Emergency Response -- 16.3 Edge Computing and Aerial Imaging -- 16.3.1 Research Perspective in Aerial Imaging -- 16.3.2 Edge Architectures -- 16.4 Comparative Analysis of the Aerial Imaging Algorithms and Architectures -- 16.5 Discussion -- 16.6 Conclusion -- References -- Chapter 17 Aerial Sensing and Imaging Analysis for Agriculture -- 17.1 Introduction -- 17.2 Experimental Methods and Techniques -- 17.3 Aerial Imaging and Sensing Applications in Agriculture -- 17.3.1 Assessing Yield and Fertilizer Response -- 17.3.2 Plant and Crop Farming -- 17.3.3 Soil and Field Analysis. 17.3.4 Weed Mapping and Management. |
| Record Nr. | UNINA-9910877032103321 |
| Hoboken, NJ : , : John Wiley & Sons, Inc. | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Drone technology in architecture, engineering and construction : a strategic guide to unmanned aerial vehicle operation and implementation / / Daniel Tal, Jon Altschuld
| Drone technology in architecture, engineering and construction : a strategic guide to unmanned aerial vehicle operation and implementation / / Daniel Tal, Jon Altschuld |
| Autore | Tal Daniel <1971-> |
| Pubbl/distr/stampa | Hoboken, NJ : , : Wiley, , [2021] |
| Descrizione fisica | 1 online resource (179 pages) |
| Disciplina | 620.00284 |
| Soggetto topico |
Aerial photography in geomorphology
Aerial photography in municipal engineering Drone aircraft in remote sensing Photogrammetry in architecture Micro air vehicles - Industrial applications Building sites - Location |
| ISBN |
1-119-54589-7
1-119-54590-0 1-119-60915-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | How to use this book -- A paradigm shift in viewing the world -- Drone data visualization as a full cycle tool -- Buy in -- Getting started -- Documentation, permissions and license -- Best practices for flying drones -- Imagery and videos -- Photogrammetry -- Working with 3D models -- The future of UAV's. |
| Record Nr. | UNINA-9910829871503321 |
Tal Daniel <1971->
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| Hoboken, NJ : , : Wiley, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Proceedings of UASG 2021 : wings 4 sustainability / / edited by Kamal Jain, Vishal Mishra, and Biswajeet Pradhan
| Proceedings of UASG 2021 : wings 4 sustainability / / edited by Kamal Jain, Vishal Mishra, and Biswajeet Pradhan |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, Springer Nature Switzerland AG, , [2023] |
| Descrizione fisica | 1 online resource (602 pages) |
| Disciplina | 623.7469 |
| Collana | Lecture Notes in Civil Engineering |
| Soggetto topico |
Drone aircraft in remote sensing
Geographic information systems |
| ISBN | 3-031-19309-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Comparison of DEM generated from UAV images and ICESat-1 Elevation Datasets with an assessment of the Cartographic Potential of UAV-based Sensor Datasets -- Chapter 2: UAV to Cadastral Parcel Boundary Translation and Synthetic UAV Image Generation Using Conditional-Generative Adversarial Network -- Chapter 3: UAV-based terrain-following mapping using LiDAR in high undulating catastrophic areas -- Chapter 4: Forest Fire Detection from UAV Images using Fusion of Pre-trained Mobile CNN Features -- Chapter 5: Deep Learning-based Improved Automatic Building Extraction from Open-Source High-Resolution Unmanned Aerial Vehicle (UAV) Imagery. |
| Record Nr. | UNINA-9910767556103321 |
| Cham, Switzerland : , : Springer, Springer Nature Switzerland AG, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Towards tree-level evapotranspiration estimation with small UAVs in precision agriculture / / Haoyu Niu and YangQuan Chen
| Towards tree-level evapotranspiration estimation with small UAVs in precision agriculture / / Haoyu Niu and YangQuan Chen |
| Autore | Niu Haoyu |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (176 pages) |
| Disciplina | 631 |
| Soggetto topico |
Precision farming
Evapotranspiration - Measurement Drone aircraft in remote sensing |
| ISBN | 3-031-14937-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgments -- Contents -- Acronyms -- List of Figures -- List of Tables -- 1 Introduction -- 1.1 What Is Evapotranspiration Estimation? -- 1.2 Challenges and Opportunities -- 1.3 Smart Big Data in Precision Agriculture: Acquisition and Advanced Analytics -- 1.3.1 What Is Smart Big Data in Precision Agriculture? -- 1.3.2 Plant Physiology-Informed Machine Learning: A New Frontier for Precision Agriculture -- 1.3.3 Big Data Acquisition and Advanced Analytics -- 1.3.4 Fractional Calculus (FC) and Fractional-Order Thinking (FOT) -- 1.3.5 Complexity and Inverse Power Laws (IPLs) -- 1.3.6 Heavy-Tailed Distributions -- 1.3.6.1 The Lévy Distribution -- 1.3.6.2 The Mittag-Leffler PDF -- 1.3.6.3 The Weibull Distribution -- 1.3.6.4 The Cauchy Distribution -- 1.3.6.5 The Pareto Distribution -- 1.3.6.6 The α-Stable Distribution -- 1.3.6.7 Mixture Distributions -- 1.3.6.8 The Gaussian Distribution -- 1.3.6.9 The Laplace Distribution -- 1.3.7 Big Data, Variability, and FC -- 1.3.7.1 The Hurst Parameter, fGn, and fBm -- 1.3.7.2 Fractional Lower-Order Moments (FLOMs) -- 1.3.7.3 Fractional Autoregressive Integrated Moving Average (FARIMA) and Gegenbauer Autoregressive Moving Average (GARMA) -- 1.3.7.4 Continuous-Time Random Walk (CTRW) -- 1.3.7.5 Unmanned Aerial Vehicles (UAVs) and Precision Agriculture -- 1.3.8 Optimal Machine Learning and Optimal Randomness -- 1.3.8.1 Derivative-Free Methods -- 1.3.8.2 Gradient-Based Methods -- 1.3.8.3 The Nesterov Accelerated Gradient Descent (NAGD) -- 1.4 Main Contributions -- 1.5 Book Organization -- 1.6 Results Reproducibility -- References -- 2 Small Unmanned Aerial Vehicles (UAVs) and Remote Sensing Payloads -- 2.1 The UAV Platform -- 2.2 Lightweight Sensors -- 2.2.1 RGB Camera -- 2.2.2 Multispectral Camera -- 2.2.3 Shortwave Infrared Camera -- 2.2.4 Thermal Camera.
2.3 UAV Image Acquisition and Processing -- 2.3.1 Flight Mission Design -- 2.3.2 UAV Image Processing -- 2.4 Challenges and Opportunities -- 2.4.1 UAVs -- 2.4.2 UAV Path Planning and Image Processing -- 2.4.3 Preflight Path Planning -- 2.4.4 Multispectral Image Calibration -- 2.4.5 Thermal Camera Calibration and Image Processing -- 2.4.6 Image Stitching and Orthomosaick Image Generation -- 2.5 Case Study I: A UAV Resolution and Waveband Aware Path Planning for Irrigation -- 2.5.1 Introduction -- 2.5.2 Material and Methods -- 2.5.2.1 Onion Study Area -- 2.5.2.2 A UAV Platform and Sensors -- 2.5.2.3 UAV Image Collection and Preprocessing -- 2.5.2.4 Principal Component Analysis -- 2.5.2.5 Linear Discriminant Analysis -- 2.5.3 Results and Discussion -- 2.5.3.1 UAV Flight Height or Resolution's Effect -- 2.5.3.2 Wavebands Configuration's Effect -- 2.5.4 Conclusions -- 2.6 Case Study II: A Detailed Study on Accuracy of Uncooled Thermal Cameras -- 2.6.1 Introduction -- 2.6.2 Material and Methods -- 2.6.2.1 Study Site -- 2.6.2.2 Image Collection -- 2.6.2.3 Groundtruth Data Collection -- 2.6.3 Results and Discussion -- 2.6.3.1 Experiment Setup -- 2.6.3.2 Thermal Camera Warm-Up Time -- 2.6.3.3 Calibration Experiment -- 2.6.3.4 The View Angle of Thermal Camera -- 2.6.3.5 The Effect of Stitching -- 2.6.4 Conclusions and Future Work -- 2.7 Case Study III: High Spatial Resolution Has Little Impact on NDVI Mean Value -- 2.7.1 Introduction -- 2.7.2 Material and Methods -- 2.7.2.1 The Study Site -- 2.7.2.2 The UAV and the Multispectral Sensor -- 2.7.2.3 Details of the UAV Imagery Dataset -- 2.7.3 Results and Discussion -- 2.7.3.1 The Relationship Between NDVI and UAV Flight Height -- 2.7.3.2 Individual Tree Canopy Segmentation Using Support Vector Machine (SVM) -- 2.7.3.3 Entropy of Individual Tree-Level NDVI Image -- 2.7.4 Conclusions and Future Work. 2.8 Chapter Summary -- References -- 3 ET Estimation Methods with Small UAVs: A Literature Review -- 3.1 Introduction -- 3.2 Related Work -- 3.2.1 One-Source Energy Balance (OSEB) -- 3.2.2 High-Resolution Mapping of ET (HRMET) -- 3.2.3 Machine Learning (ML) and Artificial Neural Networks (ANN) -- 3.2.4 Two-Source Energy Balance (TSEB) Models -- 3.2.5 Dual-Temperature-Difference (DTD) Model -- 3.2.6 Surface Energy Balance Algorithm for Land (SEBAL) -- 3.2.7 Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) -- 3.3 Existing ET Estimation Methods with UAVs: Results and Discussion -- 3.3.1 OSEB and TSEB Models -- 3.3.2 HARMET Model -- 3.3.3 Machine Learning and Neural Networks -- 3.3.4 TSEB and DTD Models -- 3.3.5 TSEB and DATTUTDUT Models -- 3.3.6 SEBAL Model -- 3.3.7 METRIC and METRIC-HR Models -- 3.4 Chapter Summary -- References -- 4 Estimating ET Using Stochastic Configuration Network and UAV-Based Crop Coefficients -- 4.1 Introduction -- 4.2 Material and Methods -- 4.2.1 Pomegranate Study Area -- 4.2.2 The UAV Platform and Multispectral Camera -- 4.2.3 UAV Image Collection and Preprocessing -- 4.2.4 Deep Stochastic Configuration Networks (DeepSCNs) -- 4.3 Results and Discussion -- 4.3.1 Seasonal Kc and NDVI -- 4.3.2 Regression Models for Kc and NDVI -- 4.4 Conclusions -- 4.5 Case Study: Optimal Randomness for SCN with Heavy-Tailed Distributions -- 4.5.1 Introduction -- 4.5.2 SCN with Heavy-Tailed PDFs -- 4.5.3 A Regression Model and Parameter Tuning -- 4.5.3.1 Performance Comparison Among SCNs with Heavy-Tailed PDFs -- 4.5.4 MNIST Handwritten Digit Classification -- 4.5.4.1 Performance Comparison Among SCNs on MNIST -- 4.6 Chapter Summary -- References -- 5 Reliable Tree-Level ET Estimation Using Lysimeter and UAV Multispectral Imagery -- 5.1 Introduction -- 5.2 Material and Methods -- 5.2.1 Study Site Description. 5.2.2 UAV Image Collection and Processing -- 5.3 Results and Discussion -- 5.3.1 Determination of Individual Tree Kc from NDVI -- 5.3.2 The Spatial Variability Mapping of Kc and ETc -- 5.3.3 Performance of the Individual Tree-Level ET Estimation -- 5.3.4 Conclusion -- 5.4 Chapter Summary -- References -- 6 Tree-Level Water Status Inference Using UAV Thermal Imagery and Machine Learning -- 6.1 Introduction -- 6.2 Material and Methods -- 6.2.1 Experimental Site and Irrigation Management -- 6.2.2 Ground Truth: Infrared Canopy and Air Temperature -- 6.2.3 Thermal Infrared Remote Sensing Data -- 6.2.3.1 UAV Thermal Image Collection and Processing -- 6.2.3.2 Tree Canopy Segmentation Using Support Vector Machine (SVM) -- 6.2.4 Complexity-Informed Machine Learning (CIML) -- 6.2.5 Principle of Tail Matching -- 6.2.5.1 Pareto Distribution -- 6.2.6 Machine Learning Classification Algorithms -- 6.2.7 Image Preprocessing for the CNN Model -- 6.3 Results and Discussion -- 6.3.1 Comparison of Canopy Temperature Per Tree Based on Ground Truth and UAV Thermal Imagery -- 6.3.2 The Relationship Between ΔT and Irrigation Treatment -- 6.3.3 The Classification Performance of CIML on Irrigation Treatment Levels -- 6.3.4 The Performance of the CNN Model -- 6.4 Conclusion and Future Research -- 6.5 Chapter Summary -- References -- 7 Conclusion and Future Research -- 7.1 Conclusions -- 7.2 Future Research -- References -- Index. |
| Record Nr. | UNINA-9910624384403321 |
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Niu Haoyu
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| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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