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Smart Big Data in Digital Agriculture Applications : Acquisition, Advanced Analytics, and Plant Physiology-Informed Artificial Intelligence
Smart Big Data in Digital Agriculture Applications : Acquisition, Advanced Analytics, and Plant Physiology-Informed Artificial Intelligence
Autore Niu Haoyu
Edizione [1st ed.]
Pubbl/distr/stampa Cham : , : Springer, , 2024
Descrizione fisica 1 online resource (243 pages)
Altri autori (Persone) ChenYangQuan
Collana Agriculture Automation and Control Series
ISBN 3-031-52645-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Acknowledgments -- Contents -- Acronyms -- Part I Why Big Data Is Not Smart Yet? -- 1 Introduction -- 1.1 Motivation -- 1.1.1 What Is Smart Big Data in Digital Agriculture? -- 1.1.2 Plant Physiology-Informed Artificial Intelligence: A New Frontier -- 1.1.3 Big Data Acquisition and Advanced Analytics -- 1.2 The Book Objectives and Methods -- 1.3 Book Contributions -- 1.4 The Book Outline -- References -- 2 Why Do Big Data and Machine Learning Entail the Fractional Dynamics? -- 2.1 Fractional Calculus (FC) and Fractional-Order Thinking (FOT) -- 2.2 Complexity and Inverse Power Laws (IPLs) -- 2.3 Heavy-Tailed Distributions -- 2.3.1 Lévy Distribution -- 2.3.2 Mittag-Leffler Distribution -- 2.3.3 Weibull Distribution -- 2.3.4 Cauchy Distribution -- 2.3.5 Pareto Distribution -- 2.3.6 The α-Stable Distribution -- 2.3.7 Mixture Distributions -- 2.3.7.1 Gaussian Distribution -- 2.3.7.2 Laplace Distribution -- 2.4 Big Data, Variability, and FC -- 2.4.1 Hurst Parameter, fGn, and fBm -- 2.4.2 Fractional Lower-Order Moments (FLOMs) -- 2.4.3 Fractional Autoregressive Integrated Moving Average (FARIMA) and Gegenbauer Autoregressive Moving Average (GARMA) -- 2.4.4 Continuous-Time Random Walk (CTRW) -- 2.4.5 Unmanned Aerial Vehicles (UAVs) and Digital Agriculture -- 2.5 Optimal Machine Learning and Optimal Randomness -- 2.5.1 Derivative-Free Methods -- 2.5.2 The Gradient-Based Methods -- 2.5.2.1 Nesterov Accelerated Gradient Descent (NAGD) -- 2.6 What Can the Control Community Offer to ML? -- 2.7 Case Study: Optimal Randomness for Stochastic Configuration Network (SCN) with Heavy-Tailed Distributions -- 2.7.1 Introduction -- 2.7.2 SCN with Heavy-Tailed PDFs -- 2.7.3 A Regression Model and Parameter Tuning -- 2.7.3.1 Performance Comparison Among SCNs with Heavy-Tailed PDFs -- 2.7.4 MNIST Handwritten Digit Classification.
2.7.4.1 Performance Comparison Among SCNs on MNIST -- 2.8 Chapter Summary -- References -- Part II Smart Big Data Acquisition Platforms -- 3 Small Unmanned Aerial Vehicles (UAVs) and Remote Sensing Payloads -- 3.1 The UAV Platform -- 3.2 Lightweight Sensors -- 3.2.1 RGB Camera -- 3.2.2 Multispectral Camera -- 3.2.3 The Short Wave Infrared Camera -- 3.2.4 Thermal Camera -- 3.3 UAV Image Acquisition and Processing -- 3.3.1 Flight Mission Design -- 3.3.2 UAV Image Processing -- 3.4 Challenges and Opportunities -- 3.4.1 UAVs -- 3.4.2 UAV Path Planning and Image Processing -- 3.4.3 Pre-flight Path Planning -- 3.4.4 Multispectral Image Calibration -- 3.4.5 Thermal Camera Calibration and Image Processing -- 3.4.6 Images Stitching and Orthomosaic Image Generation -- 3.5 Case Study: High Spatial Resolution Has Little Impact on NDVI Mean Value of UAV-Based Individual Tree-level Mapping: Evidence from Nine Field Tests and Implications -- 3.5.1 Introduction -- 3.5.2 Material and Methods -- 3.5.2.1 The Study Site -- 3.5.2.2 The UAV and the Multispectral Sensor -- 3.5.2.3 Details of the UAV Imagery Dataset -- 3.5.3 Results and Discussion -- 3.5.3.1 The Relationship Between NDVI and UAV Flight Height -- 3.5.3.2 Individual Tree Canopy Segmentation Using Support Vector Machine (SVM) -- 3.5.3.3 Entropy of Individual Tree-level NDVI Image -- 3.5.4 Conclusions and Future Work -- 3.6 Chapter Summary -- References -- 4 The Edge-AI Sensors and Internet of Living Things (IoLT) -- 4.1 Introduction -- 4.2 Proximate Sensors -- 4.2.1 The Spectrometer -- 4.2.2 A Pocket-Sized Spectrometer -- 4.2.3 A Microwave Radio Frequency 3D Sensor -- 4.3 Case Study: Onion Irrigation Treatment Inference Using a Low-Cost Edge-AI Sensor -- 4.3.1 Introduction -- 4.3.2 Materials and Methods -- 4.3.2.1 Onion Study Site -- 4.3.2.2 The Spectrometer Scio -- 4.3.2.3 Field Measurement Collection.
4.3.2.4 Principal Component Analysis -- 4.3.2.5 Linear Discriminant Analysis -- 4.3.2.6 Multi-layer Perceptron Classifier -- 4.3.3 Results and Discussion -- 4.3.3.1 Results Using PCA-Based Classifiers -- 4.3.3.2 Results Using LDA-Based Classifiers -- 4.3.3.3 Results Using MLP -- 4.3.4 Conclusion and Future Work -- 4.4 Chapter Summary -- References -- 5 The Unmanned Ground Vehicles (UGVs) for Digital Agriculture -- 5.1 Introduction -- 5.2 UGV as Data Acquisition Platform -- 5.2.1 Fundamental Research Questions -- 5.2.2 Low Barriers to Entry -- 5.2.3 Cognitive Algorithms by Deep Learning -- 5.2.4 Swarming Mechanism of UGVs -- 5.3 Case Study: Build a UGV Platform for Agricultural Research from a Low-Cost Toy Vehicle -- 5.3.1 Introduction -- 5.4 Chapter Summary -- References -- Part III Advanced Big Data Analytics, Plant Physiology-Informed Machine Learning, and Fractional-Order Thinking -- 6 Fundamentals of Big Data, Machine Learning, and Computer Vision Workflow -- 6.1 Introduction -- 6.2 A Fundamental Tutorial: Cotton Water Stress Classification with CNN -- 6.2.1 Data Loading -- 6.2.2 Data Preprocessing -- 6.2.3 Train and Test Split -- 6.2.4 Creating the Model -- 6.2.5 The Model Performance Evaluation -- 6.3 Chapter Summary -- References -- 7 A Low-Cost Proximate Sensing Method for Early Detection of Nematodes in Walnut Using Machine Learning Algorithms -- 7.1 Introduction -- 7.2 Materials and Methods -- 7.2.1 Study Area -- 7.2.2 Reflectance Measurements with a Radio Frequency Sensor -- 7.2.3 Ground Truth Data Collection and Processing -- 7.2.4 Scikit-Learn Classification Algorithms -- 7.2.5 Deep Neural Networks (DNNs) and TensorFlow -- 7.3 Results and Discussion -- 7.3.1 Data Visualization (Project 45, 2019) -- 7.3.2 Performance of Classifiers (Project 45, 2019) -- 7.3.3 Performance of Classifiers (Project 45, 2020) -- 7.4 Chapter Summary.
References -- 8 Tree-Level Evapotranspiration Estimation of Pomegranate Trees Using Lysimeter and UAV Multispectral Imagery -- 8.1 Introduction -- 8.2 Materials and Methods -- 8.2.1 Study Site Description -- 8.2.2 UAV Image Collection and Processing -- 8.3 Results and Discussion -- 8.3.1 Determination of Individual Tree Kc from NDVI -- 8.3.2 The Spatial Variability Mapping of Kc and ETc -- 8.3.3 Performance of the Individual Tree-LevelET Estimation -- 8.4 Conclusion -- 8.5 Summary -- References -- 9 Individual Tree-Level Water Status Inference Using High-Resolution UAV Thermal Imagery and Complexity-Informed Machine Learning -- 9.1 Introduction -- 9.2 Materials and Methods -- 9.2.1 Experimental Site and Irrigation Management -- 9.2.2 Ground Truth: Infrared Canopy and Air Temperature -- 9.2.3 Thermal Infrared Remote Sensing Data -- 9.2.3.1 UAV Thermal Image Collection and Processing -- 9.2.3.2 Tree Canopy Segmentation Using Support Vector Machine (SVM) -- 9.2.4 Complexity-Informed Machine Learning (CIML) -- 9.2.5 Principle of Tail Matching -- 9.2.5.1 Pareto Distribution -- 9.2.6 Machine Learning Classification Algorithms -- 9.3 Results and Discussion -- 9.3.1 Comparison of Canopy Temperature Per Tree Based on Ground Truth and UAV Thermal Imagery -- 9.3.2 The Relationship Between ΔT and IrrigationTreatment -- 9.3.3 The Classification Performance of CIML on Irrigation Treatment Levels -- 9.4 Summary -- References -- 10 Scale-Aware Pomegranate Yield Prediction Using UAV Imagery and Machine Learning -- 10.1 Introduction -- 10.2 Materials and Methods -- 10.2.1 Experimental Field and Ground Data Collection -- 10.2.2 UAV Platform and Imagery Data Acquisition -- 10.2.3 UAV Image Feature Extraction -- 10.2.3.1 The Normalized Difference Vegetation Index (NDVI) -- 10.2.3.2 The Green Normalized Difference Vegetation Index (GNDVI).
10.2.3.3 The RedEdge Normalized Difference Vegetation Index (NDVIre) -- 10.2.3.4 The RedEdge Triangulated Vegetation Index (RTVIcore) -- 10.2.3.5 The Modified Triangular Vegetation Index (MTVI2) -- 10.2.3.6 The Green Chlorophyll Index (CIg) -- 10.2.3.7 The RedEdge Chlorophyll Index (CIre) -- 10.2.4 The Machine Learning Methods -- 10.3 Results and Discussion -- 10.3.1 The Pomegranate Yield Performance in 2019 -- 10.3.2 The Correlation Between the Image Features and Pomegranate Yield -- 10.3.3 The ML Algorithm Performance on Yield Estimation -- 10.4 Summary -- References -- Part IV Towards Smart Big Data in Digital Agriculture -- 11 Intelligent Bugs Mapping and Wiping (iBMW): An Affordable Robot-Driven Robot for Farmers -- 11.1 Introduction -- 11.2 Existing Solutions -- 11.3 iBMW Innovation -- 11.3.1 Cognitive of Pest Population Mapping and Wiping -- 11.3.2 iBMW with TurtleBot 3 as ``Brain'' -- 11.3.3 Real-Time Vision Processing -- 11.3.4 Optimal Path Planning Enabled by iBMW -- 11.3.5 Ethical, Cultural, and Legal Matters -- 11.4 Measuring Success -- 11.4.1 NOW Population Temporal and Spatial Distribution -- 11.4.2 The Amount of Pesticide Being Used -- 11.4.3 The Target Trees' Almond Yield -- 11.5 Summary -- References -- 12 A Non-invasive Stem Water Potential Monitoring Method Using Proximate Sensor and Machine Learning Classification Algorithms -- 12.1 Introduction -- 12.2 Materials and Methods -- 12.2.1 Walnut Study Area -- 12.2.2 Reflectance Measurements with a Radio Frequency Sensor -- 12.2.3 Data Collection and Processing -- 12.2.4 Scikit-Learn Classification Algorithms -- 12.3 Results and Discussion -- 12.4 Summary -- References -- 13 A Low-Cost Soil Moisture Monitoring Method by Using Walabot and Machine Learning Algorithms -- 13.1 Introduction -- 13.2 Materials and Methods -- 13.2.1 The Study Site -- 13.2.2 The Proximate Sensor.
13.2.3 Experiment Setup.
Record Nr. UNINA-9910842292703321
Niu Haoyu  
Cham : , : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
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
Niu Haoyu  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui