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Biblioteca

MARC Lista (tabellare)
Green, Closed Loop, Circular Bio-Economy
Green, Closed Loop, Circular Bio-Economy
Autore Bochtis Dionysis
Pubbl/distr/stampa Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
Descrizione fisica 1 electronic resource (288 p.)
Soggetto topico Economic history
Soggetto non controllato bioeconomy
survey
strategies
research program
biogas
lignocellulose
microalgae
agricultural sustainability
sustainability assessment
review
ammonia loss
land application
manure management
irrigation
biofuels
spatial difference-in-difference
corn markets
climate change adaptation
transformative adaptation
limits to adaptation
adaptation barrier
fuzzy cognitive maps
resilience
sustainability
vulnerability
Sundarbans
circular economy
sustainable socio-economic development
quality of life
poverty alleviation
participatory modelling
ordered weighted averaging
aggregation
reflectance spectroscopy
soil spectral libraries
VNIR-SWIR
soil organic matter
carbon sequestration
forestry
wood
non-wood forest products
developing world
rural electrification
Sub-Saharan Africa
energy
agriculture
machine learning
artificial neural networks
natural gas
demand forecasting
indicators
investments' sustainability
multi-criteria analysis
decision support
ELECTRE III
coronavirus
occupational health and safety
food security
control measures
systemic design
rice
wine
value chains
by-products
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910557153603321
Bochtis Dionysis  
Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Information and communication technologies for agriculture . Theme III, : decision / / Dionysis D. Bochtis [and four others], editors
Information and communication technologies for agriculture . Theme III, : decision / / Dionysis D. Bochtis [and four others], editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (303 pages)
Disciplina 338.10285
Collana Springer Optimization and its Applications
Soggetto topico Agriculture - Decision making
Agriculture - Decision making - Methodology
Enginyeria agronòmica
Agricultura
Presa de decisions
Soggetto genere / forma Llibres electrònics
ISBN 3-030-84152-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- Part I: Value Chain -- Agricultural Information Model -- 1 Introduction -- 2 Related Work -- 3 Technical Requirements -- 3.1 Core Data Modeling Requirements -- 3.2 Semantic Interoperability Requirements -- 4 AIM Design -- 4.1 Meta-model Layer -- 4.2 Cross-Domain Layer -- Cross-Domain Integration Process -- 4.3 Domain Layer -- Domain Layer Requirements -- AIM Domain-Specific Ontologies -- 5 Semantic Interoperability -- 6 Implementation -- 6.1 Meta-model Implementation -- 6.2 Cross-Domain Implementation -- 6.3 Domain-Specific Implementation -- 7 Methodology for Profiles -- 8 Exemplary Use Cases -- 9 Conclusions and Future Work -- References -- Development of a Framework for Implementing o- on the Beef Cattle Value Chain -- 1 Introduction -- 2 Related Work -- 2.1 Frameworks for IoT in Agri-food Value Chains -- 2.2 General Frameworks for IoT and the IoT-A -- 3 Methodology -- 4 Results -- 4.1 Overview of the Beef Cattle Value Chain -- 4.2 Requirements and Services Identification -- 4.3 IoT-A for the Beef Cattle Value Chain -- 5 Discussion -- 6 Conclusions -- References -- Food Business Information Systems in Western Greece -- 1 Introduction -- 2 Literature Review -- 2.1 Studies from 1990 to 2000 -- 2.2 Studies from 2001 to 2005 -- 2.3 Studies from 2006 to 2010 -- 2.4 Studies from 2011 to 2015 -- 2.5 Studies from 2016 Until Today -- 3 Methodology -- 4 Results -- 4.1 Adoption of Human Resources Information Systems -- 4.2 Adoption of Accounting and Financial Information System -- 4.3 Adoption of Sales and Marketing Information Systems -- 4.4 Adoption of Operational Information Systems -- 4.5 Adoption of Production Information Systems -- 4.6 Analysis of Software Packages Applications in Food Businesses of Western Greece -- 5 Conclusions -- References -- Part II: Primary Production.
From Precision Agriculture to Agriculture 4.0: Integrating ICT in Farming -- 1 Introduction -- 2 Agriculture 4.0 Constituents -- 2.1 Internet of Things (IoT) -- 2.2 Artificial Intelligence (AI) -- 2.3 Machine Learning (ML) -- 2.4 Big Data Analytics -- 2.5 Wireless Sensor Networks (WSN) -- 2.6 Blockchain -- 2.7 Cloud Computing -- 2.8 Automated Guided Vehicles -- 2.9 5G Technology -- 3 Discussion -- References -- On the Routing of Unmanned Aerial Vehicles (UAVs) in Precision Farming Sampling Missions -- 1 Introduction -- 2 Types of UAVs and Their Use -- 3 UAVs Applications in Precision Agriculture -- 4 UAVs Route Planning -- 5 Algorithms for Solving TSP -- 5.1 Exact Algorithms -- Dynamic Programming -- Branch-and-Bound Algorithms -- Branch-and-Cut Algorithms -- 5.2 Algorithms for Sub-optimal Solutions -- Approximation Algorithms -- Christofides-Serdyukov Algorithm -- Heuristics -- Nearest Neighbor Algorithm -- Multiple Fragment Algorithm -- k-Opt or Lin-Kernighan Heuristics -- Metaheuristics -- Genetic Algorithms -- Ant Colony Optimization -- 6 Demonstration of UAVs Routing in Agriculture -- 6.1 Single TSP (sTSP) -- 6.2 Multiple TSP (mTSP) (Without a Fixed Depot) -- 6.3 Multiple TSP, Single (Fixed) Depot (mTSPsD) -- 6.4 Multiple TSP, Multiple (Fixed) Depots (mTSPmD) -- 6.5 Multiple TSP, Multiple (Fixed) Depots and Constrained Travelling Distance (mTSPmDcT) -- 7 Conclusions -- References -- 3D Scenery Construction of Agricultural Environments for Robotics Awareness -- 1 Introduction -- 1.1 Depth Cameras -- 2 Point Cloud Processing and Digitalization -- 2.1 3D Mapping -- 2.2 Digital Twin -- 2.3 Simulation Environments -- 2.4 Aim of This Chapter -- 3 Demonstrative Scenario: An In-field Application -- 3.1 Point Cloud Data Acquisition -- 3.2 Point Cloud Data Processing -- 3.3 Orchard´s Simulation Environment -- 4 Conclusions -- References.
A Weed Control Unmanned Ground Vehicle Prototype for Precision Farming Activities: The Case of Red Rice -- 1 Introduction -- 2 Literature Review -- 3 Materials and Methods -- 3.1 Research Design -- 3.2 Case Study -- 4 Robot Prototype Development -- 4.1 Rod Mechanism -- 4.2 Autonomous Vehicle -- 5 Results -- 5.1 Simulation Environment -- 5.2 Real-World Environment -- 6 Conclusions -- References -- Decision-Making and Decision Support System for a Successful Weed Management -- 1 Introduction -- 1.1 The Introduction of Decision Support Systems (DSS) in Agriculture -- 1.2 The Development of DSSs in Terms of Weed Management -- 2 Factors Affecting Decision-Making Process in DSSs for Weed Management -- 2.1 Weed Emergence and Weed Flora Composition in the Field -- 2.2 The Impact of Weed Competition on Crops´ Productivity -- 3 Factors Affecting Decision-Making Either in the Short- or in the Long-Term Period and Future Challenges of DSSs Developed fo... -- 4 Conclusion -- References -- Zephyrus: Grain Aeration Strategy Based on the Prediction of Temperature and Moisture Fronts -- 1 Introduction -- 2 Methodology -- 2.1 Theory Basis of Zephyrus Control Strategy -- 2.2 Description of Zephyrus Control Strategy -- 2.3 Experimental Evaluation of Zephyrus Control Strategy -- 2.4 Comparison of Zephyrus with Other Aeration Controllers -- 3 Results and Discussion -- 3.1 Experimental Evaluation of Zephyrus Control Strategy -- 3.2 Comparison of Zephyrus with Other Aeration Controllers -- 4 Conclusions -- References -- Decision-Making Applications on Smart Livestock Farming -- 1 Smart Livestock Farming -- 1.1 Concepts and Fundamentals -- 1.2 Smart Livestock Farming Models Implemented On-farm Actions -- Pig Production -- Poultry Production -- Dairy and Beef Production -- 2 Tools for Implementing Decision-Making Applications in Smart Livestock Farming.
2.1 Paraconsistent Logic Applications -- Applications -- Poultry Production -- 2.2 Pig production -- 2.3 Use of Machine Learning on Livestock Production -- Applications -- Dairy Production -- Poultry Production -- Pig Production -- 2.4 Technical Challenges -- 3 Final Remarks -- References -- Part III: Environment -- Programmable Process Structures of Unified Elements for Model-Based Planning and Operation of Complex Agri-environmental Proce... -- 1 Introduction -- 1.1 Functionality Modeling of Complex Process Systems -- 1.2 Structural Modeling of Complex Systems -- 2 Methodology -- 3 Results and Discussion -- 3.1 Recirculation Aquaculture System -- Challenge -- Experimental Unit -- Conceptual Model -- PPS Implementation of the Model -- Validation of a Pilot Experiment -- Study of a Complete Fish Grading Process -- Simulation-Based Design -- Experiences About the Applied Methodology -- 3.2 Ecosystem-Involved Fishpond -- Challenge -- Investigated Production Site -- Conceptual Model -- PPS Implementation of the Model -- Validation of the Model -- Simulation of Various Managerial Strategies -- Effect of Climate Change on Production of Fishpond -- Experiences About the Applied Methodology -- 3.3 Agroforestry Site -- Challenge -- Experimental Site -- Conceptual Model -- PPS Implementation of the Model -- Illustration of Simulation-Based Analysis -- Experiences About the Applied Methodology -- 4 Concluding Discussion -- References -- Monitoring and Estimation of Sugarcane Burning in the Middle Paranapanema Basin, Brazil, Using Linear Mixed Models -- 1 Introduction -- 2 Material and Methods -- 2.1 Topographic Survey -- 2.2 Statistical Modeling -- 3 Results and Discussion -- 4 Conclusions -- References -- A Decision Support System for Green Crop Fertilization Planning -- 1 Introduction -- 2 System Description -- 3 Case Study Demonstration.
3.1 The Demonstrated Crops -- 3.2 Fertilization Scenario -- 3.3 Input Parameters -- 4 Results -- 5 Discussion -- 6 Conclusions -- References -- Knowledge Elicitation and Modeling of Agroecological Management Strategies -- 1 Introduction -- 2 Agroecological Farm Management -- 3 Developing a Farm-Management Model -- 4 Decision-Relevant Concepts -- 4.1 Activities, Operations, and Resources -- 4.2 Goals and Plans -- 4.3 Preferences and Priorities -- 4.4 Events and Reactions -- 5 Example of an Agroecological Management Strategy -- 6 Discussion and Conclusion -- References.
Record Nr. UNISA-996472036703316
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Information and communication technologies for agriculture . Theme III, : decision / / Dionysis D. Bochtis [and four others], editors
Information and communication technologies for agriculture . Theme III, : decision / / Dionysis D. Bochtis [and four others], editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (303 pages)
Disciplina 338.10285
Collana Springer Optimization and its Applications
Soggetto topico Agriculture - Decision making
Agriculture - Decision making - Methodology
Enginyeria agronòmica
Agricultura
Presa de decisions
Soggetto genere / forma Llibres electrònics
ISBN 3-030-84152-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- Part I: Value Chain -- Agricultural Information Model -- 1 Introduction -- 2 Related Work -- 3 Technical Requirements -- 3.1 Core Data Modeling Requirements -- 3.2 Semantic Interoperability Requirements -- 4 AIM Design -- 4.1 Meta-model Layer -- 4.2 Cross-Domain Layer -- Cross-Domain Integration Process -- 4.3 Domain Layer -- Domain Layer Requirements -- AIM Domain-Specific Ontologies -- 5 Semantic Interoperability -- 6 Implementation -- 6.1 Meta-model Implementation -- 6.2 Cross-Domain Implementation -- 6.3 Domain-Specific Implementation -- 7 Methodology for Profiles -- 8 Exemplary Use Cases -- 9 Conclusions and Future Work -- References -- Development of a Framework for Implementing o- on the Beef Cattle Value Chain -- 1 Introduction -- 2 Related Work -- 2.1 Frameworks for IoT in Agri-food Value Chains -- 2.2 General Frameworks for IoT and the IoT-A -- 3 Methodology -- 4 Results -- 4.1 Overview of the Beef Cattle Value Chain -- 4.2 Requirements and Services Identification -- 4.3 IoT-A for the Beef Cattle Value Chain -- 5 Discussion -- 6 Conclusions -- References -- Food Business Information Systems in Western Greece -- 1 Introduction -- 2 Literature Review -- 2.1 Studies from 1990 to 2000 -- 2.2 Studies from 2001 to 2005 -- 2.3 Studies from 2006 to 2010 -- 2.4 Studies from 2011 to 2015 -- 2.5 Studies from 2016 Until Today -- 3 Methodology -- 4 Results -- 4.1 Adoption of Human Resources Information Systems -- 4.2 Adoption of Accounting and Financial Information System -- 4.3 Adoption of Sales and Marketing Information Systems -- 4.4 Adoption of Operational Information Systems -- 4.5 Adoption of Production Information Systems -- 4.6 Analysis of Software Packages Applications in Food Businesses of Western Greece -- 5 Conclusions -- References -- Part II: Primary Production.
From Precision Agriculture to Agriculture 4.0: Integrating ICT in Farming -- 1 Introduction -- 2 Agriculture 4.0 Constituents -- 2.1 Internet of Things (IoT) -- 2.2 Artificial Intelligence (AI) -- 2.3 Machine Learning (ML) -- 2.4 Big Data Analytics -- 2.5 Wireless Sensor Networks (WSN) -- 2.6 Blockchain -- 2.7 Cloud Computing -- 2.8 Automated Guided Vehicles -- 2.9 5G Technology -- 3 Discussion -- References -- On the Routing of Unmanned Aerial Vehicles (UAVs) in Precision Farming Sampling Missions -- 1 Introduction -- 2 Types of UAVs and Their Use -- 3 UAVs Applications in Precision Agriculture -- 4 UAVs Route Planning -- 5 Algorithms for Solving TSP -- 5.1 Exact Algorithms -- Dynamic Programming -- Branch-and-Bound Algorithms -- Branch-and-Cut Algorithms -- 5.2 Algorithms for Sub-optimal Solutions -- Approximation Algorithms -- Christofides-Serdyukov Algorithm -- Heuristics -- Nearest Neighbor Algorithm -- Multiple Fragment Algorithm -- k-Opt or Lin-Kernighan Heuristics -- Metaheuristics -- Genetic Algorithms -- Ant Colony Optimization -- 6 Demonstration of UAVs Routing in Agriculture -- 6.1 Single TSP (sTSP) -- 6.2 Multiple TSP (mTSP) (Without a Fixed Depot) -- 6.3 Multiple TSP, Single (Fixed) Depot (mTSPsD) -- 6.4 Multiple TSP, Multiple (Fixed) Depots (mTSPmD) -- 6.5 Multiple TSP, Multiple (Fixed) Depots and Constrained Travelling Distance (mTSPmDcT) -- 7 Conclusions -- References -- 3D Scenery Construction of Agricultural Environments for Robotics Awareness -- 1 Introduction -- 1.1 Depth Cameras -- 2 Point Cloud Processing and Digitalization -- 2.1 3D Mapping -- 2.2 Digital Twin -- 2.3 Simulation Environments -- 2.4 Aim of This Chapter -- 3 Demonstrative Scenario: An In-field Application -- 3.1 Point Cloud Data Acquisition -- 3.2 Point Cloud Data Processing -- 3.3 Orchard´s Simulation Environment -- 4 Conclusions -- References.
A Weed Control Unmanned Ground Vehicle Prototype for Precision Farming Activities: The Case of Red Rice -- 1 Introduction -- 2 Literature Review -- 3 Materials and Methods -- 3.1 Research Design -- 3.2 Case Study -- 4 Robot Prototype Development -- 4.1 Rod Mechanism -- 4.2 Autonomous Vehicle -- 5 Results -- 5.1 Simulation Environment -- 5.2 Real-World Environment -- 6 Conclusions -- References -- Decision-Making and Decision Support System for a Successful Weed Management -- 1 Introduction -- 1.1 The Introduction of Decision Support Systems (DSS) in Agriculture -- 1.2 The Development of DSSs in Terms of Weed Management -- 2 Factors Affecting Decision-Making Process in DSSs for Weed Management -- 2.1 Weed Emergence and Weed Flora Composition in the Field -- 2.2 The Impact of Weed Competition on Crops´ Productivity -- 3 Factors Affecting Decision-Making Either in the Short- or in the Long-Term Period and Future Challenges of DSSs Developed fo... -- 4 Conclusion -- References -- Zephyrus: Grain Aeration Strategy Based on the Prediction of Temperature and Moisture Fronts -- 1 Introduction -- 2 Methodology -- 2.1 Theory Basis of Zephyrus Control Strategy -- 2.2 Description of Zephyrus Control Strategy -- 2.3 Experimental Evaluation of Zephyrus Control Strategy -- 2.4 Comparison of Zephyrus with Other Aeration Controllers -- 3 Results and Discussion -- 3.1 Experimental Evaluation of Zephyrus Control Strategy -- 3.2 Comparison of Zephyrus with Other Aeration Controllers -- 4 Conclusions -- References -- Decision-Making Applications on Smart Livestock Farming -- 1 Smart Livestock Farming -- 1.1 Concepts and Fundamentals -- 1.2 Smart Livestock Farming Models Implemented On-farm Actions -- Pig Production -- Poultry Production -- Dairy and Beef Production -- 2 Tools for Implementing Decision-Making Applications in Smart Livestock Farming.
2.1 Paraconsistent Logic Applications -- Applications -- Poultry Production -- 2.2 Pig production -- 2.3 Use of Machine Learning on Livestock Production -- Applications -- Dairy Production -- Poultry Production -- Pig Production -- 2.4 Technical Challenges -- 3 Final Remarks -- References -- Part III: Environment -- Programmable Process Structures of Unified Elements for Model-Based Planning and Operation of Complex Agri-environmental Proce... -- 1 Introduction -- 1.1 Functionality Modeling of Complex Process Systems -- 1.2 Structural Modeling of Complex Systems -- 2 Methodology -- 3 Results and Discussion -- 3.1 Recirculation Aquaculture System -- Challenge -- Experimental Unit -- Conceptual Model -- PPS Implementation of the Model -- Validation of a Pilot Experiment -- Study of a Complete Fish Grading Process -- Simulation-Based Design -- Experiences About the Applied Methodology -- 3.2 Ecosystem-Involved Fishpond -- Challenge -- Investigated Production Site -- Conceptual Model -- PPS Implementation of the Model -- Validation of the Model -- Simulation of Various Managerial Strategies -- Effect of Climate Change on Production of Fishpond -- Experiences About the Applied Methodology -- 3.3 Agroforestry Site -- Challenge -- Experimental Site -- Conceptual Model -- PPS Implementation of the Model -- Illustration of Simulation-Based Analysis -- Experiences About the Applied Methodology -- 4 Concluding Discussion -- References -- Monitoring and Estimation of Sugarcane Burning in the Middle Paranapanema Basin, Brazil, Using Linear Mixed Models -- 1 Introduction -- 2 Material and Methods -- 2.1 Topographic Survey -- 2.2 Statistical Modeling -- 3 Results and Discussion -- 4 Conclusions -- References -- A Decision Support System for Green Crop Fertilization Planning -- 1 Introduction -- 2 System Description -- 3 Case Study Demonstration.
3.1 The Demonstrated Crops -- 3.2 Fertilization Scenario -- 3.3 Input Parameters -- 4 Results -- 5 Discussion -- 6 Conclusions -- References -- Knowledge Elicitation and Modeling of Agroecological Management Strategies -- 1 Introduction -- 2 Agroecological Farm Management -- 3 Developing a Farm-Management Model -- 4 Decision-Relevant Concepts -- 4.1 Activities, Operations, and Resources -- 4.2 Goals and Plans -- 4.3 Preferences and Priorities -- 4.4 Events and Reactions -- 5 Example of an Agroecological Management Strategy -- 6 Discussion and Conclusion -- References.
Record Nr. UNINA-9910568296603321
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Information and communication technologies for agriculture - Theme I : Sensors / / edited by Dionysis D. Bochtis, [and four others]
Information and communication technologies for agriculture - Theme I : Sensors / / edited by Dionysis D. Bochtis, [and four others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (331 pages)
Disciplina 630.2085
Collana Springer Optimization and Its Applications
Soggetto topico Agricultural informatics
Enginyeria agronòmica
Detectors
Soggetto genere / forma Llibres electrònics
ISBN 3-030-84144-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- Part I: Overview -- Emerging Sensing Technologies for Precision Agriculture -- 1 Introduction -- 1.1 Planting -- 1.2 Soil Management -- 1.3 Plant Health Management -- 1.4 Pests and Disease Management -- 1.5 Yield Harvesting and Post-Harvest -- 2 Types of Sensors -- 2.1 Remote Sensing -- 2.2 Computer Vision -- 2.2.1 RGB -- 2.2.2 Multispectral -- 2.2.3 Hyperspectral -- 2.2.4 Thermal -- 2.2.5 LiDAR -- 2.3 Synthetic Aperture Radar -- 3 Wireless Sensor Networks -- 4 Sensor Fusion -- 5 Conclusions -- References -- Soil Reflectance Spectroscopy for Supporting Sustainable Development Goals -- 1 Introduction -- 2 The Important Role of Soil in Supporting SDGs -- 2.1 Monitoring Soils for Optimization of Precision Agriculture -- 3 Earth Observation Supporting SDGs -- 3.1 Extracting Soil Information from Earth Observation -- 3.2 Estimating Essential Agricultural Variables with EO Techniques the Cases of Soil Organic Carbon and Soil Moisture -- 3.2.1 Approaches for Soil Moisture and Soil Organic Carbon/Matter Estimation Using EO Techniques -- 3.2.2 Estimating Other Agronomic Variables (pH, Clay, and Others) -- 3.2.3 Methods Exploiting Ancillary Information -- 4 Remote Sensing for Soil Monitoring: Limitations and Ways Forward -- 5 Conclusions and Recommendations -- References -- Proximal Sensing Sensors for Monitoring Crop Growth -- 1 Introduction -- 1.1 General Scope -- 2 Problems in Fields That Can Be Detected by Proximal Sensing -- 2.1 Problems in Crop Emergence -- 2.2 Agrotechnical Mistakes -- 2.3 Overwintering Damage to the Crop Field -- 3 Precision Agriculture -- 3.1 Fertilization Effect on Environment -- 3.2 Variable Rate Fertilization (VRF) -- 3.3 Sensors for Precision Fertilization -- 4 Proximal Measurement Sensors -- 4.1 Soil Characteristics -- 4.2 Proximal Soil Sensors -- 4.3 Proximal Crop Sensors.
4.4 Chlorophyll Meters -- 4.5 Reflectance Sensors for Nitrogen (N) -- 4.6 High-Resolution Spectrometers -- 4.7 Yara N-Sensor -- 5 Autonomous Platforms in Precision Agriculture -- 6 Estonian Use Case of N-Fertilization with GreenSeeker Handheld Crop Sensor -- 7 Experience of Precision Farming in Lithuania - Use Cases -- 7.1 Measurement of Soil Electrical Conductivity -- 7.2 Automated Soil Sampling -- 7.3 Changes of Mineral Fertilizer Elements in Soil Using VRF -- 7.4 Nitrogen Fertilization at a Variable Rate Using Yara N-Sensors -- 7.5 VRF Maps for N-Fertilization Using Proximal Sensors -- 7.5.1 N-Fertilization Maps for Winter Wheat -- 7.5.2 N-Fertlization Maps for Winter Rapeseed -- 7.6 N-Fertilization with Yara N-Tester -- 8 Adoption of Precision Agricultural Technologies -- 9 Conclusions -- References -- Part II: Wireless Network Systems Applications -- Experimental Performance Evaluation Techniques of LoRa Radio Modules and Exploitation for Agricultural Use -- 1 Introduction -- 2 Design Overview -- 3 Implementation Details and Measurement Methodology -- 4 Evaluation of Methods, Results and Discussion -- 5 Conclusions and Future Work -- References -- Evaluating the Performance of a Simulated Softwarized Agricultural Wireless Sensor Network -- 1 Introduction -- 2 Related Work -- 2.1 IoT in the Agricultural Domain -- 2.2 Routing Protocols -- 2.2.1 Collection Tree Protocol (CTP) -- 2.2.2 IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) -- 2.3 Software-Defined Networking (SDN) -- 3 Methodology -- 4 Results -- 5 Discussion -- 6 Conclusions -- References -- Smart Agriculture: A Low-Cost Wireless Sensor Network Approach -- 1 Introduction -- 2 Wireless Sensing Technologies in Smart Agriculture -- 2.1 Related Work -- 2.2 Equipment Overview -- 2.3 Selected Low-Cost Equipment -- 2.3.1 Arduino -- 2.3.2 Arduino Wireless SD Shield and XBee.
Zigbee Protocol -- Coordinator -- Routers -- End Devices -- 2.3.3 Raspberry Pi 3 -- 2.3.4 Sensors and Others -- 3 Synchronized Monitoring -- 3.1 Related Work -- 3.2 A Simple Synchronization Scheme -- 3.3 Experimental Evaluation in Olive Groves -- 4 Advanced Monitoring Architecture -- 4.1 Related Work -- 4.2 Cloud/Fog Architecture -- 4.3 Evaluation -- 4.4 Potential Future Applications: The Case of Wildfires -- 5 Conclusions and Future Directions -- References -- Part III: Remote Sensing Applications -- Potential of Sentinel-2 Satellite and Novel Proximal Sensor Data Fusion for Agricultural Applications -- 1 Introduction -- 1.1 Satellite-Based Sensors -- 1.2 Airborne- and Drone-Based Sensors -- 1.3 Ground-Based Proximal Sensors -- 1.4 Vegetation Indices -- 1.5 Inter-Comparison -- 2 Materials and Methods -- 2.1 Plant-O-Meter -- 2.2 Sentinel-2 -- 2.3 Data Analysis -- 3 Results and Discussion -- 4 Conclusions -- References -- Trends in Satellite Sensors and Image Time Series Processing Methods for Crop Phenology Monitoring -- 1 Introduction -- 2 Satellite Sensors for Crop Phenology Monitoring -- 3 Time Series Processing for Crop Seasonality Monitoring -- 3.1 Gap-Filling -- 3.2 LSP Calculation -- 4 Demonstration Cases Time Series Processing -- 4.1 Study Area and Data Acquisition -- 4.1.1 Crop Data Layer -- 4.1.2 MODIS and Sentinel-2 Surface Reflectance Time-Series -- 4.2 Time Series Processing Over Croplands -- 4.3 LSP Calculation Over Croplands -- 5 Discussion -- 6 Conclusions -- References -- Drone Imagery in Support of Orchards Trees Vegetation Assessment Based on Spectral Indices and Deep Learning -- 1 Introduction -- 2 Methodology -- 2.1 Tree Crown Detection and Classification -- 2.2 Vegetation Indices (VIs) -- 2.2.1 VARI - Visible Atmospherically Resistant Index -- 2.2.2 GLI - Green Leaf Index -- 2.3 Tree Health Assessment -- 3 Study sites.
3.1 Romanian Study Site (No 1) -- 3.2 Greek Study Site (No 2) -- 3.3 Drone Images Acquisition -- 4 Results and Discussion -- 4.1 Trees Detection Using Deep Learning -- 4.2 Trees Vegetation Health Assessment -- 5 Conclusions -- References -- Part IV: Proximal Sensing Applications -- What Does the NDVI Really Tell Us About Crops? Insight from Proximal Spectral Field Sensors -- 1 Introducing the Normalized Difference Vegetation Index (NDVI) -- 2 Methods -- 2.1 Sites, Sensors and Supporting Observations -- 2.2 Data Processing and Analysis -- 3 Results and Discussion -- 3.1 Temporal Variability at Site 1 -- 3.2 Spectral-Spatial Variability at Site 2 -- 4 Conclusions and Outlook -- References -- Geophysical Sensors for Mapping Soil Layers - A Comparative Case Study Using Different Electrical and Electromagnetic Sensors -- 1 Introduction -- 2 Materials and Methods -- 2.1 Site Characteristics -- 2.2 Data Acquisition -- 2.2.1 ECa Mapping with DUALEM-21 -- 2.2.2 ECa Mapping with Geophilus -- 2.2.3 DC Measurements with Static Electrodes Along Reference Transects -- 2.2.4 Ground-Penetrating Radar -- 2.3 Data Processing -- 2.4 Soil Sampling and Soil Texture Analysis -- 3 Results -- 3.1 Lateral Soil Heterogeneity -- 3.2 Information About Soil Stratification -- 3.3 Two-Dimensional Conductivity Models Along Reference Transects -- 3.4 GPR Transects -- 3.5 Soil Texture as Ground-Truth Data -- 4 Discussion -- 5 Conclusion -- References -- Geoinformation Technologies in Pest Management: Mapping Olive Fruit Fly Population in Olive Trees -- 1 Introduction -- 2 Experimental Set Up -- 3 Methods -- 4 Results & -- Discussion -- 5 Conclusions -- References -- In-field Experiments for Performance Evaluation of a New Low-Cost Active Multispectral Crop Sensor -- 1 Introduction -- 2 Materials and Methods -- 2.1 Field Trials and Experimental Design.
2.2 Sensor Measurements and Sensor Description -- 2.3 Harvest -- 2.4 Data Analysis -- 3 Results and Discussion -- 3.1 Descriptive Statistics and Analysis of Variance -- 3.2 Weather -- 3.3 Correlation Analysis -- 3.4 Linear Regression Analysis -- 4 Future Prospects for Development -- 5 Conclusions and Outlook -- References.
Record Nr. UNISA-996472038903316
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Information and communication technologies for agriculture - Theme I : Sensors / / edited by Dionysis D. Bochtis, [and four others]
Information and communication technologies for agriculture - Theme I : Sensors / / edited by Dionysis D. Bochtis, [and four others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (331 pages)
Disciplina 630.2085
Collana Springer Optimization and Its Applications
Soggetto topico Agricultural informatics
Enginyeria agronòmica
Detectors
Soggetto genere / forma Llibres electrònics
ISBN 3-030-84144-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- Part I: Overview -- Emerging Sensing Technologies for Precision Agriculture -- 1 Introduction -- 1.1 Planting -- 1.2 Soil Management -- 1.3 Plant Health Management -- 1.4 Pests and Disease Management -- 1.5 Yield Harvesting and Post-Harvest -- 2 Types of Sensors -- 2.1 Remote Sensing -- 2.2 Computer Vision -- 2.2.1 RGB -- 2.2.2 Multispectral -- 2.2.3 Hyperspectral -- 2.2.4 Thermal -- 2.2.5 LiDAR -- 2.3 Synthetic Aperture Radar -- 3 Wireless Sensor Networks -- 4 Sensor Fusion -- 5 Conclusions -- References -- Soil Reflectance Spectroscopy for Supporting Sustainable Development Goals -- 1 Introduction -- 2 The Important Role of Soil in Supporting SDGs -- 2.1 Monitoring Soils for Optimization of Precision Agriculture -- 3 Earth Observation Supporting SDGs -- 3.1 Extracting Soil Information from Earth Observation -- 3.2 Estimating Essential Agricultural Variables with EO Techniques the Cases of Soil Organic Carbon and Soil Moisture -- 3.2.1 Approaches for Soil Moisture and Soil Organic Carbon/Matter Estimation Using EO Techniques -- 3.2.2 Estimating Other Agronomic Variables (pH, Clay, and Others) -- 3.2.3 Methods Exploiting Ancillary Information -- 4 Remote Sensing for Soil Monitoring: Limitations and Ways Forward -- 5 Conclusions and Recommendations -- References -- Proximal Sensing Sensors for Monitoring Crop Growth -- 1 Introduction -- 1.1 General Scope -- 2 Problems in Fields That Can Be Detected by Proximal Sensing -- 2.1 Problems in Crop Emergence -- 2.2 Agrotechnical Mistakes -- 2.3 Overwintering Damage to the Crop Field -- 3 Precision Agriculture -- 3.1 Fertilization Effect on Environment -- 3.2 Variable Rate Fertilization (VRF) -- 3.3 Sensors for Precision Fertilization -- 4 Proximal Measurement Sensors -- 4.1 Soil Characteristics -- 4.2 Proximal Soil Sensors -- 4.3 Proximal Crop Sensors.
4.4 Chlorophyll Meters -- 4.5 Reflectance Sensors for Nitrogen (N) -- 4.6 High-Resolution Spectrometers -- 4.7 Yara N-Sensor -- 5 Autonomous Platforms in Precision Agriculture -- 6 Estonian Use Case of N-Fertilization with GreenSeeker Handheld Crop Sensor -- 7 Experience of Precision Farming in Lithuania - Use Cases -- 7.1 Measurement of Soil Electrical Conductivity -- 7.2 Automated Soil Sampling -- 7.3 Changes of Mineral Fertilizer Elements in Soil Using VRF -- 7.4 Nitrogen Fertilization at a Variable Rate Using Yara N-Sensors -- 7.5 VRF Maps for N-Fertilization Using Proximal Sensors -- 7.5.1 N-Fertilization Maps for Winter Wheat -- 7.5.2 N-Fertlization Maps for Winter Rapeseed -- 7.6 N-Fertilization with Yara N-Tester -- 8 Adoption of Precision Agricultural Technologies -- 9 Conclusions -- References -- Part II: Wireless Network Systems Applications -- Experimental Performance Evaluation Techniques of LoRa Radio Modules and Exploitation for Agricultural Use -- 1 Introduction -- 2 Design Overview -- 3 Implementation Details and Measurement Methodology -- 4 Evaluation of Methods, Results and Discussion -- 5 Conclusions and Future Work -- References -- Evaluating the Performance of a Simulated Softwarized Agricultural Wireless Sensor Network -- 1 Introduction -- 2 Related Work -- 2.1 IoT in the Agricultural Domain -- 2.2 Routing Protocols -- 2.2.1 Collection Tree Protocol (CTP) -- 2.2.2 IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) -- 2.3 Software-Defined Networking (SDN) -- 3 Methodology -- 4 Results -- 5 Discussion -- 6 Conclusions -- References -- Smart Agriculture: A Low-Cost Wireless Sensor Network Approach -- 1 Introduction -- 2 Wireless Sensing Technologies in Smart Agriculture -- 2.1 Related Work -- 2.2 Equipment Overview -- 2.3 Selected Low-Cost Equipment -- 2.3.1 Arduino -- 2.3.2 Arduino Wireless SD Shield and XBee.
Zigbee Protocol -- Coordinator -- Routers -- End Devices -- 2.3.3 Raspberry Pi 3 -- 2.3.4 Sensors and Others -- 3 Synchronized Monitoring -- 3.1 Related Work -- 3.2 A Simple Synchronization Scheme -- 3.3 Experimental Evaluation in Olive Groves -- 4 Advanced Monitoring Architecture -- 4.1 Related Work -- 4.2 Cloud/Fog Architecture -- 4.3 Evaluation -- 4.4 Potential Future Applications: The Case of Wildfires -- 5 Conclusions and Future Directions -- References -- Part III: Remote Sensing Applications -- Potential of Sentinel-2 Satellite and Novel Proximal Sensor Data Fusion for Agricultural Applications -- 1 Introduction -- 1.1 Satellite-Based Sensors -- 1.2 Airborne- and Drone-Based Sensors -- 1.3 Ground-Based Proximal Sensors -- 1.4 Vegetation Indices -- 1.5 Inter-Comparison -- 2 Materials and Methods -- 2.1 Plant-O-Meter -- 2.2 Sentinel-2 -- 2.3 Data Analysis -- 3 Results and Discussion -- 4 Conclusions -- References -- Trends in Satellite Sensors and Image Time Series Processing Methods for Crop Phenology Monitoring -- 1 Introduction -- 2 Satellite Sensors for Crop Phenology Monitoring -- 3 Time Series Processing for Crop Seasonality Monitoring -- 3.1 Gap-Filling -- 3.2 LSP Calculation -- 4 Demonstration Cases Time Series Processing -- 4.1 Study Area and Data Acquisition -- 4.1.1 Crop Data Layer -- 4.1.2 MODIS and Sentinel-2 Surface Reflectance Time-Series -- 4.2 Time Series Processing Over Croplands -- 4.3 LSP Calculation Over Croplands -- 5 Discussion -- 6 Conclusions -- References -- Drone Imagery in Support of Orchards Trees Vegetation Assessment Based on Spectral Indices and Deep Learning -- 1 Introduction -- 2 Methodology -- 2.1 Tree Crown Detection and Classification -- 2.2 Vegetation Indices (VIs) -- 2.2.1 VARI - Visible Atmospherically Resistant Index -- 2.2.2 GLI - Green Leaf Index -- 2.3 Tree Health Assessment -- 3 Study sites.
3.1 Romanian Study Site (No 1) -- 3.2 Greek Study Site (No 2) -- 3.3 Drone Images Acquisition -- 4 Results and Discussion -- 4.1 Trees Detection Using Deep Learning -- 4.2 Trees Vegetation Health Assessment -- 5 Conclusions -- References -- Part IV: Proximal Sensing Applications -- What Does the NDVI Really Tell Us About Crops? Insight from Proximal Spectral Field Sensors -- 1 Introducing the Normalized Difference Vegetation Index (NDVI) -- 2 Methods -- 2.1 Sites, Sensors and Supporting Observations -- 2.2 Data Processing and Analysis -- 3 Results and Discussion -- 3.1 Temporal Variability at Site 1 -- 3.2 Spectral-Spatial Variability at Site 2 -- 4 Conclusions and Outlook -- References -- Geophysical Sensors for Mapping Soil Layers - A Comparative Case Study Using Different Electrical and Electromagnetic Sensors -- 1 Introduction -- 2 Materials and Methods -- 2.1 Site Characteristics -- 2.2 Data Acquisition -- 2.2.1 ECa Mapping with DUALEM-21 -- 2.2.2 ECa Mapping with Geophilus -- 2.2.3 DC Measurements with Static Electrodes Along Reference Transects -- 2.2.4 Ground-Penetrating Radar -- 2.3 Data Processing -- 2.4 Soil Sampling and Soil Texture Analysis -- 3 Results -- 3.1 Lateral Soil Heterogeneity -- 3.2 Information About Soil Stratification -- 3.3 Two-Dimensional Conductivity Models Along Reference Transects -- 3.4 GPR Transects -- 3.5 Soil Texture as Ground-Truth Data -- 4 Discussion -- 5 Conclusion -- References -- Geoinformation Technologies in Pest Management: Mapping Olive Fruit Fly Population in Olive Trees -- 1 Introduction -- 2 Experimental Set Up -- 3 Methods -- 4 Results & -- Discussion -- 5 Conclusions -- References -- In-field Experiments for Performance Evaluation of a New Low-Cost Active Multispectral Crop Sensor -- 1 Introduction -- 2 Materials and Methods -- 2.1 Field Trials and Experimental Design.
2.2 Sensor Measurements and Sensor Description -- 2.3 Harvest -- 2.4 Data Analysis -- 3 Results and Discussion -- 3.1 Descriptive Statistics and Analysis of Variance -- 3.2 Weather -- 3.3 Correlation Analysis -- 3.4 Linear Regression Analysis -- 4 Future Prospects for Development -- 5 Conclusions and Outlook -- References.
Record Nr. UNINA-9910559396703321
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Information and communication technologies for agriculture theme II : data / / edited by Dionysis D. Bochtis [and four others]
Information and communication technologies for agriculture theme II : data / / edited by Dionysis D. Bochtis [and four others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (296 pages)
Disciplina 630.2085
Collana Springer Optimization and Its Applications
Soggetto topico Agricultural informatics
Enginyeria agronòmica
Innovacions agrícoles
Internet de les coses
Aplicacions industrials
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-030-84148-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- Part I: Data Technologies -- You Got Data Now What: Building the Right Solution for the Problem -- 1 Introduction -- 2 Sensors and Their Readings -- 3 Networks of Sensors -- 3.1 In-Field Crop Production -- 3.2 Intensive Crop Production -- 3.3 Intensive Animal Production -- 4 Using Machine Learning -- 5 Remaining Challenges and Opportunities -- References -- Data Fusion and Its Applications in Agriculture -- 1 Introduction -- 2 Data Fusion -- 2.1 Introduction -- 2.2 The ``Whys´´ and ``Wherefores´´ of Information Fusion -- 2.3 Information Fusion: Methods, Techniques, and Algorithms -- 2.4 Models of Data Fusion -- Architectures and Performance Aspects -- Data Alignment and Fusion of Attributes -- 2.5 Applications of Information Fusion in Agriculture -- Remote Sensing Image Preprocessing -- Restoration and Denoising -- Pixel-based Classification -- Spectral Feature Classification -- Classification with Spatial Information -- Target Recognition -- Scene Understanding -- 2.6 Data Mining and Artificial Intelligence in Agriculture -- Yield Prediction -- Disease Detection -- Weed Detection -- Species Recognition -- 3 Conclusions and Future Challenges -- References -- Machine Learning Technology and Its Current Implementation in Agriculture -- 1 Introduction -- 2 Machine Learning Versus Conventional Programming -- 3 Fundamental Features of Machine Learning -- 4 Types of Machine Learning Methods -- 4.1 Supervised Learning -- Regression -- Classification -- 4.2 Unsupervised Learning -- Clustering -- Dimensionality Reduction -- Association -- 4.3 Reinforcement Learning -- Classification -- Control -- 4.4 Recommender Systems (Active Learning) -- Content-based -- Collaborative Filtering -- 5 Families of Machine Learning Algorithms -- 5.1 Regression -- 5.2 Regularization -- 5.3 Bayesian -- 5.4 Instance-based -- 5.5 Decision Tree.
5.6 Ensemble -- 5.7 Clustering -- 5.8 Dimensionality Reduction -- 5.9 Association Rule -- 5.10 Artificial Neural Networks -- 5.11 Deep Neural Networks -- 6 Machine Learning in Agriculture -- 6.1 Yield Prediction -- 6.2 Crop Disease Detection -- 6.3 Weed Detection -- 6.4 Quality Assessment -- 7 Summary of the Basic Aspects of the Reviewed Studies -- 8 Conclusions -- References -- Part II: Applications -- Application Possibilities of IoT-based Management Systems in Agriculture -- 1 Introduction -- 1.1 Data Acquisition and Management in Agriculture -- 2 Methodology -- 3 Progression and Evaluation of the System -- 3.1 The Main Characteristics Based on the Literature -- 3.2 Determining the Possibilities from a Practical Standpoint -- Data Acquisition Systems -- Data Management Methods and Applications -- Data Utilization -- 4 Discussion -- 5 Conclusions -- References -- Plant Species Detection Using Image Processing and Deep Learning: A Mobile-Based Application -- 1 Introduction -- 2 Background Research -- 2.1 Deep Learning -- 3 Methodology -- 3.1 Dataset and Data Preparation -- Background Removal -- Data Augmentation -- 4 Software Development and Analysis -- 5 Detailed Design and Software Implementation -- 5.1 Developing Convolutional Neural Network -- 5.2 Online Classification System App -- 6 Testing and Evaluation -- 7 Discussion and Future Work -- 8 Conclusions -- References -- Computer Vision-based Detection and Tracking in the Olive Sorting Pipeline -- 1 Introduction -- 1.1 Industrial Sorters -- 2 Problem Description -- 2.1 Related Work -- 3 The Proposed Olive Separation Approach -- 3.1 Image Binarization -- 3.2 Distance Transform -- 3.3 Watershed Transform -- 3.4 Centroid Extraction -- 3.5 Multiple Object Tracking -- 4 The Unscented Kalman Filter -- 4.1 Prediction Phase of the UKF -- 4.2 Update Phase of the UKF.
5 The Kuhn-Munkres (Hungarian) Algorithm -- 6 Results -- 6.1 Sample Collection -- 6.2 Simulation Design -- 6.3 Results Using Kalman Filtering -- 7 Evaluation of the Results -- 8 Conclusions -- References -- Integrating Spatial with Qualitative Data to Monitor Land Use Intensity: Evidence from Arable Land - Animal Husbandry Systems -- 1 Introduction -- 1.1 Land Use Intensity and Farming Systems -- 1.2 Land Use/Land Cover (LULC) Extraction -- 2 Methodology -- 2.1 Study Area -- 2.2 Materials and Methods -- Timeline of Changes -- Remote Sensing Data -- 2.3 Participatory Workshop -- 3 Results and Discussion -- 3.1 Image Processing -- 3.2 Land Cover Type Extraction and Change Detection -- 3.3 Land Conversions -- 3.4 Results from Qualitative Methods -- 3.5 Comparison and Synthesis of Results -- 3.6 Farming Systems and Land Use Intensity -- 4 Conclusion: Ways Forward in Integrating Qualitative Data in Land Use Intensity -- References -- Air drill Seeder Distributor Head Evaluation: A Comparison between Laboratory Tests and Computational Fluid Dynamics Simulatio... -- 1 Introduction -- 2 Materials and Methods -- 2.1 Tested Model Description -- 2.2 Description of Distributor Head´s Test Bench -- 2.3 Experiment Design -- 2.4 Numerical Simulations -- Air-Seeds Mixture Flow -- Air Flow -- Particles Trajectory -- Discrete Phase Model Setup -- 3 Results -- 3.1 Experimental Results -- 3.2 Numerical Results -- 3.3 Validation of the Numerical Model -- 4 Conclusions and Perspectives -- References -- Part III: Value Chain -- Data-Based Agricultural Business Continuity Management Policies -- 1 Introduction -- 2 Motivation -- 2.1 Business Intelligence Tools as Business Continuity Solutions in the Modern Era -- 2.2 Business Continuity and Big Data Challenges in the Agricultural Domain -- 2.3 Research Steps -- 3 Tools and Methods -- 3.1 Formulation of Datasets.
3.2 Business Intelligence Multidimensional Data Models - Preliminary Concepts -- 3.3 Business Process Modelling Notation (BPMN) for Supporting Business Decisions Based on Multidimensional Data -- 3.4 A Robust Machine Learning Agricultural Business Continuity Classifier -- 4 Results -- 4.1 The Multidimensional Data Models for Supporting Agricultural Business Continuity Management Decisions -- Model 1: The Criticality Levels Multidimensional Model -- Model 2: The Risks/Hazards Multidimensional Model -- 4.2 Machine Learning Predictive Analytics Based on the Proposed Multidimensional Schemas -- Data Preprocessing -- Risk Exposure Classification Based on Decision Tree Induction -- Boosting the Risk Exposure classifier´s Predictive Power with the 10-Fold Cross-Validation and the Random Forest Techniques -- 5 Discussion -- 6 Conclusions -- References -- Soybean Price Trend Forecast Using Deep Learning Techniques Based on Prices and Text Sentiments -- 1 Introduction -- 2 Related Work -- 2.1 Price Prediction of Agricultural Commodities -- 2.2 Deep Learning for Price Trend Prediction -- 2.3 Deep Learning for Text Sentiment Analysis -- 3 Methodology -- 4 Results -- 4.1 Models Considering Only Prices -- 4.2 Models Considering Only Text Sentiments -- 4.3 Ensemble Model Considering Prices and Text Sentiments -- 5 Discussion -- 5.1 Benefits of Deep Learning for Agricultural Price Prediction -- 5.2 Adaptation and Uses for Other Products -- 6 Conclusions -- References -- Use of Unsupervised Machine Learning for Agricultural Supply Chain Data Labeling -- 1 Introduction -- 2 Unsupervised Machine Learning in Agriculture -- 3 Methodology -- 4 Results -- 5 Discussion -- 5.1 Training Time for Each Model -- 5.2 Implementation Difficulties -- 5.3 Benefits for SC Traceability -- 5.4 Adaptation to Other SCs -- 6 Conclusions -- References.
Record Nr. UNISA-996466419403316
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Information and communication technologies for agriculture theme II : data / / edited by Dionysis D. Bochtis [and four others]
Information and communication technologies for agriculture theme II : data / / edited by Dionysis D. Bochtis [and four others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (296 pages)
Disciplina 630.2085
Collana Springer Optimization and Its Applications
Soggetto topico Agricultural informatics
Enginyeria agronòmica
Innovacions agrícoles
Internet de les coses
Aplicacions industrials
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-030-84148-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- Part I: Data Technologies -- You Got Data Now What: Building the Right Solution for the Problem -- 1 Introduction -- 2 Sensors and Their Readings -- 3 Networks of Sensors -- 3.1 In-Field Crop Production -- 3.2 Intensive Crop Production -- 3.3 Intensive Animal Production -- 4 Using Machine Learning -- 5 Remaining Challenges and Opportunities -- References -- Data Fusion and Its Applications in Agriculture -- 1 Introduction -- 2 Data Fusion -- 2.1 Introduction -- 2.2 The ``Whys´´ and ``Wherefores´´ of Information Fusion -- 2.3 Information Fusion: Methods, Techniques, and Algorithms -- 2.4 Models of Data Fusion -- Architectures and Performance Aspects -- Data Alignment and Fusion of Attributes -- 2.5 Applications of Information Fusion in Agriculture -- Remote Sensing Image Preprocessing -- Restoration and Denoising -- Pixel-based Classification -- Spectral Feature Classification -- Classification with Spatial Information -- Target Recognition -- Scene Understanding -- 2.6 Data Mining and Artificial Intelligence in Agriculture -- Yield Prediction -- Disease Detection -- Weed Detection -- Species Recognition -- 3 Conclusions and Future Challenges -- References -- Machine Learning Technology and Its Current Implementation in Agriculture -- 1 Introduction -- 2 Machine Learning Versus Conventional Programming -- 3 Fundamental Features of Machine Learning -- 4 Types of Machine Learning Methods -- 4.1 Supervised Learning -- Regression -- Classification -- 4.2 Unsupervised Learning -- Clustering -- Dimensionality Reduction -- Association -- 4.3 Reinforcement Learning -- Classification -- Control -- 4.4 Recommender Systems (Active Learning) -- Content-based -- Collaborative Filtering -- 5 Families of Machine Learning Algorithms -- 5.1 Regression -- 5.2 Regularization -- 5.3 Bayesian -- 5.4 Instance-based -- 5.5 Decision Tree.
5.6 Ensemble -- 5.7 Clustering -- 5.8 Dimensionality Reduction -- 5.9 Association Rule -- 5.10 Artificial Neural Networks -- 5.11 Deep Neural Networks -- 6 Machine Learning in Agriculture -- 6.1 Yield Prediction -- 6.2 Crop Disease Detection -- 6.3 Weed Detection -- 6.4 Quality Assessment -- 7 Summary of the Basic Aspects of the Reviewed Studies -- 8 Conclusions -- References -- Part II: Applications -- Application Possibilities of IoT-based Management Systems in Agriculture -- 1 Introduction -- 1.1 Data Acquisition and Management in Agriculture -- 2 Methodology -- 3 Progression and Evaluation of the System -- 3.1 The Main Characteristics Based on the Literature -- 3.2 Determining the Possibilities from a Practical Standpoint -- Data Acquisition Systems -- Data Management Methods and Applications -- Data Utilization -- 4 Discussion -- 5 Conclusions -- References -- Plant Species Detection Using Image Processing and Deep Learning: A Mobile-Based Application -- 1 Introduction -- 2 Background Research -- 2.1 Deep Learning -- 3 Methodology -- 3.1 Dataset and Data Preparation -- Background Removal -- Data Augmentation -- 4 Software Development and Analysis -- 5 Detailed Design and Software Implementation -- 5.1 Developing Convolutional Neural Network -- 5.2 Online Classification System App -- 6 Testing and Evaluation -- 7 Discussion and Future Work -- 8 Conclusions -- References -- Computer Vision-based Detection and Tracking in the Olive Sorting Pipeline -- 1 Introduction -- 1.1 Industrial Sorters -- 2 Problem Description -- 2.1 Related Work -- 3 The Proposed Olive Separation Approach -- 3.1 Image Binarization -- 3.2 Distance Transform -- 3.3 Watershed Transform -- 3.4 Centroid Extraction -- 3.5 Multiple Object Tracking -- 4 The Unscented Kalman Filter -- 4.1 Prediction Phase of the UKF -- 4.2 Update Phase of the UKF.
5 The Kuhn-Munkres (Hungarian) Algorithm -- 6 Results -- 6.1 Sample Collection -- 6.2 Simulation Design -- 6.3 Results Using Kalman Filtering -- 7 Evaluation of the Results -- 8 Conclusions -- References -- Integrating Spatial with Qualitative Data to Monitor Land Use Intensity: Evidence from Arable Land - Animal Husbandry Systems -- 1 Introduction -- 1.1 Land Use Intensity and Farming Systems -- 1.2 Land Use/Land Cover (LULC) Extraction -- 2 Methodology -- 2.1 Study Area -- 2.2 Materials and Methods -- Timeline of Changes -- Remote Sensing Data -- 2.3 Participatory Workshop -- 3 Results and Discussion -- 3.1 Image Processing -- 3.2 Land Cover Type Extraction and Change Detection -- 3.3 Land Conversions -- 3.4 Results from Qualitative Methods -- 3.5 Comparison and Synthesis of Results -- 3.6 Farming Systems and Land Use Intensity -- 4 Conclusion: Ways Forward in Integrating Qualitative Data in Land Use Intensity -- References -- Air drill Seeder Distributor Head Evaluation: A Comparison between Laboratory Tests and Computational Fluid Dynamics Simulatio... -- 1 Introduction -- 2 Materials and Methods -- 2.1 Tested Model Description -- 2.2 Description of Distributor Head´s Test Bench -- 2.3 Experiment Design -- 2.4 Numerical Simulations -- Air-Seeds Mixture Flow -- Air Flow -- Particles Trajectory -- Discrete Phase Model Setup -- 3 Results -- 3.1 Experimental Results -- 3.2 Numerical Results -- 3.3 Validation of the Numerical Model -- 4 Conclusions and Perspectives -- References -- Part III: Value Chain -- Data-Based Agricultural Business Continuity Management Policies -- 1 Introduction -- 2 Motivation -- 2.1 Business Intelligence Tools as Business Continuity Solutions in the Modern Era -- 2.2 Business Continuity and Big Data Challenges in the Agricultural Domain -- 2.3 Research Steps -- 3 Tools and Methods -- 3.1 Formulation of Datasets.
3.2 Business Intelligence Multidimensional Data Models - Preliminary Concepts -- 3.3 Business Process Modelling Notation (BPMN) for Supporting Business Decisions Based on Multidimensional Data -- 3.4 A Robust Machine Learning Agricultural Business Continuity Classifier -- 4 Results -- 4.1 The Multidimensional Data Models for Supporting Agricultural Business Continuity Management Decisions -- Model 1: The Criticality Levels Multidimensional Model -- Model 2: The Risks/Hazards Multidimensional Model -- 4.2 Machine Learning Predictive Analytics Based on the Proposed Multidimensional Schemas -- Data Preprocessing -- Risk Exposure Classification Based on Decision Tree Induction -- Boosting the Risk Exposure classifier´s Predictive Power with the 10-Fold Cross-Validation and the Random Forest Techniques -- 5 Discussion -- 6 Conclusions -- References -- Soybean Price Trend Forecast Using Deep Learning Techniques Based on Prices and Text Sentiments -- 1 Introduction -- 2 Related Work -- 2.1 Price Prediction of Agricultural Commodities -- 2.2 Deep Learning for Price Trend Prediction -- 2.3 Deep Learning for Text Sentiment Analysis -- 3 Methodology -- 4 Results -- 4.1 Models Considering Only Prices -- 4.2 Models Considering Only Text Sentiments -- 4.3 Ensemble Model Considering Prices and Text Sentiments -- 5 Discussion -- 5.1 Benefits of Deep Learning for Agricultural Price Prediction -- 5.2 Adaptation and Uses for Other Products -- 6 Conclusions -- References -- Use of Unsupervised Machine Learning for Agricultural Supply Chain Data Labeling -- 1 Introduction -- 2 Unsupervised Machine Learning in Agriculture -- 3 Methodology -- 4 Results -- 5 Discussion -- 5.1 Training Time for Each Model -- 5.2 Implementation Difficulties -- 5.3 Benefits for SC Traceability -- 5.4 Adaptation to Other SCs -- 6 Conclusions -- References.
Record Nr. UNINA-9910552715003321
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Information and communication technologies for agriculture-Theme IV : actions / / edited by Dionysis D. Bochtis [and four others]
Information and communication technologies for agriculture-Theme IV : actions / / edited by Dionysis D. Bochtis [and four others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (293 pages)
Disciplina 630.2085
Collana Springer Optimization and Its Applications
Soggetto topico Agricultural informatics
Innovacions agrícoles
Internet de les coses
Aplicacions industrials
Soggetto genere / forma Llibres electrònics
ISBN 3-030-84156-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996466555703316
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Information and communication technologies for agriculture-Theme IV : actions / / edited by Dionysis D. Bochtis [and four others]
Information and communication technologies for agriculture-Theme IV : actions / / edited by Dionysis D. Bochtis [and four others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (293 pages)
Disciplina 630.2085
Collana Springer Optimization and Its Applications
Soggetto topico Agricultural informatics
Innovacions agrícoles
Internet de les coses
Aplicacions industrials
Soggetto genere / forma Llibres electrònics
ISBN 3-030-84156-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910552711703321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Supply Chain Management for Bioenergy and Bioresources
Supply Chain Management for Bioenergy and Bioresources
Autore Bochtis Dionysis
Pubbl/distr/stampa Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
Descrizione fisica 1 electronic resource (148 p.)
Soggetto topico Research & information: general
Biology, life sciences
Technology, engineering, agriculture
Soggetto non controllato supply-chain design
strategic planning
operational planning
energy crop production
crop residue
dry above ground biomass
soybean
empirical models
bilinear regression analysis
agricultural operations
energy use
assessment tool
workability
machinery
agricultural machinery
fleet management
auto-steering system
collaborative operating system
flow-shop
simulation
field experiment
Fuzzy Cognitive Maps
photovoltaic solar energy
scenario analysis
decision-support
energy management
bioenergy
efficiency of bio-resources
decision support system
multi-criteria analysis
sustainability
neuro-fuzzy
ANFIS
neural networks
soft computing
fuzzy cognitive maps
energy forecasting
natural gas
prediction
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910557285203321
Bochtis Dionysis  
Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
Materiale a stampa
Lo trovi qui: Univ. Federico II
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