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Agronomic Crops : Volume 1: Production Technologies / / edited by Mirza Hasanuzzaman
| Agronomic Crops : Volume 1: Production Technologies / / edited by Mirza Hasanuzzaman |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (xx, 710 pages) : illustrations |
| Disciplina | 631 |
| Soggetto topico |
Agriculture
Sustainable development Soil science Soil conservation Plant physiology Plant science Botany Sustainable Development Soil Science & Conservation Plant Physiology Plant Sciences Desenvolupament sostenible Enginyeria agronòmica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-329-151-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Agronomic Crops: Types and Uses -- Climate Resilient Minor Crops for Food Security -- Climatic Variability and Agronomic Cropping Pattern -- Soil Health in Cropping Systems: An Overview -- Agronomic Cropping Systems in relation to Climatic Variability. -Growth and Development Dynamics in Agronomic Crops under Environmental Stress -- Tillage and Crop Production -- Effect of Planting Dates on Agronomic Crop Production -- Crop production under changing climate – Past, Present and Future -- Cultivation of Aromatic Rice: A review -- Direct Seeding in Rice: Problems and Prospects -- Advanced Production Technologies of Wheat -- Advanced Production Technologies of Maize -- Agrotechnologies of Baby Corn Production -- Advanced Production Technologies of Millets -- Advanced Production Technologies of Legumes Crops -- Advanced Production Technologies of Oilseed Crops -- Advanced Production Technology of Sugar Crops -- Advanced Production Technologies of Potato -- Advanced Production Technology and Processing of Jute -- Tea production in Bangladesh: From bush to mug -- Tea: a worthwhile, popular beverage crop since time immemorial -- Agronomy of Betelvine Crop -- Fundamentals of Crop Rotation in Agronomic Management -- Cool Season Food Legumes in Rice Fallows: An Indian Perspective -- Crop Diversification and Food Security -- Fundamentals of Seed Production and Processing of Agronomic Crops -- Seed Production Technologies of some Major Field Crops -- Postharvest Technologies for Major Agronomic Crops. |
| Record Nr. | UNINA-9910373911803321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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 | ||
| ||
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 | ||
| ||
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 | ||
| ||
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 | ||
| ||
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 | ||
| ||
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 | ||
| ||
