Handbook of Research of Internet of Things and Cyber-Physical Systems: An Integrative Approach to an Interconnected Future

Cover
Half-Title Page
Series Page
Title Page
Copyright Page
Contents
Preface
1 Scope and Recent Trends of Artificial Intelligence in Indian Agriculture
	1.1 Introduction
	1.2 Different Forms of AI
	1.3 Different Technologies in AI
		1.3.1 Machine Learning
			1.3.1.1 Data Pre-processing
			1.3.1.2 Feature Extraction
			1.3.1.3 Working With Data Sets
			1.3.1.4 Model Development
			1.3.1.5 Improving the Model With New Data
		1.3.2 Artificial Neural Network
			1.3.2.1 ANN in Agriculture
		1.3.3 Deep Learning for Smart Agriculture
			1.3.3.1 Data Pre-processing
			1.3.3.2 Data Augmentation
			1.3.3.3 Different DL Models
	1.4 AI With Big Data and Internet of Things
	1.5 AI in the Lifecycle of the Agricultural Process
		1.5.1 Improving Crop Sowing and Productivity
		1.5.2 Soil Health Monitoring
		1.5.3 Weed and Pest Control
		1.5.4 Water Management
		1.5.5 Crop Harvesting
	1.6 Indian Agriculture and Smart Farming
		1.6.1 Sensors for Smart Farming
	1.7 Advantages of Using AI in Agriculture
	1.8 Role of AI in Indian Agriculture
	1.9 Case Study in Plant Disease Identification Using AI Technology—Tomato and Potato Crops
	1.10 Challenges in AI
	1.11 Conclusion
	References
2 Comparative Evaluation of Neural Networks in Crop Yield Prediction of Paddy and Sugarcane Crop
	2.1 Introduction
	2.2 Introduction to Artificial Neural Networks
		2.2.1 Overview of Artificial Neural Networks
		2.2.2 Components of Neural Networks
		2.2.3 Types and Suitability of Neural Networks
	2.3 Application of Neural Networks in Agriculture
		2.3.1 Potential Applications of Neural Networks in Agriculture
		2.3.2 Significance of Neural Networks in Crop Yield Prediction
	2.4 Importance of Remote Sensing in Crop Yield Estimation
	2.5 Derivation of Crop-Sensitive Parameters From Remote Sensing for Paddy and Sugarcane Crops
		2.5.1 Study Area
		2.5.2 Materials and Methods
			2.5.2.1 Data Acquisition and Crop Parameters Retrieval From Remote Sensing Images
		2.5.3 Results and Conclusions
	2.6 Neural Network Model Development, Calibration and Validation
		2.6.1 Materials and Methods
			2.6.1.1 ANN Model Design
			2.6.1.2 Model Training
			2.6.1.3 Model Validation
		2.6.2 Results and Conclusions
	2.7 Conclusion
	References
3 Smart Irrigation Systems Using Machine Learning and Control Theory
	3.1 Machine Learning for Irrigation Systems
	3.2 Control Theory for Irrigation Systems
		3.2.1 Application Literature
		3.2.2 An Evaluation of Machine Learning–Based Irrigation Control Applications
		3.2.3 Remote Control Extensions
	3.3 Conclusion and Future Directions
	References
4 Enabling Technologies for Future Robotic Agriculture Systems: A Case Study in Indian Scenario
	4.1 Need for Robotics in Agriculture
	4.2 Different Types of Agricultural Bots
		4.2.1 Field Robots
		4.2.2 Drones
		4.2.3 Livestock Drones
		4.2.4 Multirobot System
	4.3 Existing Agricultural Robots
	4.4 Precision Agriculture and Robotics
	4.5 Technologies for Smart Farming
		4.5.1 Concepts of Internet of Things
		4.5.2 Big Data
		4.5.3 Cyber Physical System
		4.5.4 Cloud Computing
	4.6 Impact of AI and Robotics in Agriculture
	4.7 Unmanned Aerial Vehicles (UAV) in Agriculture
	4.8 Agricultural Manipulators
	4.9 Ethical Impact of Robotics and AI
	4.10 Scope of Agribots in India
	4.11 Challenges in the Deployment of Robots
	4.12 Future Scope of Robotics in Agriculture
	4.13 Conclusion
	References
5 The Applications of Industry 4.0 (I4.0) Technologies in the Palm Oil Industry in Colombia (Latin America)
	5.1 Introduction
	5.2 Methodology
		5.2.1 Sample Selection
	5.3 Results Analysis
		5.3.1 Data Visualization
		5.3.2 Cooccurrence
		5.3.3 Coauthorship
		5.3.4 Citation
		5.3.5 Cocitation
	5.4 Colombia PO Industry
	5.5 The PO Industry and the Circular Economy
	5.6 Conclusion
	5.7 Further Recommendations for the Colombian PO Industry
	Acknowledgments
	References
6 Intelligent Multiagent System for Agricultural Management Processes (Case Study: Greenhouse)
	Abbreviations
	6.1 Introduction
	6.2 Modern Agricultural Methods
	6.3 Internet of Things Applications in Smart Agriculture
	6.4 Artificial Intelligence
		6.4.1 Overview of AI
		6.4.2 Branches of DAI
		6.4.3 The Differences Between MAS and Computing Paradigms
	6.5 MAS
		6.5.1 Overview of MAS
		6.5.2 MAS Simulation
	6.6 Design and Implementation
		6.6.1 Conception of the Solution
			6.6.1.1 The Existing Study
			6.6.1.2 Agents List
		6.6.2 Introduction to the System Implementation
			6.6.2.1 Environment
			6.6.2.2 Group Communication (Multicast)
			6.6.2.3 Message Transport
			6.6.2.4 Data Exchange Format
			6.6.2.5 Cooperation
			6.6.2.6 Coordination
			6.6.2.7 Negotiation
	6.7 Analysis and Discussion
	6.8 Conclusion
	References
7 Smart Irrigation System for Smart Agricultural Using IoT: Concepts, Architecture, and Applications
	7.1 Introduction
	7.2 Irrigation Systems
		7.2.1 Agricultural Irrigation Techniques
		7.2.2 Surface Irrigation Systems
		7.2.3 Sprinkler Irrigation
		7.2.4 Micro-Irrigation Systems
		7.2.5 Comparison of Irrigation Methods
		7.2.6 Efficiency of Irrigation Systems
	7.3 IoT
		7.3.1 IoT History
		7.3.2 IoT Architecture
		7.3.3 Examples of Uses for the IoT
		7.3.4 IoT Importance in Different Sectors
	7.4 IoT Applications in Agriculture
		7.4.1 Precision Cultivation
		7.4.2 Agricultural Unmanned Aircraft
		7.4.3 Livestock Control
		7.4.4 Smart Greenhouses
	7.5 IoT and Water Management
	7.6 Introduction to the Implementation
	7.7 Analysis and Discussion
	7.8 Conclusion
	References
8 The Internet of Things (IoT) for Sustainable Agriculture
	8.1 Introduction
	8.2 ICT in Agriculture
	8.3 Internet of Things in Agriculture and Allied Sector
		8.3.1 Precision Farming
		8.3.2 Agriculture Drones
		8.3.3 Livestock Monitoring
		8.3.4 Smart Greenhouses
	8.4 Geospatial Technology
		8.4.1 Remote Sensing
		8.4.2 Geographic Information System
		8.4.3 GPS for Agriculture Resources Mapping
	8.5 Summary and Conclusion
	References
9 Advances in Bionic Approaches for Agriculture and Forestry Development
	9.1 Introduction
	9.2 Precision Farming
		9.2.1 Nanosensors and Its Role in Agriculture
			9.2.1.1 Nanobiosensor Use for Heavy Metal Detection
			9.2.1.2 Nanobiosensors Use for Urea Detection
			9.2.1.3 Nanosensors for Soil Analysis
			9.2.1.4 Nanosensors for Disease Assessment
	9.3 Powerful Role of Drones in Agriculture
		9.3.1 Unmanned Aerial Vehicle Providing Crop Data
		9.3.2 Using Raw Data to Produce Useful Information
		9.3.3 Crop Health Surveillance and Monitoring
	9.4 Nanobionics in Plants
	9.5 Role of Nanotechnology in Forestry
		9.5.1 Chemotaxonomy
		9.5.2 Wood and Paper Processing
	9.6 Conclusion
	References
10 Simulation of Water Management Processes of Distributed Irrigation Systems
	10.1 Introduction
	10.2 Modeling of Water Facilities
	10.3 Processing and Conducting Experiments
	10.4 Conclusion
	References
11 Conceptual Principles of Reengineering of Agricultural Resources: Open Problems, Challenges and Future Trends
	11.1 Introduction
	11.2 Modern Agronomy and Approaches for Environment Sustenance
		11.2.1 Sustainable Agriculture
	11.3 International Federation of Organic Agriculture Movements (IFOAM) and Significance
	11.4 Low Cost versus Sustainable Agricultural Production
	11.5 Change of Trends in Agriculture
	References
12 Role of Agritech Start-Ups in Supply Chain—An Organizational Approach of Ninjacart
	12.1 Introduction
	12.2 How Does the Chain Work?
	12.3 Undisrupted Chain of Ninjacart During Pandemic-19
	12.4 Conclusion
	References
13 Institutional Model of Integrating Agricultural Production Technologies with Accounting and Information Systems
	13.1 Introduction
	13.2 Research Methodology
	13.3 The General Model of a New Informational Paradigm of Agricultural Activities’ Organization
	13.4 The Model of Institutional Interaction of Information Agents in Agricultural Production
	13.5 Conclusions
	References
14 Relevance of Artificial Intelligence in Wastewater Management
	14.1 Introduction
	14.2 Digital Technologies and Industrial Sustainability
	14.3 Artificial Neural Networks and Its Categories
	14.4 AI in Technical Performance
	14.5 AI in Economic Performance
	14.6 AI in Management Performance
	14.7 AI in Wastewater Reuse
	14.8 Conclusion
	References
15 Risks of Agrobusiness Digital Transformation
	15.1 Modern Global Trends in Agriculture
	15.2 The Global Innovative Differentiation
	15.3 National Indicative Planning of Innovative Transformations
	15.4 Key Myths and Risks of Digitalization of Agrobusiness
	15.5 Examples of Use of Digital Technologies in Agriculture
	15.6 Imperatives of Transforming the Region into a Cost-Effective Ecosystem of Digital Highly Productive and Risk-Free Agriculture
	15.7 Conclusion
	References
16 Water Resource Management in Distributed Irrigation Systems
	16.1 Introduction
	16.2 Types of Mathematical Models for Modeling the Process of Managing Irrigation Channels
	16.3 Building a River Model
		16.3.1 Classification of Models by Solution Methods
		16.3.2 Method of Characteristics
		16.3.3 Hydrological Analogy Method
		16.3.4 Analysis of Works on the Formulation of Boundary Value Problems
	16.4 Spatial Hierarchy of River Terrain
		16.4.1 Small Drainage Basin Study Scheme
		16.4.2 Modeling Water Management in Uzbekistan
		16.4.3 Stages of Developing a Water Resources Management Model
	16.5 Organizations in the Structure of Water Resources Management
	16.6 Conclusion
	References
17 Digital Transformation via Blockchain in the Agricultural Commodity Value Chain
	17.1 Introduction
	17.2 Precision Agriculture for Food Supply Security
		17.2.1 Smart Agriculture Business
		17.2.2 Trading Venues for Contract Farming, Crowdfunding and E-Trades
	17.3 Blockchain Technology Practices and Literature Reviews on Food Supply Chain
		17.3.1 Food Supply Chain
		17.3.2 Smart Contracts
	17.4 Agricultural Sector Value Chain Digitalization
		17.4.1 Digital Solution for Contract Farming
		17.4.2 Commodity Funding
			17.4.2.1 Smart Contracts
			17.4.2.2 Crowdfunding Token Trading
		17.4.3 Digital Transfer System
	17.5 Conclusion
	References
18 Role of Start-Ups in Altering Agrimarket Channel (Input-Output)
	18.1 Introduction
	18.2 Agriculture Supply Chain Management
	18.3 How Start-Ups Fill the Concerns and Gaps in Agri Input Supply Chain?
	18.4 Output Supply Chain
	18.5 How Start-Ups are Filling the Concerns and Gaps in Agri Output Supply Chain
	18.6 Conclusion
	References
19 Development of Blockchain Agriculture Supply Chain Framework Using Social Network Theory: An Empirical Evidence Based on Malaysian Agriculture Firms
	19.1 Introduction
	19.2 Literature Review
		19.2.1 Agriculture Malaysia
		19.2.2 Agriculture Supply Chain
		19.2.3 Blockchain Technology
		19.2.4 Blockchain Agriculture Supply Chain Management
		19.2.5 Social Network Theory
		19.2.6 Social Network Analysis
	19.3 Methodology
		19.3.1 Blockchain Agriculture Supply Chain Management Framework
		19.3.2 Research Design
	19.4 Results and Discussion
		19.4.1 Demographic Profiles
		19.4.2 Social Network Analysis Results
	19.5 Conclusion
	19.6 Acknowledgment
	References
20 Potential Options and Applications of Machine Learning in Soil Science
	20.1 Introduction: A Deep Insight on Machine Learning, Deep Learning and Artificial Intelligence
	20.2 Application of ML in Soil Science
	20.3 Classification of ML Techniques
		20.3.1 Supervised ML
		20.3.2 Unsupervised ML
		20.3.3 Reinforcement ML
	20.4 Artificial Neural Network
	20.5 Support Vector Machine
	20.6 Conclusion
	References
Index
EULA