Internet of Things and Analytics for Agriculture, Volume 3

Preface
Contents
Editors and Contributors
1 Functional Framework for IoT-Based Agricultural System
	Abstract
	1 Introduction
	2 Scope of IoT in Agriculture
		2.1 Irrigation and Fertilizers
		2.2 Disease Control
		2.3 Precision Farming
		2.4 Crop Yield
		2.5 Storage and Transportation
		2.6 Livestock Monitoring
		2.7 Farm Machinery and Maintenance
	3 IoT Framework for Smart Agriculture
		3.1 Hardware Requirement
			3.1.1 Sensors
			3.1.2 GPS Module
		3.2 Relays
		3.3 Software and Algorithms
			3.3.1 ML-Based Image Processing Algorithms
		3.4 Data Storage
		3.5 Methodology
			3.5.1 Architecture of Smart Agriculture Systems
			3.5.2 Sensor-Based Data Capturing
			3.5.3 Data Preparation and Feature Extraction
			3.5.4 Classification and Quantification
	4 Yield Prediction
		4.1 Hardware Requirement
			4.1.1 Sensors
		4.2 Software and Algorithms
		4.3 Predictive Models for Yield
		4.4 Methodology
			4.4.1 Architecture for Yield Prediction
			4.4.2 Sensor-Based Data Capturing
			4.4.3 Data Preparation and Feature Extraction
		4.5 Yield Estimation
	5 IoT-Based Irrigation Requirement
		5.1 Methodology
			5.1.1 Sensor-Based Irrigation Requirements and Data Capturing
			5.1.2 Data Preparation and Feature Extraction
			5.1.3 Sensor-Based Moisture Assessment
			5.1.4 Irrigation Requirement Estimation
	6 IoT-Based Soil Health Monitoring
		6.1 Methodology
			6.1.1 Sensors for Soil Health Monitoring
			6.1.2 Sensor-Based Data Accusation
			6.1.3 Sensor-Based Fertilizer Requirement
			6.1.4 IoT-Based Pesticides Monitoring
			6.1.5 Image-Based Pesticides Requirement Analysis
	7 Outcomes of the Framework
	8 Summary
	References
2 A Review on Advances in IoT-Based Technologies for Smart Agricultural System
	Abstract
	1 Introduction
	2 Soil Health Monitoring
	3 Smart Irrigation
	4 Crop Health Monitoring
	5 Real-Time Weather Forecasting
	6 Internet of Things Architecture in Smart Agriculture
	7 Schematic of IoT-Based Smart Agricultural System
	8 Conclusion
	References
3 Artificial Intelligence in Agri-Food Systems—An Introduction
	Abstract
	1 Introduction
	2 Artificial Intelligence
		2.1 Functioning of a Human Brain
		2.2 Artificial Neural Network and Its Functioning
	3 Machine Learning and Deep Learning for AI Application
		3.1 Machine Learning
		3.2 Deep Learning (DL)
	4 AI Application in Food
	5 Conclusion
	References
4 Intelligent Agro-Food Chain Supply
	Abstract
	1 Introduction
		1.1 Food Clusters (Metropolitan Agriculture)
		1.2 Food Waste
		1.3 Information Management
	2 IoT in Agriculture
		2.1 Agriculture Production
		2.2 Storage and Transportation
		2.3 Agriculture Production Frameworks
		2.4 Precision Farming
		2.5 Livestock Monitoring
	3 IoT in Supply Chain of Agriculture Products
	4 Existing supply chain system for Agriculture products
	5 Industry 4.0 for Agricultural Processes
		5.1 Big Data in Agriculture
		5.2 IoT in Agriculture
		5.3 Knowledge Model in Agriculture
		5.4 AI in Agriculture
		5.5 Smart Farming
		5.6 Precision Agriculture
	6 Challenges for IoT in Supply Chain
		6.1 Functional Challenges
		6.2 Organizational Challenges
		6.3 Social Challenges
	7 Role of Big Data Analytics in Supply Chain
		7.1 Functional Impact of the Big-Data Analytics Agriculture
	8 AI and IoT Combo for Agri-Product Supply Chain Management
		8.1 Characteristics of Agro-Products Supply Chain Networks [26, 27]
	9 IoT-based Supply Chain Management System (SCMS) Architecture
		9.1 Hardware and Software
			9.1.1 Sensors
			9.1.2 GPS Module
			9.1.3 Relays
			9.1.4 Software and Algorithms Required for IoT-Based Agro-Product Supply Chain
			9.1.5 Data Storage
		9.2 Risk Management
		9.3 Collaboration and Governance
		9.4 Cold Chain
		9.5 Globalization and Communication Technologies
		9.6 Agro-Product Supply Chain Management: Logistic Challenges [31, 32]
	10 Outcomes of the Framework
	11 Summary
	References
5 Machine Learning Approaches for Agro IoT Systems
	Abstract
	1 Introduction
		1.1 Water Management
		1.2 Pest and Disease Management
		1.3 Weed Management
		1.4 Soil Management
	2 Various Machine Learning Approaches for IoT
		2.1 Support Vector Machine (SVM)
		2.2 K-Nearest Neighbor (KNN)
		2.3 Naive Bayes (NB)
		2.4 K-Means Clustering
		2.5 Deep Learning
	3 Ensemble Methodology for Crop Management
		3.1 Smart IoT-Based Crop Recommendation System Using Deep Learning
			3.1.1 Data Collection
			3.1.2 Methodology
		3.2 An Optimized Convolutional Neural Network for Disease Detection
			3.2.1 Dataset
			3.2.2 Preprocessing
			3.2.3 Convolutional Neural Network and Models
			3.2.4 Deep Learning Optimizers
			3.2.5 Performance Evaluation
	4 Conclusion
	References
6 AI-Based Yield Prediction and Smart Irrigation
	Abstract
	1 Introduction
	2 Literature Survey
	3 Environment Impact on Agriculture
	4 Scope of AI in Agriculture
	5 Yield Prediction
	6 Machine Learning-Based Yield Prediction
		6.1 Soil Health and Mineral Content
		6.2 Temperature and Humidity Prediction
		6.3 Plant Pest and Disease Prediction
	7 Material and Method
		7.1 Remote Sensing
		7.2 Machine Learning
	8 Smart Irrigation
	9 Conclusion
	References
7 IoT Enabled Technologies in Smart Farming and Challenges for Adoption
	Abstract
	1 Introduction
	2 Related Work
	3 Digital Transformation of Smart Farming
		3.1 Machine Learning
			3.1.1 Support Vector Machine
			3.1.2 Naive Bayes
			3.1.3 Logistic Regression
			3.1.4 Decision Trees
		3.2 Big Data
		3.3 Internet of Things
		3.4 Autonomous Vehicles
		3.5 Computer Vision
	4 Challenges for Adoption of IoT in Smart Farming
		4.1 Network Quality
		4.2 Quality of Hardware
		4.3 Interference Between Communication Devices
		4.4 Reliability and Scalability
		4.5 Cost Analysis and Lack of Knowledge Regarding Technology
		4.6 Data Quality and Access
		4.7 Security and Privacy
		4.8 Energy Efficiency
		4.9 Technical Failure and Resultant Damage
		4.10 Propagation Loss
	5 Conclusion
	References
8 IoT Based Agricultural Business Model for Estimating Crop Health Management to Reduce Farmer Distress Using SVM and Machine Learning
	Abstract
	1 Introduction
	2 Literature Survey
	3 Procedure
	4 Results
	5 Conclusion
	References
9 Rice and Potato Yield Prediction Using Artificial Intelligence Techniques
	Abstract
	1 Introduction
	2 Materials and Methods
		2.1 Support Vector Machines
		2.2 Artificial Neural Network (ANN)
		2.3 Deep Neural Network
		2.4 Performance of Model Evaluation
		2.5 Study Region
		2.6 Data Acquisition
		2.7 Image Processing
		2.8 Yield Data
		2.9 Soil Nutrient Analysis
	3 Results and Discussion
		3.1 Accuracy of Yield Prediction
			3.1.1 SVM
			3.1.2 ANN
			3.1.3 DNN
	4 Discussion
	5 Conclusion
	References
10 Socioeconomic Impact of IoT on Agriculture: A Comparative Study on India and China
	Abstract
	1 Introduction
	2 Food Security in India
	3 Food Security in China
	4 Internet of Things (IoT): An Introduction
		4.1 Development of IoT
	5 IoT in the Twenty-First Century
		5.1 IoT Ecosystem in India
		5.2 IoT Ecosystem in China
	6 IoT in Agriculture
		6.1 Socioeconomic Impact of IoT: Theoretical Considerations
		6.2 A Comparative Study of India and China
	7 Conclusion
	References
11 The Impact of Irrigation on Generation of Marketable Surplus in the Bolpur Subdivision, West Bengal
	Abstract
	1 Introduction
	2 Statement of Problems
	3 Literature Survey and Review on Generation of Marketable Surplus
	4 Objectives of the Research
	5 Database
	6 Methodology
	7 Location of the Study Area
	8 Impact of Irrigation on Marketable Surplus of Crops
		8.1 Marketable Surplus of Aman Paddy
		8.2 Marketable Surplus of Boro Paddy
		8.3 Marketable Surplus of Potato Crop
		8.4 Marketable Surplus of Mustard
		8.5 Marketable Surplus of Til
		8.6 Marketable Surplus of Mursuri
	9 Identification of Problems
	10 Policy Measure
	11 Major Findings
	12 Conclusion
	References
12 A Farmer-Friendly Connected IoT Platform for Predicting Crop Suitability Based on Farmland Assessment
	Abstract
	1 Introduction
		1.1 Contributions
		1.2 Organization of the Chapter
	2 Literature Review
	3 Proposed System
		3.1 Edge Units
			3.1.1 Macronutrient Sensor
			3.1.2 pH Sensor
			3.1.3 GPS Sensor
			3.1.4 Battery Module
		3.2 Relay Units
		3.3 Intelligence Unit
		3.4 Crop Prediction
	4 Results and Discussions
		4.1 Hardware Equipment’s Used
		4.2 Datasets Used and Locale Data Collection
			4.2.1 Local Data Collection
			4.2.2 Central Station Data Collection
			4.2.3 Intelligence Unit Calculations
	5 Conclusion and Future Scope
	References
13 Smart Farming with IoT: A Case Study
	Abstract
	1 Introduction
		1.1 Internet of Things: An Overview
		1.2 Internet of Things in the Field of Agriculture
	2 Organization of the Chapter
	3 Case Studies
	4 Conclusion
	References
14 Blockhain Solutions for Agro-Food Chain Systems
	Abstract
	1 Introduction
	2 Blockchain
	3 Blockhain Solutions for Agro-Food Chain
		3.1 Real-Life Applications
	4 Proposed Solution
	5 Conclusion
	References
15 Efficiency and Reliability of IoT in Smart Agriculture
	Abstract
	1 Introduction
		1.1 Contribution to GDP Over the years
		1.2 Importance of Agriculture on Economy
	2 Significant Parts of IOT-Based Cultivating
	3 IOT Agricultural Network Architecture
		3.1 Application Layer
		3.2 Transport Layer
		3.3 Network Layer
		3.4 Adaption Layer
		3.5 Physical and Mac Layer
	4 IOT Agricultural Network Platform
		4.1 Predictive Analysis
		4.2 Multicultural Analysis
		4.3 Physical Implementation
		4.4 Sensing and Monitoring
		4.5 Communication Protocol
		4.6 Storage Services
	5 IOT Agricultural Network Platform Based on Cloud
		5.1 IoT Agricultural Network Topology and Protocols
		5.2 IOT Protocols for Agriculture
		5.3 Agricultural Application Domain
		5.4 Precision Farming
		5.5 Climate Conditions Monitoring
		5.6 Soil Monitoring
		5.7 Pest and Crop Disease Monitoring
		5.8 Irrigation Monitoring System
		5.9 Agricultural Drones
		5.10 Green House Monitoring
		5.11 Livestock Monitoring
		5.12 Security Issues
	6 Conclusion
	References
	Journal Article
	Conference Paper
	Book Chapter
	Website
16 Architecture, Security Vulnerabilities, and the Proposed Countermeasures in Agriculture-Internet-of-Things (AIoT) Systems
	Abstract
	1 Introduction
	2 Overview of IoT
		2.1 Evolution of Internet to IoT
		2.2 Objectives of IoT
		2.3 IoT Enabling Technologies
			2.3.1 The Application Layer
			2.3.2 The Network Layer
			2.3.3 The Perception Layer
		2.4 Communication Technologies to IoT
			2.4.1 Radio-Frequency Identification (RFID)
			2.4.2 Long Term Evolution (LTE)
			2.4.3 Long Range (LoRa)
			2.4.4 Near-Field Communication (NFC)
			2.4.5 Machine-to-Machine (M2M)
	3 Agriculture IoT (AIoT)
		3.1 Definition and Development
		3.2 AIoT Architecture
		3.3 AIoT Applications
		3.4 Research Challenges of IoT in Agriculture
			3.4.1 Professional Agricultural Sensors
			3.4.2 Wireless Power Transfer and Ambient Energy Harvesting
			3.4.3 Cross-Media and Cross-Technology Communication
			3.4.4 Robust Wireless Networks
	4 Security and Privacy Issues in AIoT System
		4.1 Data Security and Privacy
		4.2 Authorization and Trust
		4.3 Authentication and Secure Communication
		4.4 Compliance and Regulations
			4.4.1 Contracts and Agreements
			4.4.2 Data Security and Privacy
			4.4.3 Intellectual Property (IP)
	5 Attacks in AIoT Systems
	6 Conclusions
	References
17 Protocols, Solutions, and Testbeds for Cyber-Attack Prevention in Industrial SCADA Systems
	Abstract
	1 Introduction
	2 SACDA System
		2.1 Architecture
			2.1.1 Operator
			2.1.2 Human Machine Interface
			2.1.3 Wide Area Network
			2.1.4 Master Terminal Unit
			2.1.5 Field Devices
			2.1.6 Remote Terminal Unit
		2.2 Traffic Properties in SCADA Network.
			2.2.1 Traffic Features
			2.2.2 Requirements for Quality of Service
			2.2.3 Update and Events’ Order
			2.2.4 Configuration for Protocols
			2.2.5 Addressing
		2.3 Security Requirements
			2.3.1 Confidentiality
			2.3.2 Integrity
			2.3.3 Availability
			2.3.4 Access Control
			2.3.5 Network Security
			2.3.6 Policy
		2.4 Security: IT Versus SCADA
			2.4.1 Security Concept
			2.4.2 Managing Vulnerabilities
			2.4.3 Operational Requirements
	3 Protocols Involved in SCADA
		3.1 Fieldbus-Based Protocols
			3.1.1 Bit-Bus
			3.1.2 Foundation Fields H1
			3.1.3 WorldFIP
			3.1.4 Distributed Network Protocol 3
		3.2 Ethernet-Based Protocol
			3.2.1 Foundation High-Speed Ethernet (HSE)
			3.2.2 International Electrotechnical Commission (IEC)
			3.2.3 Sercos III
		3.3 Serial-Based Protocols
			3.3.1 Modbus
			3.3.2 Unitronics PCOM
		3.4 Common Industrial Protocol
			3.4.1 Highway Addressable Remote Transducer (HART)
			3.4.2 Device Net
			3.4.3 EthernetNet/IP
			3.4.4 DC-Bus
	4 Key-Management Architecture for Secure SCADA Communications
		4.1 SCADA Key Establishment Protocol
		4.2 SCADA Key-Management Architecture Protocol
		4.3 The Logical Key Hierarchy Protocol
		4.4 Iolus Framework
		4.5 Advanced Key-Management Protocol
	5 SCADA Vulnerabilities and Recommendations
		5.1 SCADA Software and Hardware
		5.2 Vulnerability Due to Archaic Defensive Components
	6 Testbeds for SCADA System
		6.1 Physical Testbed
		6.2 Virtual Testbed
		6.3 Virtual Physical Testbeds
		6.4 Hybrid Testbed
	7 Conclusion
	References