Cover
Half Title
Title Page
Copyright Page
Table of Contents
Foreword
Preface
Acknowledgements
About the Editors
Contributors
Chapter 1 Smart Farming Using Artificial Intelligence, the Internet of Things, and Robotics: A Comprehensive Review
1.1 Introduction
1.2 The Role of Artificial Intelligence in Advanced Farming
1.2.1 The Fundamentals of AI Technologies Involved in Agriculture
1.2.2 AI in Crop Or Seed Selection
1.2.3 AI in Crop Management Practices
1.2.4 AI in Yield Prediction
1.2.5 AI in Pest and Weed Management
1.2.6 AI in Storing and Marketing Products
1.3 The Role of the Internet of Things in Advanced Farming
1.3.1 IoT-Based Soil Sampling
1.3.2 IoT-Based Disease and Pest Monitoring
1.3.3 IoT-Based Fertilization
1.3.4 IoT-Based Yield Monitoring
1.3.5 IoT-Based Irrigation
1.3.6 IoT-Based Food Safety and Transportation
1.4 The Role of Robotics in Advanced Farming
1.4.1 Robotics in Planting
1.4.2 Robotics in Weed Control and Spraying
1.4.3 Robotics in Field Inspection and Data Collection
1.4.4 Robotics in Harvesting
1.5 The Challenges and Recommendations of Indulging Technologies in Advanced Farming
1.6 Conclusion
References
Chapter 2 Towards the Technological Adaptation of Advanced Farming Through Artificial Intelligence, the Internet of β¦
2.1 Introduction
2.2 Technology in Advanced Farming
2.2.1 AI in Advanced Farming
2.2.2 IoT in Advanced Farming
2.2.3 Robotics in Advanced Farming
2.3 Challenges in Adoption of Technology
2.4 Conclusion
References
Chapter 3 Artificial Intelligence and the Blockchain in Smart Agriculture: Emergence, Opportunities, and Challenges
3.1 Introduction
3.2 Literature Review
3.2.1 Overview
3.2.2 Artificial Intelligence in Agriculture
3.2.3 Blockchain in Agriculture
3.3 Case Study: AgroChain β A Blockchain-Powered Transparent Marketplace
3.4 AI and Blockchain for Smart Agriculture: Future Research Dimensions
3.5 The Limitations of AI and the Blockchain in Smart Agriculture
3.6 Conclusion
References
Chapter 4 Artificial Intelligence and Internet of Things Enabled Smart Farming for Sustainable Development: The Future ...
4.1 Introduction
4.1.1 Challenges in Traditional Farming
4.2 Smart Farming
4.3 Smart Agriculture for Sustainable Development
4.4 AI in Agriculture
4.4.1 AI for Field Condition Management
4.4.2 AI for Crop Management
4.4.3 AI for Livestock Management
4.4.4 AI for Precision Agriculture
4.4.5 AI for Weather Forecasting
4.4.6 AI for Better Decision-Making
4.4.7 AI for Cost Savings
4.5 Machine Learning in Agriculture
4.5.1 Management of Species
4.5.2 Management of Field Conditions
4.5.3 Crop Management
4.5.4 Livestock Management
4.5.5 Models Behind
4.6 How Data Analytics Is Transforming Agriculture
4.6.1 Predictive Analytics
4.6.2 Recommendation System
4.6.3 Data Mining
4.7 Agricultureβs Data Analytics Benefits
4.8 The Challenges of AI in Agriculture
4.9 The IoT and Sensors in Agriculture
4.9.1 The Need for the IoT
4.9.2 Applications of the IoT
4.9.3 The Role of Sensors in the IoT
4.9.4 Sensors in Smart Farming
4.9.5 Architectural Design
4.9.6 ATmega328 Arduino Microcontroller
4.9.7 GSM Module
4.9.8 Supporting Technologies for Smart Farming
4.9.8.1 Zigbee
4.9.8.2 Bluetooth
4.9.8.3 Smartphones
4.9.8.4 Cloud Computing
4.9.9 How Is Data Collected From Sensors?
4.10 Drones in Agriculture
4.10.1 Drone Components
4.10.1.1 Types of Drones
4.10.1.2 UAVs in Smart Agriculture
Uses for Agricultural Drones
4.10.1.3 Steps for Capturing Data From an Agriculture Drone
4.10.2 Benefits of Drone Technology
4.11 Challenges and Future Opportunities in Farming
4.12 Conclusion
References
Chapter 5 A Science, Technology, and Society Approach to Studying the Cumin Revolution in Western India
5.1 Introduction
5.2 Methodology
5.3 Cumin Cultivation in Salt and Water Stress Areas of Patan
5.3.1 Desert Development Programmes and Cumin Cultivation in Patan
5.4 Climate Change and Its Affect On Cumin Cultivation
5.5 Socio-Economic Status of Patan Farmers
5.6 Need for Artificial Intelligence-Based Meteorological Developments in Rural Farming Practices
5.7 Conclusion and Future Perspective
5.8 Limitations of the Study
References
Chapter 6 The Role of Big Data in Agriculture
6.1 Introduction
6.2 Recent Study and Survey On Global Urbanization
6.3 What Role Does Big Data Play in Agriculture?
6.3.1 The Top Four Big Data Applications at the Farm
6.3.2 Challenges Presented By Implementing Big Data Solutions in Agriculture
6.4 Big Data in Precision Agriculture
6.4.1 Farmerβs Suitability for and Use of Meteorological Data
6.4.1.1 Creating Irrigation Schedules for the Farm
6.4.1.2 Amount of Renewable Energy That the Farm Will Receive
6.4.1.3 Assists in the Safe Handling of the Farm
6.4.2 Weather Forecasting Through Satellite
6.4.3 Forecasting Schedules Created Just for You
6.4.4 Weather Factors That Have an Impact On Farm Planning and Operations
6.5 Forecasting Floods
6.5.1 Flood Monitoring and Forecasting Are Difficult Tasks
6.6 What Role Do Automation and Big Data Play in Feeding the World?
6.6.1 The Benefits of Hydroponic Food Production
6.6.2 What Role Do Big Data and Automation Play in Hydroponics?
6.6.3 The Challenges of Automated Food Production
6.7 Conclusion
References
Chapter 7 Blockchain-Based Agri Manufacture Industry
7.1 Introduction
7.2 Background
7.3 Expand Manufacturing
7.4 Agri-Blockchain
7.4.1 Blockhain-Based Food Chain
7.4.2 Transactions
7.4.3 Crop Insurance
7.4.4 Traceability
7.5 Shifts in Manufacturing
7.6 Research Framework
7.7 Development of Agri-Blockchain
7.8 Research Process
7.9 Research Hypotheses
7.9.1 To the Body of Knowledge
7.9.2 To the Potential Clients
7.9.3 To the Stakeholders
7.9.4 Novel Theories/New Findings/Knowledge
7.10 Conclusion
References
Chapter 8 Agricultural Data Mining and Information Extraction
8.1 Introduction: Agriculture and Data Mining
8.2 Data Mining Techniques in Farming
8.2.1 Classification
8.2.2 Clustering
8.2.3 Association Analysis
8.2.4 Prediction
8.2.5 Data Mining With Other Methods
8.3 Case Studies in Agricultural Data Mining
8.3.1 Yield Prediction
8.3.2 Identification of Diseases
8.3.3 Identification of Weeds/Wildflowers
8.3.4 Crop Quality
8.3.5 Gathering of Species
8.3.6 Soil Management
8.4 Discussion
8.5 Research Challenges of Data Mining in Farming
8.5.1 Confidentiality
8.5.2 Quality and Accuracy of Information
8.5.3 Significance of Spatial Information
8.5.4 Inclusion of Farming Field Experience in Data Mining
8.5.5 Scalability of Data Mining Techniques
8.6 Conclusion and Future Scope
References
Chapter 9 Machine Learning and Its Application in Food Processing and Preservation
9.1 Introduction
9.2 Introduction to Machine Learning
9.3 Machine Learning Techniques and Algorithms
9.4 Machine Learning Algorithms
9.4.1 Naive Bayes
9.4.2 Support-Vector Machine
9.4.3 Neural Network
9.4.4 K-Nearest Neighbour
9.4.5 Decision Tree
9.5 Machine Learning Application to Food Processing and Preservation
9.5.1 Grading and Sorting of Fruits Using Artificial Intelligence
9.5.2 Grading and Sorting of Fruits Using Machine Learning
9.5.3 Grading and Sorting of Fruits Using Support-Vector Machine
9.5.4 Fruit Grading and Sorting Using Artificial Neural Network
9.5.5 Coffee Fruit Using Artificial Neural Network
9.5.6 Dragon Fruit Using Artificial Neural Network
9.5.7 Dates Using Artificial Neural Network
9.5.8 Oil Palm Fruits Using Hyperspectral and Machine Learning
9.5.9 Papaya Fruits Using Machine Learning
9.5.10 Orange Classification and Grading Using Machine Learning
9.5.11 Machine Learning for Automatically Detecting and Grading Multiple Fruits
9.6 Drying of Fruit and Vegetables
9.7 Detection of Quality of Oil
9.7.1 Detection of Quality of Olive Oil
9.7.2 Detection of Extra Virgin Olive Oil Quality
9.7.3 Detection of Quality of Edible Oils
9.7.4 Detection of Quality of Peanut, Soybean, and Sesame Oils
9.7.5 Detection of Quality of Sesame Oil
9.7.6 Detection of Sesame Oil Quality With Sunflower Oil, Hazel Oil, and Canola Oil
9.8 Food Recognition and Classification
9.9 Food Adulteration
9.10 Sensometric and Consumer Science
9.11 Production and Prediction of Bioactive Compounds in Plants
9.11.1 Bioactive Compounds in Tomatoes
9.11.2 Bioactive Compounds of Grape Skins
9.11.3 Artificial Neural Network for Prediction of Bioactive Constituents in Plants
9.12 Food Contamination and Spoilage
9.13 Conclusion
References
Chapter 10 Study of Disruptive Technologies for Sustainable Agriculture
10.1 Introduction
10.2 Disruptive Technologies in Sustainable Agriculture
10.2.1 Artificial Intelligence and Machine Learning
10.2.2 Big Data Analytics
10.2.3 Geographic Information System
10.2.4 Robotics
10.2.5 Drone Technology
10.2.6 Remote Sensing
10.2.7 Digital Image Processing
10.2.7.1 Image-Based Insight Generation
10.2.8 Cloud Computing
10.2.9 Internet of Things
10.2.10 Blockchain Technology
10.2.10.1 Blockchain in Agriculture
10.2.10.2 Decision Support System
10.3 Framework of Agriculture 4.0
10.4 Applications of Sustainable Agriculture
10.5 Challenges
10.6 Cloud-Based IoT Architecture
10.7 Conclusion
References
Chapter 11 Role of Dimensionality Reduction Techniques for Plant Disease Prediction
11.1 Introduction
11.2 Dimensionality Reduction Techniques
11.2.1 Principal Component Analysis
11.2.2 Kernel Principal Component Analysis
11.2.3 Singular Value Decomposition
11.2.4 Locality Preserving Projection
11.2.5 Locally Linear Embedding
11.2.6 Isomap
11.2.7 Multidimensional Scaling
11.2.8 T-Stochastic Neighbour Embedding
11.3 Role of Dimensionality Reduction Techniques for Plant Disease Prediction
11.4 Opportunities and Challenges of Applying DRTs for Plant Disease Prediction
11.5 Conclusion
References
Chapter 12 A Review of Deep Learning Approaches for Plant Disease Detection and Classification
12.1 Introduction
12.2 Major Crops and Their Disease Detection Using Deep Learning in India
12.2.1 Cereal Crop Disease Detection Using Deep Learning Methods
12.2.2 Oilseed Crop Disease Detection Using Deep Learning Methods
12.2.3 Cash Crop Disease Detection Using Deep Learning Methods
12.3 Deep Learning Architectures and Models for Crop Disease Detection
12.4 Standard Datasets Used for Crop Disease Detection
12.4.1 PlantVillage Dataset
12.4.2 PlantDoc Dataset
12.4.3 Cropped-PlantDoc Dataset
12.4.4 Plant Disease Symptoms Image Database (PDDB)
12.4.5 Northern Corn Leaf Blight (NCLB) Dataset for Maize
12.4.6 New Plant Diseases Dataset (Augmented)
12.4.7 Rice Leaf Diseases Dataset
12.4.8 Image Set for Deep Learning
12.4.9 UCI Plant Dataset
12.4.10 Michalskiβs Soybean Disease Database
12.4.11 Arkansas Plant Disease Database
12.4.12 One-Hundred Plant Species Leaves Dataset
12.5 Performance of Different Deep Learning Algorithms Used for Crops Disease Detection
12.5.1 Confusion Matrix
12.5.2 Classification Accuracy
12.5.3 Precision
12.5.4 Recall
12.5.5 F1-Score
12.6 Conclusion
References
Chapter 13 Cyber Threats to Farming Automation
13.1 Introduction
13.2 Farming Automation
13.3 Security in Farm Automation
13.3.1 Confidentiality
13.3.2 Integrity
13.3.3 Availability
13.4 Types of Cyber Threat in Farming Automation-Based Systems
13.4.1 Data Attacks
13.4.1.1 Ill-Intentioned Employee Data Leakage
13.4.1.2 Phishing Attack
13.4.1.3 False Data Injection Attack
13.4.2 Networking and Equipment Attacks
13.4.2.1 Radio Frequency Jamming Attack
13.4.2.2 Malware Injection Attack
13.4.2.3 Denial-Of-Service Attack
13.4.2.4 Side-Channel Attack
13.5 Artificial Intelligence and Machine Learning-Based Cybersecurity Use Cases
13.5.1 User Behaviour Modelling
13.5.2 Network Threat Identification
13.5.3 Email Monitoring
13.6 Future of Artificial Intelligence in Cybersecurity
13.7 Conclusion
References
Chapter 14 Prospects of Smart Farming as a Key to Sustainable Agricultural Development: A Case Study of India
14.1 Introduction
14.2 Smart Farming Tools for Future of Agriculture
14.3 Technological Advancements
14.4 Climate-Smart Agriculture
14.5 Evolution of Cutting-Edge Technologies That Are Revolutionizing the Agriculture Industry in India
14.5.1 Drones for Agriculture
14.5.2 Artificial Intelligence and Information Technology
14.5.3 Agricultural Mechanization
14.5.4 Agriculture Financing (AgriFin) Technology
14.5.5 Technology for Post-Harvesting
14.5.6 Animal Agriculture With Insight
14.5.7 Food and Agriculture Nanotechnology
14.5.8 Nanotechnology-Based Smart Pesticide Formulations
14.5.9 Microbe-Based Climate Smart Agriculture
14.5.10 Agriculture Smart Water Management Platform
14.5.11 Using High-Efficiency Sun Drying for Smart Agriculture
14.5.12 Cloud-Based Platform: Internet of Agriculture Things (IoAT)
14.5.13 Smart Multi-Sensor Platform in Agriculture Support for Analysis and Social Decision
14.5.14 Automation in the Agriculture Sector
14.6 How Can One Use Technology to Create Their Ideal Farmhouse?
14.6.1 Obtaining Weather Information
14.6.2 The National Agriculture Market (ENAM)
14.6.3 Unified Farmer Service Platform
14.6.4 Farmersβ Database
14.6.5 Benefits of the IoT in the Agricultural Sector
14.7 Smart Farmingβs Obstacles
14.8 Some Examples of Smart Farming Applications
14.8.1 Aquaculture
14.8.2 Potatoes and Water Conservation
14.8.3 Lettuces That Can Benefit People With Renal Illness
14.9 Future Scopes and Challenges
14.9.1 Scopes
14.9.2 Challenges
14.10 Conclusion
References
Index