Buch, Englisch, 320 Seiten
Buch, Englisch, 320 Seiten
ISBN: 978-1-394-33633-3
Verlag: Wiley
Transform the future of sustainable farming with this guide to mastering deep reinforcement learning architectures and algorithms that turn complex environmental data into precise, high-yield decisions for climate-smart agriculture. It conveys the importance of deep reinforcement learning and its technological advancements across climate-smart agriculture applications, addresses challenges related to privacy, security, and scalability of climatic and agricultural data, and explains reinforcement learning from AI and optimal control perspectives. The book explores advanced solutions such as meta learning, hierarchical learning, multi-agent learning, and imitation learning, emphasizing modern frameworks, algorithms, tools, and decision-making systems that support farmers through intelligent, data-driven applications.
A machine learning method called reinforcement learning trains computers to make decisions that produce optimal outcomes by learning through trial and error. Applicable across robotics, autonomous vehicles, healthcare, finance, and agriculture, reinforcement learning plays a critical role in modern intelligent systems. This book provides a detailed analysis of climate-smart agriculture, examining farmers’ challenges, current technology-enabled systems, and deep reinforcement learning frameworks, algorithms, and architectures. It also addresses data privacy, security, and scalability issues in applications such as yield prediction, crop management, disease prediction, soil health monitoring, precision agriculture, and environmental monitoring.
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Weitere Infos & Material
Preface xvii
1 Deep Reinforcement Learning from the Perspectives of Artificial Intelligence and Optimal Control 1
J. Jesy Janet Kumari, Thangam S., Raghu Ramamoorthy and Anitha Velu
1.1 Smart Agriculture 2
1.1.1 Necessity of Smart Agriculture 2
1.2 Necessity for Deep Reinforcement Learning in Smart Agriculture 2
1.2.1 Importance of Reinforcement Learning 4
1.2.2 Deep Learning Approaches 7
1.3 Machine Learning 8
1.3.1 The Need for Artificial Neural Networks 8
1.3.2 Intelligent Artificial System 8
1.4 Applications of Deep Learning in Smart Agriculture 10
1.4.1 Land Cover Classification 11
1.4.2 Convolutional Neural Network 11
1.5 Impact of Deep Reinforcement Learning on Artificial Intelligence and Optimal Control 12
1.5.1 Input and Output 12
1.5.2 Automatic Network Construction 12
1.5.3 Training Step 12
1.5.4 Visibility of Fundamental Methods 12
1.6 The Challenges 14
1.7 Conclusions and Future Scope 15
References 15
2 Climate-Smart Agriculture: Adoption, Impacts, and Implications for Sustainable Development 17
J. Chandra Priya, G. Nanthakumar, C. Alamelu and Afizan Bin Azman
2.1 Overview of Climate Change Impacts on Agriculture 18
2.2 Mixed-Method Approach to Climate-Smart Agriculture 21
2.3 Multi-Stakeholder Technological Intervention Model 22
2.3.1 Stakeholder Categories 23
2.3.2 Vulnerability Context 24
2.3.3 Climate-Smart Agricultural and Technological Interventions 25
2.3.4 Integration of Indigenous Knowledge 25
2.3.5 Livelihood Outcomes 26
2.4 Integration of Artificial Intelligence and the Internet of Things in Precision Agriculture 28
2.4.1 Wireless Sensor Networks for Soil Monitoring 28
2.4.2 Near-Surface Camera Network for Monitoring within the Climate-Smart Agricultural Framework 29
2.4.3 Integrating Unmanned Aerial Vehicles and Artificial Intelligence for Precision Agriculture 30
2.5 The Internet-of-Things-Based Smart Farming Robots 30
2.6 Linking Weather and Climate Information Services with Climate-Smart Agriculture 31
2.6.1 Machine Learning for Weather Forecasts 32
2.7 Advanced Water Management Techniques 33
2.7.1 Optimizing Water Consumption through Artificial Intelligence-Based Irrigation Management 33
2.7.2 Internet of Things-Based Smart Irrigation Systems 34
2.8 Contribution of Agroforestry Practices and Renewable Energy 35
2.8.1 Precision Land Management for Agroforestry 36
2.9 Conclusion 37
References 38
3 Grokking Deep Reinforcement Learning for Climate-Smart Agriculture 41
N. Mythili, V. Saranya, P. Manjula and Raffaele Mascella
3.1 Introduction 42
3.2 Panoramic Perspective of Climate-Smart Agriculture 43
3.2.1 Climate-Smart Agricultural Policy 43
3.2.2 Outline of Climate-Smart Agriculture 44
3.2.3 Climate-Smart Agriculture as Sustainable Farming 44
3.3 Big Data 45
3.3.1 Data Collection 45
3.3.2 Edge Computing 46
3.3.3 Data Transmission Layer 46
3.3.4 Cloud Computing and Sequential Decision-Making 47
3.4 Machine Learning 48
3.5 Deep Reinforcement Learning 49
3.5.1 Deep Learning 49
3.5.1.1 Convolution Neural Networks 49
3.5.1.2 Recurrent Neural Networks 51
3.5.1.3 Generative Adversarial Networks 51
3.5.2 Reinforcement Learning 52
3.6 Various Monitoring Systems Using Deep Reinforcement Learning in Climate-Smart Agriculture 53
3.6.1 Crop Monitoring, Field Mapping Using Deep Reinforcement Learning 53
3.6.2 Seed Sowing and Water Management-Based Deep Reinforcement Learning 54
3.6.3 Pest, Weed Detection/Management Using Deep Reinforcement Learning 54
3.6.4 Fleet Management and Logistics Using Deep Reinforcement Learning 55
3.6.5 Livestock Management Using Deep Reinforcement Learning 55
3.7 Adaptation and Alleviation Strategies Under a Climate Change Scenario 56
3.8 Future Scope of Deep Reinforcement Learning in Climate-Smart Agriculture 56
3.9 Conclusion 57
References 58
4 Understand Cutting-Edge Reinforcement Learning Algorithms for Controlled Environment Agriculture 61
Udayakumar K., Revathi M., Sharmila L. and Muhammad Rukunuddin Ghalib
4.1 Introduction 62
4.1.1 The Role of Controlled Environment Agriculture 63
4.1.2 Significance of Automation in Controlled Environment Agriculture 65
4.2 The Notion of Reinforcement Learning in Controlled Environment Agriculture 66
4.3 Fundamentals of Reinforcement Learning 68
4.3.1 Cutting-Edge Reinforcement Algorithms in Controlled Environment Agriculture 71
4.3.1.1 Value-Based Reinforcement Learning Algorithm for Controlled Environment Agriculture 71
4.3.1.2 Policy-Based Reinforcement Learning Algorithms in Controlled Environment Agriculture 73
4.3.2 Comparison of Policy and Value-Based Method 74
4.4 Case Study: Irrigation System Using Deep Q-Network 75
4.5 Challenges and Potential Solutions 77
4.5.1 Technical Challenges 77
4.5.2 Environmental Challenges 78
4.5.3 Operational Challenges 78
4.5.4 Potential Solutions 79
4.6 Reinforcement Learning Integration with Other Emerging Technologies 79
4.6.1 Adaptation Analysis of Cutting-Edge Technologies in Controlled Environment Agriculture 79
4.6.1.1 Machine Learning 79
4.6.1.2 Deep Learning 81
4.6.1.3 Internet of Things and Sensors 81
4.6.1.4 Digital Twin 81
4.6.1.5 Reinforcement Learning 81
4.7 Conclusion 82
References 83
5 Augmented Reality-/Virtual Reality-Assisted Deep Reinforcement Learning-Based Model toward Management of Soil Microbes on Organic Farms 85
G. Amuthavalli, U. Palani, G. Vallathan and Prasanth Aruchamy
5.1 Introduction 86
5.2 Soil Microbial Management Using Artificial Intelligence 88
5.3 Integration of Augmented Reality and Virtual Reality in Organic Farming 89
5.4 Augmented Reality-/Virtual Reality-Assisted Deep Reinforcement Learning Model for Soil Microbial Management 92
5.4.1 Framework of Augmented Reality-/Virtual Reality-Assisted Deep Reinforcement Learning-Based Model 92
5.4.2 Soil Contamination Identification by Augmented Reality Visualization 93
5.4.3 Virtual Reality Simulation-Based Prediction of Microbial Response to Contaminants 94
5.5 Real-World Applications and Their Challenges in Augmented Reality-/Virtual Reality-Assisted Organic Farming 95
5.6 Conclusion and Future Prospects 97
References 98
6 Intelligent Farm: An Automated Farming Technology Deploying Reinforcement Learning for Agroforestry Conservation Agriculture 101
K. Kalaivanan, V. Bhanumathi and Prasanth Aruchamy
6.1 Introduction to the Components of Intelligent Farming 102
6.2 Big Data Analysis 103
6.2.1 Data Acquisition 104
6.2.2 Pre-Processing 104
6.2.3 Data Processing and Analytics 104
6.2.4 Decision-Making and Visualization 104
6.3 Reinforcement Learning 105
6.3.1 Markov Decision Process 106
6.3.2 Q-Learning 107
6.3.3 Deep Q-Learning 107
6.3.4 Double Deep Q-Networks 108
6.3.5 Dueling Deep Q-Networks 108
6.4 Need for the Internet of Things in Smart Applications 109
6.4.1 Function of Internet of Things Elements 110
6.4.1.1 Cloud Computing 110
6.4.1.2 Fog Computing 111
6.4.1.3 Edge Computing 111
6.5 Challenges of the Internet of Things 113
6.5.1 Scalability 113
6.5.2 Interoperability 114
6.5.3 Latency 114
6.5.4 Security 114
6.5.5 Location Awareness 115
6.5.6 Mobility 115
6.5.7 Quality of Services 115
6.5.8 Availability 115
6.6 Smart Agriculture Applications 116
6.7 Conclusion 120
References 121
7 Overcoming Challenges of Data Privacy, Security, and Scalability for Commercial Grain Farming 125
S. Venkatesh, D. Jeevitha, B. Senthilkumaran and K. K. Devi Sowndarya
7.1 Introduction 126
7.1.1 Application of Data in Agriculture 126
7.1.2 Data Challenges in Climate-Smart Agriculture 127
7.2 Data Privacy in Agriculture 127
7.2.1 Data’s Significance in Agriculture 127
7.2.2 Initiatives to Mitigate Privacy Issues 128
7.3 Data Security in Agriculture 129
7.3.1 The Importance of Data in Agriculture 129
7.3.2 Challenges to Data Security 130
7.3.3 Applications in Agricultural Data Security 130
7.4 Privacy-Preserving Data Sharing Framework 131
7.4.1 Federated Learning Models 131
7.4.2 Key Benefits 132
7.4.3 Federated Learning Models for Climate-Smart Agriculture 133
7.4.4 Differential Privacy Mechanisms 134
7.4.5 Challenges of Differential Privacy for Climate-Smart Agriculture 135
7.5 Securing Agricultural Data Systems 136
7.6 Blockchain Can Enhance Climate-Smart Agriculture 136
7.7 Discussions 138
7.7.1 Farmer-Centric Data Ownership Policies 138
7.7.2 International Standards for Agricultural Data Security 138
7.8 Case Studies: Overcoming Challenges of Data Privacy, Security, and Scalability for Commercial Grain Farming 139
7.8.1 Case Study: Remote Sensing and Geographic Information Systems-Based Crop Monitoring 139
7.8.1.1 Initiation by the Indian Space Research Organization 139
7.8.1.2 Characteristics and Advantages 140
7.8.1.3 Challenges and Solutions 142
7.8.2 Case Study: e-Choupal by ITC 142
7.8.2.1 Overview and Implementation 142
7.8.2.2 Technology and Security 143
7.9 Conclusion 144
References 144
8 Seizing Opportunities in Integration of Reinforcement Learning with the Internet of Things for High-Tech Greenhouse and Vertical Farms 147
N. Sathish, V. Yokesh, Prasanth Aruchamy and Pham Chien Thang
8.1 Introduction 148
8.1.1 Overview of High-Tech Greenhouses and Vertical Farms 148
8.1.2 Role of the Internet of Things in Modern Agriculture 149
8.1.3 Potential of Reinforcement Learning in Smart Farming 150
8.1.4 Objectives and Scope of the Chapter 151
8.2 Background and Related Works 152
8.2.1 The Internet of Things in Agriculture: Current Trends and Challenges 152
8.2.1.1 Current Trends in the Internet of Things for Agriculture 152
8.2.1.2 Challenges Relative to the Implementation of the Internet of Things 152
8.2.2 Fundamentals of Reinforcement Learning 153
8.3 System Architecture for the Intelligence-of-Things-Driven Reinforcement Learning in Agriculture 153
8.3.1 Overview of Integrated Systems 153
8.3.2 Internet of Things Framework for High-Tech Greenhouses and Vertical Farms 154
8.3.3 Reinforcement Learning Framework 157
8.3.3.1 Environment 157
8.3.3.2 Agent 158
8.3.3.3 State Representation 158
8.3.3.4 Action Space 158
8.3.3.5 Reward Function 158
8.3.4 End-to-End System Integration 158
8.4 Key Applications and Use Cases 159
8.4.1 Climate Control and Energy Optimizations 159
8.4.1.1 Climate Control Strategies 159
8.4.1.2 Energy Optimization Techniques 160
8.4.2 Automated Irrigation and Nutrient Management 160
8.4.3 Pest and Disease Management 160
8.4.4 Crop Yield Prediction and Enhancement 162
8.4.5 Resource Management in Vertical Farming 162
8.4.5.1 Resources in Vertical Farming 162
8.4.5.2 Resource Management Strategies 163
8.5 Implementation Challenges and Solutions 165
8.6 Evaluation Metrics and Performance Analysis 165
8.6.1 Performance Metrics for the Internet of Things 165
8.6.2 Reinforcement Learning-Based Optimization Benchmarks 165
8.6.3 Comparative Analysis of the Internet of Things- Reinforcement Learning Systems 166
8.6.4 Insights from Experimental Results 166
8.7 Future Directions and Opportunities 167
8.7.1 Advanced Automation and Robotics 167
8.7.2 Integration of Artificial Intelligence and Machine Learning 167
8.7.3 Renewable Energy and Sustainability Initiatives 167
8.7.4 Multi-Crop and Specialized Farming 168
8.7.5 Vertical Farming in Urban Settings 168
8.7.6 Enhanced Lighting and Climate Control Systems 168
8.8 Conclusion 169
References 170
9 Case Study on the Initialization of Mapping between the Raw Data and Crop Yield Values for Yield Prediction 173
Dharani Jaganathan, Vishnu Kumar Kaliappan, Mani Deepak Choudhry and Sam Goundar
9.1 Role of Crop Yield Management Systems 174
9.1.1 Crop Yield Management in Addressing Climate and Environmental Challenges 174
9.2 Challenges in Handling Raw Agricultural Data 175
9.2.1 Data Heterogeneity and Integration 175
9.2.2 Data Quality and Noise 176
9.2.3 Temporal and Seasonal Variability 176
9.2.4 Timeliness and Real-Time Analysis 176
9.3 Reinforcement Learning for Crop Yield Prediction 177
9.4 Key Components of Reinforcement Learning 177
9.5 Reinforcement Learning Algorithms 178
9.5.1 Value-Based Methods (Q-Learning) 178
9.5.2 Policy-Based Methods (REINFORCE Algorithm) 178
9.5.3 Actor-Critic 179
9.5.4 How is Reinforcement Learning Suited for Crop Yield Data Mapping 180
9.6 Deep Q-Network 182
9.7 Deep Q-Network Algorithm 183
9.7.1 Q-Learning Update Rule 183
9.7.2 Integration of Neural Networks 183
9.7.3 Loss Function 183
9.7.4 Experience Replay 184
9.7.5 Target Network 184
9.8 Reinforced Random Forest 184
9.8.1 Data Preprocessing 185
9.8.2 Feature Standardization 186
9.8.3 Model Training and Validation 186
9.8.4 Q-Learning for Feature Selection 186
9.8.5 Reward Tracking and Analysis 187
9.9 Reinforced Linear Regression Feature Selector 188
9.9.1 Reward Tracking and Analysis 188
9.10 Experimental Setup and Parameter Optimization 189
9.11 Results and Discussion 189
9.12 Conclusion and Future Scope 190
References 192
10 Case Study on Reinforcement Learning-Based Decentralized Approach for Precision Agriculture and Environmental Monitoring 195
S. Vijayprasath, R. Mohan Raj, R. Sathesh Raaj and Ashok Manoharan
10.1 Introduction 196
10.1.1 Overview of Precision Agriculture 196
10.1.2 Environmental Monitoring in Agriculture 198
10.1.2.1 Monitoring Technologies for the Environment 198
10.1.3 Role of Reinforcement Learning 199
10.1.4 Objective of the Case Study 199
10.2 Fundamentals of Reinforcement Learning-Based Decentralized Systems 200
10.2.1 Overview of Reinforcement Learning 200
10.2.2 Decentralized Systems in Precision Agriculture 201
10.2.2.1 Integration of Reinforcement Learning in Decentralized Agricultural Systems 201
10.2.3 Environmental Monitoring Using Reinforcement Learning 202
10.3 System Design 203
10.3.1 System Architecture 203
10.3.2 Reinforcement Learning in Agriculture 204
10.3.3 Implementation of the Reinforcement Learning Model in Agriculture 204
10.3.4 Reinforcement Learning Decentralized Design in Environmental Monitoring 206
10.4 Real-Time Case Studies in the Use of Reinforcement Learning for Precision Agriculture and Environmental Monitoring 208
10.4.1 Case Study: Reinforcement Learning Applied to Precision Drip Irrigation of Sugarcane Farms 209
10.4.2 Case Study: Banana Farms in a Reinforcement Learning Decentralized Approach 210
10.4.3 Case Study: Reinforcement Learning Approach for Environmental Air Quality Monitoring 212
10.4.4 Case Study: Reinforcement Learning-Based Real-Time Flood Management System 214
10.5 Conclusion 215
References 216
11 Case Study on Crop Knowledge Discovery Based on Reinforcement Learning through Normalized, Homogenized, and Integrated Agricultural Data 219
M. Nalini, Kaarthica Gopi, S. Sathya Sai Ram and D. Rajesh Kumar
11.1 Insights on Machine Learning Techniques in Agriculture 220
11.2 Methodologies for Artificial Intelligence-Driven Crop Knowledge Discovery 221
11.2.1 Challenges 222
11.3 Implementation Reinforcement Learning 223
11.3.1 Agricultural Data Preparation 223
11.3.1.1 Types of Agricultural Data 223
11.3.1.2 Data Cleaning 224
11.3.1.3 Data Normalization 225
11.3.1.4 Data Homogenization 226
11.3.1.5 Data Integration 226
11.3.2 Reinforcement Learning 227
11.3.2.1 Basics of Reinforcement Learning 227
11.3.3 Deep Q-Learning for Crop Knowledge Discovery 230
11.3.3.1 Fundamentals of Deep Q-Learning 230
11.4 Advancing Agricultural Decision-Making with Reinforcement Learning 236
11.5 Conclusion 237
References 238
12 Case Study on Soil Health Surveillance: Establishing Reinforcement Learning for Decision-Making and Improving Product Quality 241
M. Nalini, Kaarthica Gopi, Sathya Sai Ram and Mariya Ouaissa
12.1 Soil Health Surveillance 242
12.2 Methods Used in Soil Health Surveillance 243
12.2.1 Challenges 245
12.3 Establishing Reinforcement Learning in Soil Health Monitoring 246
12.3.1 Soil Health Indicators 246
12.3.1.1 Physical Indicators 246
12.3.1.2 Chemical Indicators 247
12.3.1.3 Biological Indicators 248
12.3.2 Soil Sampling and Data Collection 248
12.3.3 Data Normalization 249
12.3.4 Integration of Sensor Data 250
12.3.5 Reinforcement Learning 251
12.3.5.1 Designing the Learning Model for Soil Surveillance 252
12.3.5.2 Proximal Policy Optimization 253
12.3.5.3 Algorithmic Workflow of the Proximal Policy Optimization 255
12.3.5.4 Proximal Policy Optimization for Soil Health Surveillance and Improved Product Quality 256
12.4 Conclusion 257
References 258
13 Case Study on Automated Crop Disease Detection and Classification Using Computer Vision and Reinforcement Learning Techniques 261
Fathima G., Sujatha S., Raghu Ramamoorthy and Pritha A.
13.1 A Run-Through on Artificial Intelligence in Agriculture 262
13.1.1 Overview 262
13.1.2 Technological Advancements in Agriculture 262
13.2 Background of Artificial Intelligence in Climate-Smart Agriculture 263
13.3 Detection and Classification of Plant Disease 265
13.3.1 Dataset Description 266
13.3.1.1 Fruit Dataset 266
13.3.1.2 Vegetable Dataset 267
13.3.2 Data Augmentation Techniques for Enhancing Model Generalizability in Reinforcement Learning 268
13.3.2.1 Random Jittering for Generalization 268
13.3.3 Employing Keras ImageDataGenerator for Image Preprocessing 270
13.3.4 Feature Extraction Using a Pre-Trained VGG16 Model 271
13.3.5 Model Training with a Convolutional Neural Network 271
13.3.6 Model Deployment in Application 272
13.3.7 Implementation 272
13.4 Results and Discussions 273
13.5 Conclusion 274
References 275
Index 277
