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Climate Smart Agriculture

Edited by Anitha Velu, Prasanth Aruchamy, Raghu Ramamoorthy, Rajesh Kumar Dhanaraj, and Seifdine Kadry
Copyright: 2026   |   Expected Pub Date: 2026
ISBN: 9781394336333  |  Hardcover  |  
300 pages
Price: $225 USD
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One Line Description
Transform the future of sustainable farming with this guide to mastering the deep reinforcement learning architectures and algorithms that turn complex environmental data into precise, high-yield decisions for climate-smart agriculture. 

Audience
Academics, researchers, AI professionals, software engineers and developers, and IoT specialists who are working in the field of research and development of deep reinforcement learning in climate smart agriculture.

Description
A machine learning method called reinforcement learning trains computers to make decisions based on what would produce the best outcomes. It emulates how humans learn by making mistakes and trying again until they reach their objectives. There are a number of application domains supported by reinforcement learning, including robotics, agriculture, autonomous vehicles, healthcare, finance, and advertising. This book provides a detailed analysis of the climate smart agriculture situation as it stands, the roles and difficulties of farmers, and current technologies and application systems that are technology-enabled. It discusses the art of the deep reinforcement learning framework, algorithms, and architecture with various insights, as well as the challenges faced in reinforcement learning methods by framing out data privacy, security, and scalability in various applications related to climate smart agriculture, like yield prediction, crop management, crop disease prediction, soil health surveillance, precision agriculture, and environmental monitoring.

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Author / Editor Details
Anitha Velu, PhD is an Assistant Professor in the Department of Electronics and Communication Engineering at the Sri Sairam College of Engineering. She has published more than 15 papers in reputed journals and two patents, and has been granted two additional patents. Her research interests include image processing, VLSI design, ontology, and the semantic web.

Prasanth Aruchamy, PhD is an Associate Professor at Vel Tech Rangarajan Dr. Sagunthala Research and Design Institute of Science and Technology. He has published more than 45 research articles in the reputed international journals, ten patents, and more than 15 books. His research interests include the Internet of Things, blockchain, wireless sensor networks, medical image processing, and machine learning.

Raghu Ramamoorthy, PhD is an Assistant Professor in the Department of Computer Science and Engineering at the Oxford College of Engineering. He has published his research works in international journals and conferences of repute. His research focuses on wireless communications and vehicular ad hoc networks.

Rajesh Kumar Dhanaraj, PhD is a Professor at Symbiosis International University. He has authored and edited more than 50 books and 115 articles in international journals of repute and holds 22 patents. His research interests encompass machine learning, cyber-physical systems, and wireless sensor networks.

Seifedine Kadry, PhD is a Professor at Noroff University College. He has more than 1100 international publications to his credit. His research focuses on data science, education using technology, system prognostics, stochastic systems, and applied mathematics.

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Table of Contents
Preface
1. Deep Reinforcement Learning from the Perspectives of Artificial Intelligence and Optimal Control
J. Jesy Janet Kumari, Thangam S., Raghu Ramamoorthy and Anitha Velu
1.1 Smart Agriculture
1.1.1 Necessity of Smart Agriculture
1.2 Necessity for Deep Reinforcement Learning in Smart Agriculture
1.2.1 Importance of Reinforcement Learning
1.2.2 Deep Learning Approaches
1.3 Machine Learning
1.3.1 The Need for Artificial Neural Networks
1.3.2 Intelligent Artificial System
1.4 Applications of Deep Learning in Smart Agriculture
1.4.1 Land Cover Classification
1.4.2 Convolutional Neural Network
1.5 Impact of Deep Reinforcement Learning on Artificial Intelligence and Optimal Control
1.5.1 Input and Output
1.5.2 Automatic Network Construction
1.5.3 Training Step
1.5.4 Visibility of Fundamental Methods
1.6 The Challenges
1.7 Conclusions and Future Scope
References
2. Climate-Smart Agriculture: Adoption, Impacts, and Implications for Sustainable Development
J. Chandra Priya, G. Nanthakumar, C. Alamelu and Afizan Bin Azman
2.1 Overview of Climate Change Impacts on Agriculture
2.2 Mixed-Method Approach to Climate-Smart Agriculture
2.3 Multi-Stakeholder Technological Intervention Model
2.3.1 Stakeholder Categories
2.3.2 Vulnerability Context
2.3.3 Climate-Smart Agricultural and Technological Interventions
2.3.4 Integration of Indigenous Knowledge
2.3.5 Livelihood Outcomes
2.4 Integration of Artificial Intelligence and the Internet of Things in Precision Agriculture
2.4.1 Wireless Sensor Networks for Soil Monitoring
2.4.2 Near-Surface Camera Network for Monitoring within the Climate-Smart Agricultural Framework
2.4.3 Integrating Unmanned Aerial Vehicles and Artificial Intelligence for Precision Agriculture
2.5 The Internet-of-Things-Based Smart Farming Robots
2.6 Linking Weather and Climate Information Services with Climate-Smart Agriculture
2.6.1 Machine Learning for Weather Forecasts
2.7 Advanced Water Management Techniques
2.7.1 Optimizing Water Consumption through Artificial Intelligence-Based Irrigation Management
2.7.2 Internet of Things-Based Smart Irrigation Systems
2.8 Contribution of Agroforestry Practices and Renewable Energy
2.8.1 Precision Land Management for Agroforestry
2.9 Conclusion
References
3. Grokking Deep Reinforcement Learning for Climate-Smart Agriculture
N. Mythili, V. Saranya, P. Manjula and Raffaele Mascella
3.1 Introduction
3.2 Panoramic Perspective of Climate-Smart Agriculture
3.2.1 Climate-Smart Agricultural Policy
3.2.2 Outline of Climate-Smart Agriculture
3.2.3 Climate-Smart Agriculture as Sustainable Farming
3.3 Big Data
3.3.1 Data Collection
3.3.2 Edge Computing
3.3.3 Data Transmission Layer
3.3.4 Cloud Computing and Sequential Decision-Making
3.4 Machine Learning
3.5 Deep Reinforcement Learning
3.5.1 Deep Learning
3.5.1.1 Convolution Neural Networks
3.5.1.2 Recurrent Neural Networks
3.5.1.3 Generative Adversarial Networks
3.5.2 Reinforcement Learning
3.6 Various Monitoring Systems Using Deep Reinforcement Learning in Climate-Smart Agriculture
3.6.1 Crop Monitoring, Field Mapping Using Deep Reinforcement Learning
3.6.2 Seed Sowing and Water Management-Based Deep Reinforcement Learning
3.6.3 Pest, Weed Detection/Management Using Deep Reinforcement Learning
3.6.4 Fleet Management and Logistics Using Deep Reinforcement Learning
3.6.5 Livestock Management Using Deep Reinforcement Learning
3.7 Adaptation and Alleviation Strategies Under a Climate Change Scenario
3.8 Future Scope of Deep Reinforcement Learning in Climate-Smart Agriculture
3.9 Conclusion
References
4. Understand Cutting-Edge Reinforcement Learning Algorithms for Controlled Environment Agriculture
Udayakumar K., Revathi M., Sharmila L. and Muhammad Rukunuddin Ghalib
4.1 Introduction
4.1.1 The Role of Controlled Environment Agriculture
4.1.2 Significance of Automation in Controlled Environment Agriculture
4.2 The Notion of Reinforcement Learning in Controlled Environment Agriculture
4.3 Fundamentals of Reinforcement Learning
4.3.1 Cutting-Edge Reinforcement Algorithms in Controlled Environment Agriculture
4.3.1.1 Value-Based Reinforcement Learning Algorithm for Controlled Environment Agriculture
4.3.1.2 Policy-Based Reinforcement Learning Algorithms in Controlled Environment Agriculture
4.3.2 Comparison of Policy and Value-Based Method
4.4 Case Study: Irrigation System Using Deep Q-Network
4.5 Challenges and Potential Solutions
4.5.1 Technical Challenges
4.5.2 Environmental Challenges
4.5.3 Operational Challenges
4.5.4 Potential Solutions
4.6 Reinforcement Learning Integration with Other Emerging Technologies
4.6.1 Adaptation Analysis of Cutting-Edge Technologies in Controlled Environment Agriculture
4.6.1.1 Machine Learning
4.6.1.2 Deep Learning
4.6.1.3 Internet of Things and Sensors
4.6.1.4 Digital Twin
4.6.1.5 Reinforcement Learning
4.7 Conclusion
References
5. Augmented Reality-/Virtual Reality‑Assisted Deep Reinforcement Learning-Based Model toward Management of Soil Microbes on Organic Farms
G. Amuthavalli, U. Palani, G. Vallathan and Prasanth Aruchamy
5.1 Introduction
5.2 Soil Microbial Management Using Artificial Intelligence
5.3 Integration of Augmented Reality and Virtual Reality in Organic Farming
5.4 Augmented Reality-/Virtual Reality-Assisted Deep Reinforcement Learning Model for Soil Microbial Management
5.4.1 Framework of Augmented Reality-/Virtual Reality-Assisted Deep Reinforcement Learning-Based Model
5.4.2 Soil Contamination Identification by Augmented Reality Visualization
5.4.3 Virtual Reality Simulation-Based Prediction of Microbial Response to Contaminants
5.5 Real-World Applications and Their Challenges in Augmented Reality-/Virtual Reality-Assisted Organic Farming
5.6 Conclusion and Future Prospects
References
6. Intelligent Farm: An Automated Farming Technology Deploying Reinforcement Learning for Agroforestry Conservation Agriculture
K. Kalaivanan, V. Bhanumathi and Prasanth Aruchamy
6.1 Introduction to the Components of Intelligent Farming
6.2 Big Data Analysis
6.2.1 Data Acquisition
6.2.2 Pre-Processing
6.2.3 Data Processing and Analytics
6.2.4 Decision-Making and Visualization
6.3 Reinforcement Learning
6.3.1 Markov Decision Process
6.3.2 Q-Learning
6.3.3 Deep Q-Learning
6.3.4 Double Deep Q-Networks
6.3.5 Dueling Deep Q-Networks
6.4 Need for the Internet of Things in Smart Applications
6.4.1 Function of Internet of Things Elements
6.4.1.1 Cloud Computing
6.4.1.2 Fog Computing
6.4.1.3 Edge Computing
6.5 Challenges of the Internet of Things
6.5.1 Scalability
6.5.2 Interoperability
6.5.3 Latency
6.5.4 Security
6.5.5 Location Awareness
6.5.6 Mobility
6.5.7 Quality of Services
6.5.8 Availability
6.6 Smart Agriculture Applications
6.7 Conclusion
References
7. Overcoming Challenges of Data Privacy, Security, and Scalability for Commercial Grain Farming
S. Venkatesh, D. Jeevitha, B. Senthilkumaran and K. K. Devi Sowndarya
7.1 Introduction
7.1.1 Application of Data in Agriculture
7.1.2 Data Challenges in Climate-Smart Agriculture
7.2 Data Privacy in Agriculture
7.2.1 Data’s Significance in Agriculture
7.2.2 Initiatives to Mitigate Privacy Issues
7.3 Data Security in Agriculture
7.3.1 The Importance of Data in Agriculture
7.3.2 Challenges to Data Security
7.3.3 Applications in Agricultural Data Security
7.4 Privacy-Preserving Data Sharing Framework
7.4.1 Federated Learning Models
7.4.2 Key Benefits
7.4.3 Federated Learning Models for Climate-Smart Agriculture
7.4.4 Differential Privacy Mechanisms
7.4.5 Challenges of Differential Privacy for Climate-Smart Agriculture
7.5 Securing Agricultural Data Systems
7.6 Blockchain Can Enhance Climate-Smart Agriculture
7.7 Discussions
7.7.1 Farmer-Centric Data Ownership Policies
7.7.2 International Standards for Agricultural Data Security
7.8 Case Studies: Overcoming Challenges of Data Privacy, Security, and Scalability for Commercial Grain Farming
7.8.1 Case Study: Remote Sensing and Geographic Information Systems-Based Crop Monitoring
7.8.1.1 Initiation by the Indian Space Research Organization
7.8.1.2 Characteristics and Advantages
7.8.1.3 Challenges and Solutions
7.8.2 Case Study: e-Choupal by ITC
7.8.2.1 Overview and Implementation
7.8.2.2 Technology and Security
7.9 Conclusion
References
8. Seizing Opportunities in Integration of Reinforcement Learning with the Internet of Things for High-Tech Greenhouse and Vertical Farms
N. Sathish, V. Yokesh, Prasanth Aruchamy and Pham Chien Thang
8.1 Introduction
8.1.1 Overview of High-Tech Greenhouses and Vertical Farms
8.1.2 Role of the Internet of Things in Modern Agriculture
8.1.3 Potential of Reinforcement Learning in Smart Farming
8.1.4 Objectives and Scope of the Chapter
8.2 Background and Related Works
8.2.1 The Internet of Things in Agriculture: Current Trends and Challenges
8.2.1.1 Current Trends in the Internet of Things for Agriculture
8.2.1.2 Challenges Relative to the Implementation of the Internet of Things
8.2.2 Fundamentals of Reinforcement Learning
8.3 System Architecture for the Intelligence-of-Things-Driven Reinforcement Learning in Agriculture
8.3.1 Overview of Integrated Systems
8.3.2 Internet of Things Framework for High-Tech Greenhouses and Vertical Farms
8.3.3 Reinforcement Learning Framework
8.3.3.1 Environment
8.3.3.2 Agent
8.3.3.3 State Representation
8.3.3.4 Action Space
8.3.3.5 Reward Function
8.3.4 End-to-End System Integration
8.4 Key Applications and Use Cases
8.4.1 Climate Control and Energy Optimizations
8.4.1.1 Climate Control Strategies
8.4.1.2 Energy Optimization Techniques
8.4.2 Automated Irrigation and Nutrient Management
8.4.3 Pest and Disease Management
8.4.4 Crop Yield Prediction and Enhancement
8.4.5 Resource Management in Vertical Farming
8.4.5.1 Resources in Vertical Farming
8.4.5.2 Resource Management Strategies
8.5 Implementation Challenges and Solutions
8.6 Evaluation Metrics and Performance Analysis
8.6.1 Performance Metrics for the Internet of Things
8.6.2 Reinforcement Learning-Based Optimization Benchmarks
8.6.3 Comparative Analysis of the Internet of Things-Reinforcement Learning Systems
8.6.4 Insights from Experimental Results
8.7 Future Directions and Opportunities
8.7.1 Advanced Automation and Robotics
8.7.2 Integration of Artificial Intelligence and Machine Learning
8.7.3 Renewable Energy and Sustainability Initiatives
8.7.4 Multi-Crop and Specialized Farming
8.7.5 Vertical Farming in Urban Settings
8.7.6 Enhanced Lighting and Climate Control Systems
8.8 Conclusion
References
9. Case Study on the Initialization of Mapping between the Raw Data and Crop Yield Values for Yield Prediction
Dharani Jaganathan, Vishnu Kumar Kaliappan, Mani Deepak Choudhry and Sam Goundar
9.1 Role of Crop Yield Management Systems
9.1.1 Crop Yield Management in Addressing Climate and Environmental Challenges
9.2 Challenges in Handling Raw Agricultural Data
9.2.1 Data Heterogeneity and Integration
9.2.2 Data Quality and Noise
9.2.3 Temporal and Seasonal Variability
9.2.4 Timeliness and Real-Time Analysis
9.3 Reinforcement Learning for Crop Yield Prediction
9.4 Key Components of Reinforcement Learning
9.5 Reinforcement Learning Algorithms
9.5.1 Value-Based Methods (Q-Learning)
9.5.2 Policy-Based Methods (REINFORCE Algorithm)
9.5.3 Actor-Critic
9.5.4 How is Reinforcement Learning Suited for Crop Yield Data Mapping
9.6 Deep Q-Network
9.7 Deep Q-Network Algorithm
9.7.1 Q-Learning Update Rule
9.7.2 Integration of Neural Networks
9.7.3 Loss Function
9.7.4 Experience Replay
9.7.5 Target Network
9.8 Reinforced Random Forest
9.8.1 Data Preprocessing
9.8.2 Feature Standardization
9.8.3 Model Training and Validation
9.8.4 Q-Learning for Feature Selection
9.8.5 Reward Tracking and Analysis
9.9 Reinforced Linear Regression Feature Selector
9.9.1 Reward Tracking and Analysis
9.10 Experimental Setup and Parameter Optimization
9.11 Results and Discussion
9.12 Conclusion and Future Scope
References
10. Case Study on Reinforcement Learning-Based Decentralized Approach for Precision Agriculture and Environmental Monitoring
S. Vijayprasath, R. Mohan Raj, R. Sathesh Raaj and Ashok Manoharan
10.1 Introduction
10.1.1 Overview of Precision Agriculture
10.1.2 Environmental Monitoring in Agriculture
10.1.2.1 Monitoring Technologies for the Environment
10.1.3 Role of Reinforcement Learning
10.1.4 Objective of the Case Study
10.2 Fundamentals of Reinforcement Learning-Based Decentralized Systems
10.2.1 Overview of Reinforcement Learning
10.2.2 Decentralized Systems in Precision Agriculture
10.2.2.1 Integration of Reinforcement Learning in Decentralized Agricultural Systems
10.2.3 Environmental Monitoring Using Reinforcement Learning
10.3 System Design
10.3.1 System Architecture
10.3.2 Reinforcement Learning in Agriculture
10.3.3 Implementation of the Reinforcement Learning Model in Agriculture
10.3.4 Reinforcement Learning Decentralized Design in Environmental Monitoring
10.4 Real-Time Case Studies in the Use of Reinforcement Learning for Precision Agriculture and Environmental Monitoring
10.4.1 Case Study: Reinforcement Learning Applied to Precision Drip Irrigation of Sugarcane Farms
10.4.2 Case Study: Banana Farms in a Reinforcement Learning Decentralized Approach
10.4.3 Case Study: Reinforcement Learning Approach for Environmental Air Quality Monitoring
10.4.4 Case Study: Reinforcement Learning-Based Real-Time Flood Management System
10.5 Conclusion
References
11. Case Study on Crop Knowledge Discovery Based on Reinforcement Learning through Normalized, Homogenized, and Integrated Agricultural Data
M. Nalini, Kaarthica Gopi, S. Sathya Sai Ram and D. Rajesh Kumar
11.1 Insights on Machine Learning Techniques in Agriculture
11.2 Methodologies for Artificial Intelligence-Driven Crop Knowledge Discovery
11.2.1 Challenges
11.3 Implementation Reinforcement Learning
11.3.1 Agricultural Data Preparation
11.3.1.1 Types of Agricultural Data
11.3.1.2 Data Cleaning
11.3.1.3 Data Normalization
11.3.1.4 Data Homogenization
11.3.1.5 Data Integration
11.3.2 Reinforcement Learning
11.3.2.1 Basics of Reinforcement Learning
11.3.3 Deep Q-Learning for Crop Knowledge Discovery
11.3.3.1 Fundamentals of Deep Q-Learning
11.4 Advancing Agricultural Decision-Making with Reinforcement Learning
11.5 Conclusion
References
12. Case Study on Soil Health Surveillance: Establishing Reinforcement Learning for Decision-Making and Improving Product Quality
M. Nalini, Kaarthica Gopi, Sathya Sai Ram and Mariya Ouaissa
12.1 Soil Health Surveillance
12.2 Methods Used in Soil Health Surveillance
12.2.1 Challenges
12.3 Establishing Reinforcement Learning in Soil Health Monitoring
12.3.1 Soil Health Indicators
12.3.1.1 Physical Indicators
12.3.1.2 Chemical Indicators
12.3.1.3 Biological Indicators
12.3.2 Soil Sampling and Data Collection
12.3.3 Data Normalization
12.3.4 Integration of Sensor Data
12.3.5 Reinforcement Learning
12.3.5.1 Designing the Learning Model for Soil Surveillance
12.3.5.2 Proximal Policy Optimization
12.3.5.3 Algorithmic Workflow of the Proximal Policy Optimization
12.3.5.4 Proximal Policy Optimization for Soil Health Surveillance and Improved Product Quality
12.4 Conclusion
References
13. Case Study on Automated Crop Disease Detection and Classification Using Computer Vision and Reinforcement Learning Techniques
Fathima G., Sujatha S., Raghu Ramamoorthy and Pritha A.
13.1 A Run-Through on Artificial Intelligence in Agriculture
13.1.1 Overview
13.1.2 Technological Advancements in Agriculture
13.2 Background of Artificial Intelligence in Climate-Smart Agriculture
13.3 Detection and Classification of Plant Disease
13.3.1 Dataset Description
13.3.1.1 Fruit Dataset
13.3.1.2 Vegetable Dataset
13.3.2 Data Augmentation Techniques for Enhancing Model Generalizability in Reinforcement Learning
13.3.2.1 Random Jittering for Generalization
13.3.3 Employing Keras ImageDataGenerator for Image Preprocessing
13.3.4 Feature Extraction Using a Pre-Trained VGG16 Model
13.3.5 Model Training with a Convolutional Neural Network
13.3.6 Model Deployment in Application
13.3.7 Implementation
13.4 Results and Discussions
13.5 Conclusion
References
Index

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