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Submit a ProposalIntercropping and Sustainable Agriculture Using Internet of Things and Artificial Intelligence
One Line Description
Unlock the future of sustainable farming with this definitive guide that transforms complex intercropping into a highly efficient, commercially viable, and scalable practice using AI, IoT, and big data analytics.
Description
The agricultural sector is undergoing a transformative shift through the fourth industrial revolution, often referred to as Agriculture 4.0. This new era emphasises digital transformation, automation, and data-driven decision-making, redefining conventional farming systems. Historically, intercropping has enhanced biodiversity, soil fertility, and resource optimisation. Yet, its wider adoption has been limited due to scalability and complexity, especially in industrial agriculture. This book addresses these barriers by introducing IoT, artificial intelligence, and big data analytics into intercropping systems, making sustainable farming both efficient and commercially viable. By concentrating on the synergies between intercropping and these cutting-edge technologies, the book offers a targeted, technically rich, and practically relevant roadmap for revitalising this sustainable method at scale. The book bridges the gap between scientific research, technological innovation, and field-level implementation. It serves as an essential resource for agricultural practitioners, policymakers, environmental strategists, and technology developers, delivering practical, scalable solutions for future-ready farming.
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The agricultural sector is undergoing a transformative shift through the fourth industrial revolution, often referred to as Agriculture 4.0. This new era emphasises digital transformation, automation, and data-driven decision-making, redefining conventional farming systems. Historically, intercropping has enhanced biodiversity, soil fertility, and resource optimisation. Yet, its wider adoption has been limited due to scalability and complexity, especially in industrial agriculture. This book addresses these barriers by introducing IoT, artificial intelligence, and big data analytics into intercropping systems, making sustainable farming both efficient and commercially viable. By concentrating on the synergies between intercropping and these cutting-edge technologies, the book offers a targeted, technically rich, and practically relevant roadmap for revitalising this sustainable method at scale. The book bridges the gap between scientific research, technological innovation, and field-level implementation. It serves as an essential resource for agricultural practitioners, policymakers, environmental strategists, and technology developers, delivering practical, scalable solutions for future-ready farming.
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Author / Editor Details
Vikram Singh, PhD is a Professor in the Department of Computer Science and Engineering at Chaudhary Devi Lal University, India. He has held key university positions, including Dean of Colleges, Dean of Academic Affairs, Dean of Research, and Director of the University Computer Centre. His teaching and research interests include operating systems, modelling and simulation, and machine learning.
Vikram Singh, PhD is a Professor in the Department of Computer Science and Engineering at Chaudhary Devi Lal University, India. He has held key university positions, including Dean of Colleges, Dean of Academic Affairs, Dean of Research, and Director of the University Computer Centre. His teaching and research interests include operating systems, modelling and simulation, and machine learning.
Mudassir Khan, PhD is an Assistant Professor of Computer Science at King Khalid University, Saudi Arabia, and a postdoctoral fellow at Multimedia University, Malaysia. His research interests include machine learning, deep learning, data science, and AI applications in medicine. He has published more than 150 research articles, edited numerous books, and authored four computer science books.
Varun Malik, PhD is an Associate Professor in the Department of Computer Applications at Chitkara University, Punjab, India. His research interests include blockchain, artificial intelligence, cloud computing, big data, and data science. He has more than 14 years of experience, serves as a reviewer for international journals, and holds more than 25 Indian patents and copyrights.
Satveer Kour, PhD is an Assistant Professor in the Department of Computer Engineering and Technology at Guru Nanak Dev University, India. Her research interests include wireless networks, mobile and vehicular ad hoc networks, and quantum computing. She has published more than 50 research articles and is a life member of the International Association of Engineers.
Surajit Mondal, PhD is associated with Amity University, Bengaluru, and has more than 12 years of experience in teaching, research, and project management. He has published more than 100 research articles, including more than 80 in Scopus- or SCI-indexed journals. He holds 98 patents in the areas of renewable energy, sustainability, energy management, and smart technologies.
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Table of Contents
Preface
1. Introduction to Intercropping 4.0
Akila M., Yazhini S.P., Ibtissam Chouja and Tanveer Ahmad
1.1 Introduction
1.1.1 Evolution of Traditional Intercropping to Smart Agriculture
1.1.1.1 From Traditional to Smart Intercropping
1.1.2 Benefits of Intercropping in Sustainable Agriculture
1.2 Technological Foundations of Intercropping 4.0
1.2.1 Role of Big Data and Machine Learning
1.2.1.1 Applications in Agriculture
1.2.1.2 Challenges in Adoption
1.2.1.3 Future Prospects
1.2.1.4 Significance for Intercropping 4.0
1.2.2 Application of IoT and Smart Sensors in Crop Management as an Intercrop
1.2.3 Major Elements of IoT-Based Crop Management
1.2.3.1 Physical Devices
1.2.3.2 Monitoring Systems
1.2.3.3 Data Acquisition and Analytics
1.2.3.4 Biosensors for Crop Health
1.2.4 The Advantages of IoT and Smart Sensors in Intercropping
1.3 Emerging Technologies
1.4 Intercropping, AI, and Big Data
1.4.1 Uses in Intercropping
1.5 Available Tools and Innovations in Intercropping 4.0
1.5.1 GIS and Remote Sensing Applications in Agriculture
1.5.2 Crop Yield Analysis, Damage Assessment, and Forecasting
1.5.3 Stress Management in Nutrients and Water
1.5.4 Flood Management and Water Resource Management
1.5.5 Mapping of Land Use and Land Cover
1.5.6 Agro-Meteorological Applications
1.5.7 Monitoring of Pest Infestation
1.6 Drones That Monitor and Intervene: Real-Time Soil and Crop Healthy Monitoring in Intercropping Systems
1.6.1 Drones Used in Intercropping
1.6.2 Uses of Drones in Intercropping
1.6.3 Intercropping Drone Sensor and Technologies
1.6.4 Advantages of the Intercropping System
1.7 Ecological and Agronomic Benefits of Intercropping
1.7.1 Enhancing Resource-Use Efficiency
1.7.2 Soil Fertility and Enhanced Biodiversity
1.7.3 Climate Adaptation and Resilience
1.7.4 Aspects of Sustainability and Profitability
1.8 Challenges and Constraints in Adoption of Intercropping 4.0
1.8.1 Knowledge, Financial, and Technical Barriers
1.8.2 Smallholder Farmer Constraints of Adopting
1.9 Conclusion
Acknowledgments
References
2. The Evolution of Agriculture 4.0: Intercropping Systems
for Sustainable Agriculture to Enhance Productivity and Resource Use Efficiency
Anita Rani Mehta, Pardeep Kumar, Sanjay Tyagi, Guru Prem and Amrutanshu Tyagi
2.1 Introduction
2.1.1 Overview and Types of Intercropping
2.1.2 Major Intercropping Types
2.2 Crop Geometry, Spatial Arrangement, and Plant Density in Intercropping Systems
2.3 Performance Assessment of Intercropping through Competitive Indices
2.4 Enhancing Resource Use Efficiency through Intercropping
2.4.1 Suppression of Insect Pests, Diseases, and Weeds
2.4.2 Soil Nutrient Dynamics in Legume Intercropping Systems
2.4.3 Conservation of Natural Resources
2.5 Analyzing Intercropping System’s Performance Using Competitive Indices
2.6 Challenges
2.7 Conclusions and Future Scope
Acknowledgments
References
3. Traditional Knowledge and Smart Farming in Transition:
An Interdisciplinary Approach to Sustainable Agriculture
Naresh Prajapati, Pradeep Kulshreshtha, Deepak Kumar Chauhan, Versha Vahini and Kokila Konasinghe
3.1 Introduction
3.2 Traditional Knowledge Systems in Indian Agriculture
3.3 Smart Farming and Digital Agriculture Technologies
3.4 The Sustainability Paradox: Technology vs. Ecology
3.5 Climate Resilience through Indigenous Practices
3.6 Synergistic Integration Models
3.7 Case Studies and Pilot Interventions
3.7.1 Case 1: Digital Green – Local Video for Local Knowledge (India)
3.7.2 Case 2: Potato Park – Blending Ancestral Biodiversity
with Modern Tools (Peru)
3.7.3 Additional Reflections and Comparative Cases
3.7.4 Key Insights
3.8 Legal, Ethical, and Policy Dimensions
3.8.1 Protecting Traditional Knowledge in Indian Law
3.8.2 Global Protocols: Equity and Contestations
3.8.3 Uncharted Territory: Smart Farming and Legal Ambiguity
3.8.4 The Role of Law Schools and Universities
3.9 Toward Agricultural Justice
3.10 Rethinking Innovation: From Top-Down Transfer to Participatory Co-Creation
3.11 Multi-Stakeholder Platforms for Co-Creation
3.12 Farmer–Scientist Collaborations: Reviving Trust
3.13 Equity as the Foundation: Gender and Caste Dynamics
3.14 Commons-Based Tools and Community Innovation
3.15 Toward a Participatory Policy Vision
3.16 Conclusion
Acknowledgments
References
4. Technologies Powering Smart Intercropping
Sandeep Kaur, Satveer Kour, Manjit Sandhu, Toufik Mzili and Amna Ikram
4.1 Introduction
4.1.1 Importance of Smart Technologies in Modern Agriculture
4.1.2 Objectives and Scope
4.2 Fundamentals of Intercropping Systems
4.2.1 Types of Intercropping
4.2.2 Agronomic Benefits and Limitations
4.2.2.1 Benefits of Intercropping
4.2.2.2 Limitations of Intercropping
4.2.3 Role of Biodiversity and Soil Health
4.3 Integration of Smart Technologies in Agriculture
4.3.1 Role of Artificial Intelligence (AI) and Machine Learning (ML)
4.3.2 Deep Learning (DL) for Crop Prediction and Health Analysis
4.3.3 Internet of Things (IoT) in Smart Farming
4.3.4 Remote Sensing and Precision Agriculture Tools
4.4 Smart Intercropping Framework
4.5 Applications of IoT and AI in Intercropping
4.5.1 Crop Pairing and Spatial Optimization
4.5.1.1 Role of IoT
4.5.1.2 Role of AI
4.5.2 Soil and Nutrient Management
4.5.2.1 Role of IoT
4.5.2.2 Role of AI
4.5.3 Water Conservation and Smart Irrigation
4.5.3.1 Role of IoT
4.5.3.2 Role of AI
4.5.4 Pest and Disease Prediction and Control
4.5.4.1 Role of IoT
4.5.4.2 Role of AI
4.6 Emerging Technologies and Innovations
4.6.1 Blockchain for Supply Chain Transparency
4.6.2 Robotics and Automation in Intercropping
4.6.3 Cloud and Edge Computing for Agricultural Intelligence
4.6.4 Mobile-Based Decision Support Platforms
4.7 Challenges and Limitations
4.7.1 Technological and Infrastructure Barriers
4.7.2 Data Privacy and Security Concerns
4.7.3 Climate Variability and Environmental Factors
4.8 Future Research Directions
4.9 Conclusion
Acknowledgments
References
5. Integration and Implementation of IoT in Intercropping
Kuldeep Singh Kaswan, Jagjit Singh Dhatterwal, Kiran Malik, Anju Kapri, Ayman Nayef Ahmad Alhalaybeh and Mohammad Mazhar Nezami
5.1 Introduction
5.2 Fundamentals of Intercropping Systems
5.3 IoT Architecture for Intercropping Applications
5.4 Sensor Technologies in Intercropping
5.5 Data Fusion and Analytics in Multi-Crop Environments
5.6 Actuation and Control Mechanisms
5.7 Energy Management in IoT-Enabled Intercropping
5.8 Conclusion
Acknowledgments
References
6. Intercropping 4.0: Harnessing IoT, AI, and Big Data for Sustainable Farming Practices
C.V. Suresh Babu, Sangeethapriya M. and Malini Premakumari William
6.1 Introduction
6.1.1 Background: Global Food Security and Intercropping Potential
6.1.2 Traditional Intercropping Limitations and Current Barriers
6.1.3 Emergence of Intercropping 4.0: Technological Integration
6.1.4 Research Objectives and Study Scope
6.2 Literature Review and Digital Agriculture Landscape
6.2.1 Intercropping Fundamentals and Agroecological Benefits
6.2.2 Digital Agriculture Adoption and IoT–AI Integration
6.2.3 Comparative Performance: Traditional vs. Technology-Enabled Intercropping
6.3 IoT Framework for Smart Intercropping
6.3.1 Conceptual Framework Architecture
6.3.2 IoT Implementation and Real-Time Monitoring
6.3.3 AI Layer: Predictive Analytics and Decision Support
6.3.4 Big Data Integration and Cross-Scale Intelligence
6.4 Methodology and Evaluation Framework
6.4.1 Research Design
6.4.2 Data Collection and Selection Process
6.4.3 Case Study Framework
6.4.4 Analytical Procedures
6.4.5 Evaluation Metrics and Indicators
6.4.6 Data Validation and Reliability
6.4.7 Ethical Considerations and Data Governance
6.4.8 Methodological Rigor and Limitations
6.5 Challenges and Implementation Roadmap
6.5.1 Technical and Economic Barriers
6.5.2 Data Governance and Scalability Requirements
6.5.3 Implementation Roadmap and Institutional Requirements
6.5.4 Future Viability and Research Priorities
6.6 Conclusions
6.6.1 Key Findings and Implications
6.6.2 Sustainability Implications and Policy Considerations
Acknowledgments
References
7. AIoT-Driven Environmental Intelligence for Sustainable Tea Destinations: Integrating Smart Climate, Soil, and Biodiversity Monitoring
Shuvasree Banerjee, Mohammad Badruddoza Talukder, Tathagata Banerjee and Mehmet Emin Baynazoglu
7.1 Introduction
7.2 Historical Trajectory and Rationale
7.3 An Overview of AIoT in Environmental Monitoring
7.4 Environmental Intelligence Powered by AIoT
7.4.1 What AIoT Is?
7.4.2 Applications in Agriculture and Tourism
7.4.2.1 Agriculture Applications
7.4.2.2 Tourism Applications
7.5 Key Advantages Over Traditional Monitoring
7.6 Technological Components
7.7 AIoT in Environmental Monitoring: A Conceptual Framework
7.7.1 IoT Sensor Networks for Data Collection
7.7.2 AI Processing and Predictive Analytics
7.7.3 Real-Time Dashboards
7.7.4 Decision and Actuation Layer
7.8 Climate Monitoring in Tea Destinations
7.9 Soil Health and Nutrient Management
7.10 Biodiversity Monitoring in Tea Destinations
7.11 Case Studies: AIoT in Sustainable Tea Destinations
7.11.1 Darjeeling
7.11.2 Boseong (South Korea)
7.11.3 Wuyi Mountain (China)
7.12 Challenges and Barriers
7.12.1 Technical
7.12.2 Economic
7.12.3 Social
7.12.4 Ethics and Governance
7.13 Policy and Management Recommendations
7.14 Implementation Roadmap
7.15 Evaluation Metrics and Evidence
7.16 Ethics, Equity, and Data Rights
7.17 Limitations and Research Agenda
7.18 Conclusion
Acknowledgments
References
8. The Need for IoT-Enabled Decision Making in Intercropping
Gayathri Ramesh, Raja Somasundaram, Rithik Prajwal, Madhu Shree Bheeman, Noor Hafizah Hassan and Saurav Mallik
8.1 Introduction
8.2 Limitations of Traditional Decision-Making Approaches
8.2.1 Cognitive Constraints in Complex Systems
8.2.2 Spatial and Temporal Information Deficits
8.2.3 Inadequacy of Conventional Advisory Systems
8.3 Role of IoT in Intercropping
8.3.1 Distributed Sensor Networks
8.3.2 Network Connectivity
8.3.3 Computing System
8.3.4 Decision Support Systems
8.4 Artificial Intelligence and Decision-Making Frameworks
8.4.1 Interaction Prediction in Crop with Machine Learning
8.4.2 Dynamic Resource Allocation by Reinforcement Learning
8.4.3 Edge AI Decision Engines to On-Farm Actions
8.4.4 Data Fusion: Soil, Microclimate, and Plant Phenology
8.4.5 Explainable Artificial Intelligence to Support Farmer-Centric Decisions
8.5 Application Areas and Case Studies
8.5.1 Intercropping between Cereal and Legume
8.5.2 Systems of Agroforestry
8.5.3 Vegetable Intercropping
8.6 Future Perspectives
References
9. Decision-Making for Sustainable Intercropping
Kiran Malik, Jagjit Singh Dhatterwal, Kuldeep Singh Kaswan,
Varun Malik, Saurav Mallik and Maram Bani Younes
9.1 Introduction
9.1.1 Rationale for Intercropping in Sustainable Agroecosystems
9.1.2 Evolution of Decision-Making Approaches in Cropping System Design
9.1.3 Key Challenges in Sustainable Intercropping Decisions
9.2 Conceptual Foundations of Decision-Making in Sustainable Intercropping
9.2.1 Systems Thinking in Intercropping Design
9.2.2 Multi-Dimensional Nature of Sustainability: Ecological, Economic, and Social
9.2.3 Stakeholder Engagement and Participatory Frameworks
9.3 Agronomic and Ecological Considerations
9.3.1 Species Selection: Complementarity, Competition, and Facilitation
9.3.2 Spatiotemporal Arrangement and Resource Partitioning
9.3.3 Soil Health, Nutrient Cycling, and Microbial Synergy in Intercrops
9.3.4 Pest, Disease, and Weed Dynamics in Mixed Cropping Systems
9.4 Decision-Support Frameworks for Intercropping
9.4.1 Multi-Criteria Decision Analysis (MCDA) in Cropping Choices
9.4.2 Analytic Hierarchy Process (AHP) and Pairwise Comparison Methods
9.4.3 Fuzzy Logic and Uncertainty Handling in Intercropping Planning
9.4.4 Optimization Models: Linear, Non-Linear, and Multi-Objective Approaches
9.5 Quantitative Models and Simulation Tools
9.5.1 Process-Based Models for Predicting Intercropping Performance
9.5.2 Agent-Based and System Dynamics Models in Decision-Making
9.5.3 Geospatial and Remote Sensing Inputs for Intercropping Planning
9.5.4 Integrating AI and Machine Learning for Adaptive Decision Systems
9.6 Socioeconomic and Policy Dimensions
9.6.1 Economic Viability Assessment: Profitability and Risk Trade-Offs
9.6.2 Farmer Perceptions and Behavioral Decision Drivers
9.6.3 Incentive Structures and Policy Interventions for Sustainable Intercropping
9.6.4 Market Linkages and Value-Chain Integration
9.7 Metrics and Indicators for Sustainability Evaluation
9.7.1 Environmental Indicators: Biodiversity, Soil Carbon, and Water Footprint
9.7.2 Economic Indicators: Gross Margins, Input Efficiency, and Cost-Benefit Ratios
9.7.3 Social Indicators: Labor Dynamics, Gender, and Community Well-Being
9.7.4 Composite Sustainability Indexes for Intercropping Systems
9.8 Case Studies and Practical Applications
9.8.1 Successful Intercropping Decision-Making Frameworks in Smallholder Systems
9.8.2 Decision Support in Mechanized and Large-Scale Intercropping
9.8.3 Lessons from Indigenous Knowledge and Traditional Multi-Crop Systems
9.9 Challenges, Trade-Offs, and Future Directions
9.9.1 Reconciling Conflicting Objectives in Intercropping Decisions
9.9.2 Data Gaps, Model Limitations, and Scaling Challenges
9.9.3 Integrating Climate Adaptation and Resilience in Decision Frameworks
9.10 Conclusion
Acknowledgments
References
10. Balancing Productivity and Sustainability: Challenges in Modern Agriculture
Yazhini S.P., Akila M. and Neha Gautham
10.1 Introduction
10.1.1 Irony of Higher Productivity vs. Nature Degradation
10.1.2 Sustainability of Agricultural Systems
10.1.3 Digital Technologies in Agricultural Makeovers
10.2 Idea of Sustainable Agriculture
10.2.1 Essentials of Sustainable Agriculture
10.2.1.1 Preservation and Improvement of Natural Resources
10.2.2 The Three Bottom Lines: People, Planet, and Profit
10.2.3 Agroecological Principles of Sustainability
10.2.4 SDGs and Sustainable Agriculture
10.2.5 Intercropping as a Practice in Sustainable Agriculture
10.3 Challenges in Modern Agriculture
10.3.1 The Overreliance on Chemical Inputs and Its Outcomes
10.3.2 Soil Erosion and Lack of Nutrient Balances
10.3.3 Water Shortage and Ineffective Irrigation Activities
10.3.4 Climate Change and Its Effects on Agriculture
10.3.5 Depletion of Biodiversity and Ecosystem Services
10.4 Intercropping as a Sustainable Agriculture Tool
10.5 The Part of Emerging Technologies in Sustainable Agriculture
10.6 Internet of Things (IoT) in Agriculture
10.7 Sustainable Agriculture Artificial Intelligence (AI)
10.8 IoT and AI Smart Intercropping Systems Integration
10.9 Challenges and Limitations
10.10 Prospects and Future Research
10.11 Conclusion
Acknowledgments
References
11. Psychological Resilience and Behavioral Adaptation in AI-Enabled Smart Agriculture
Durgeshwary Kolhe, Arshad Bhat, Firdous Ahmad Malik and Raja Nazakat Ali
11.1 Introduction
11.2 Psychological Resilience in Agricultural Contexts
11.3 AI and IoT in Smart Agriculture
11.4 Behavioral Adaptation to Smart Technologies
11.5 Linking Resilience with Technology Adoption
11.6 Case Studies and Empirical Insights
11.7 Designing Human-Centered Smart Agricultural Systems
11.8 Policy and Extension Interventions
11.9 Conclusion and Future Directions
Acknowledgments
References
12. Integrating Robotics and Automation in Modern Intercropping Systems
Arsh Beri, Anushka Bhatnagar, Gaganpreet Kaur, Mohd Asif Shah and Barga Mohammed Mujahid
12.1 Introduction
12.2 Applications of Robotics in Intercropping Systems
12.2.1 Agricultural Innovation
12.2.2 Accurate Planting and Sowing
12.2.3 Convergence of Livestock and Post-Harvesting
12.2.4 Agriculture and Watering Together
12.3 Benefits and Opportunities of Robotics in Agriculture
12.3.1 Improved Accuracy and Efficiency
12.3.2 Utilizing Resources in a Sustainable Way
12.3.3 Effectiveness of Work and Improvement of Skills
12.3.4 Data-Informed Choices
12.4 Challenges and Limitations of Robotics in Smart Agriculture
12.5 Case Study
12.6 Conclusion
Acknowledgments
References
13. Climate-Smart Intercropping: Adapting to Weather Changes
Rashid Abbas Khan, Zeeshan Ahmad, Rida Batool, Muhammad Iftikhar and Tauqeer Ahmed Qadri
13.1 Introduction
13.1.1 Climate Change and Weather Variability: Implications for Cropping Systems
13.1.1.1 Key Climate Stressors Affecting Cropping Systems
13.1.1.2 Impacts on Yield Stability, Pests, and Soil Health
13.1.2 Principles of Intercropping That Confer Climate Resilience
13.1.2.1 Resource Complementarity and Niche Differentiation
13.1.2.2 Risk Spreading and Buffering through Diversity
13.1.2.3 Biophysical Mechanisms of Climate Resilience
13.1.3 Climate-Smart Intercropping Strategies
13.1.3.1 Design, Pairing, and Temporal Strategies
13.1.3.2 Temporal Strategies: Relay and Simultaneous Intercropping
13.1.3.3 Soil and Water Management Complements
13.1.3.4 Adaptation Objectives of Climate-Smart Intercropping
13.1.3.5 Practical Design Guidelines for Smallholders and Commercial Systems
13.1.4 Role of IoT in Enabling Climate-Smart Intercropping
13.1.4.1 Sensors and Measurements for Climate-Smart
Decisions
13.1.4.2 Networks and Communication Infrastructure
13.1.4.3 Actuation and Precision Interventions
13.1.4.4 Data Flows and Real-Time Adaptive Management
13.1.5 Role of Artificial Intelligence and Decision Support
13.1.5.1 Predictive Models for Anticipating Climate and Crop Responses
13.1.5.2 Prescriptive Analytics and Optimization for Intercropping Systems
13.1.5.3 Image Analysis and Phenotyping for Crop Health and Stress Monitoring
13.1.5.4 Explainability and Farmer-Friendly Decision Support
13.1.6 Implementation Framework and Technology Adoption Pathway
13.1.6.1 Stepwise Pathway for Climate-Smart Intercropping
13.1.6.2 Economic Feasibility and Capacity Building
13.1.6.3 Farmer Training/Capacity Building
13.1.7 Socio-Economic, Institutional, and Policy Considerations
13.1.8 Conclusion
Acknowledgments
References
14. Economic and Environmental Benefits of IoT-Enabled Farming
Sithandekile Sibanda
14.1 Introduction
14.1.1 Global Context and Urgency
14.1.2 Technological Innovation and Quantifiable Sustainability Results
14.2 IoT-Based Agriculture Economic Values
14.2.1 Workforce Cost Saving and Efficiency
14.2.2 Comparative Analysis: Traditional and IoT-Based Operations
14.2.3 Trends in Market Growth and Investment
14.2.4 Productivity Improvement and Quality
14.3 Farming IoT-Enabled: Environmental Benefits
14.3.1 Resource Conversation and Optimization of Inputs
14.3.2 Greenhouse Gas Emission Reduction
14.3.3 Conserving Biodiversity and Ecosystem Health
14.3.4 Environmental Indicator Traditional Farming IoT-Enabled
Farming Improvement Source
14.3.5 Climate Resilience and Adaptation
14.3.6 Sensing Technologies and System Architecture – IoT Core
14.4 Data Acquisition and Sensor Technologies
14.4.1 Network Architecture and Communication Technologies
14.4.2 Commercial Internet of Things Agricultural Applications
14.5 Case Studies: IoT-Enhanced Agriculture Implementation on a Global Scale
14.5.1 Case Study Analysis Regionally
14.5.2 Case Study Insights and Success Factors
14.6 Future Projections: AIoT Integration and Future Innovations
14.6.1 Agriculture-Based Artificial Intelligence of Things
(AIoT)
14.6.2 Edge Computing and Real-Time Analytics
14.6.3 Integrating Blockchain to Provide Transparency and Traceability
14.6.4 Autonomous Farming Systems and Robotics
14.6.5 Advanced Data Sensing and Multi-Mode Data Integration
14.6.6 Improved Connectivity: 5G and Future Networks
14.6.7 Circular Economy and Sustainability Integration
14.7 Difficulties and Obstacles to IoT Implementation
14.7.1 Economic and Financial Obstacles
14.7.2 Social and Human Factors
14.7.3 Data Privacy and Security Concerns
14.7.4 Policy and Regulatory Gaps
14.8 Policy Recommendations and Conclusions
14.8.1 Summary of Key Findings
14.8.2 Policy Recommendations
14.9 Conclusion
Acknowledgments
References
15. Adoption and Impact of AI, 5G, and Autonomous Systems on Intercropping Efficiency: A Cross-Country Analysis of the USA, Japan, China, and India
Saurabh Mishra, Pardeep Bawa Sharma, Vikas Tyagi, András Szeberényi and Nadeesha Muthuthantrige
15.1 Introduction
15.2 Need for the Study
15.3 Literature Review
15.3.1 Artificial Intelligence in Intercropping Systems
15.3.2 5G and Real-Time Communication in Agriculture
15.3.3 Autonomous Farming Technologies and Labor Substitution
15.3.4 Policy, Infrastructure, and Adoption Readiness
15.3.5 Intercropping and Sustainable Intensification
15.3.6 Study Objectives
15.4 Research Methodology
15.4.1 Quantitative Component
15.4.2 Qualitative Component
15.4.3 Data Analysis and Presentation
15.4.4 Ethical Considerations
15.5 Findings
15.6 Recommendations for India
15.7 Limitations and Scope for Future Research
15.8 Conclusion
Acknowledgments
References
16. The Green Paradox in Agriculture 4.0: Sustainability Challenges in Agricultural Practices
Jay Prakash Maurya, Ajay Sharma, Chour Singh Rajpoot, Ghanshyam G. Tejani and Zainab Aizaz
16.1 Introduction
16.1.1 Historical Background Origins of the Green Paradox and Evolution of Agriculture 4.0
16.1.1.1 Need and Importance
16.1.1.2 Applications of Agriculture 4.0 and Potential Paradox Pathways
16.1.1.3 Past Research: Theoretical and Empirical Findings
16.1.1.4 Research Gaps
16.2 Literature Review
16.3 Implementation Method
16.3.1 Policy Announcement and Timing Effects
16.3.2 Technology-Driven Rebound Effect
16.3.3 Capital Lock-In Under Digital Incentives
16.4 Results
16.5 Conclusion
Acknowledgments
References
17. Economic Growth and Environmental Sustainability through IoT-Powered Farming
Amanpreet Singh, Rupinder Singh, Jaswinder Singh, Satveer Kour and R. John Martin
17.1 Introduction
17.2 Motivation and Contribution
17.3 Human Factors and Skill Gaps
17.4 Conclusion and Future Work
Acknowledgments
References
18. Mathematical Modeling and Deep Learning Fusion for Soil
Moisture Forecasting in Precision Agriculture
Randhir Singh Baghel, Govind Shay Sharma, Harkaran Singh and Mudassir Khan
18.1 Introduction
18.2 Literature Review
18.2.1 Soil Moisture Models
18.2.2 Machine Learning and Deep Learning in Agriculture
18.2.3 Previous Hybrid Modeling Attempts
18.2.4 Gaps Identified in Current Research
18.3 Methodology
18.3.1 Mathematical Modeling
18.3.1.1 Governing Equations
18.3.1.2 Boundary and Initial Conditions
18.3.1.3 Numerical Solution Method
18.3.2 Deep Learning Model
18.3.2.1 Model Architecture
18.3.2.2 Input Features
18.3.2.3 Data Pre-Processing
18.3.2.4 Model Training Details
18.3.3 Hybrid Fusion Approach
18.3.3.1 Method of Fusion
18.3.3.2 Mathematical Formulation
18.3.3.3 Training Strategy
18.4 Data and Experimental Setup
18.4.1 Study Site and Simulation Environment
18.4.2 Datasets Used
18.4.2.1 In Situ Soil Moisture Sensors
18.4.2.2 Satellite and Remote Sensing Data
18.4.2.3 Weather Data
18.4.3 Model Training and Testing Procedures
18.5 Results and Discussion
18.5.1 Comparison of Modeling Approaches
18.5.2 Quantitative Evaluation
18.5.3 Visualizations
18.5.4 Impact of Environmental Variables
18.5.5 Generalizability and Robustness
18.6 Conclusion
Acknowledgments
References
19. Supply Chain Optimization for Fresh Agricultural Products: Role of Distribution Centers in Agricultural Superdocking
Anshraj, Rohit Raj, Monika, Govind Shay Sharma and Chatchawan Panraksa
19.1 Introduction
19.2 Literature Review
19.3 Result and Discussion
19.4 Conclusion
19.5 Future Research and Practical Implications
Acknowledgments
References
20. Innovative Robotics and Automation Approaches in Intercropping
Abiban Kumari, Jaswinder Singh, Alok K. Kushwaha and Vadthya Lokya
20.1 Introduction
20.2 Intercropping System
20.3 Advantages of Intercropping
20.4 Automation in Intercropping System
20.4.1 Automated Process in the Intercropping System
20.5 Role of Robotics in the Intercropping System
20.6 Integration of Data-Driven Technologies (AI, Internet of Things, Robotics, and Machine Vision)
20.7 Conclusion
Acknowledgements
References
21. Modernizing Intercropping with IoT, AI, and Big Data
Technologies
Garima, Arti, Somesh and Mudassir Khan
21.1 Introduction
21.2 Literature Review
21.3 Methodology
21.3.1 Research Design
21.3.2 Data Sources
21.3.3 Analytical Framework
21.3.4 Limitations
21.4 Intercropping 4.0 Framework
21.4.1 IoT in Intercropping
21.4.2 AI in Intercropping
21.5 Case Studies
21.6 Challenges and Limitations
21.7 Future Directions
21.8 Conclusion
Acknowledgments
References
22. Rethinking Intercropping with AI, 5G, and Automation:
Towards Data-Driven Sustainable Agriculture
Raj Bhattacharjee, Kanimozhi S., Nayef Ahmad Alhalaybeh and Mohammad Mazhar Nezami
22.1 Introduction
22.2 Related Works
22.3 Intercropping: Principles and Advantages
22.4 Digital Agriculture Foundations
22.5 Case Study Context and Objectives
22.6 Integration of AI and Data-Driven Tools in Intercropping
22.6.1 Crop Monitoring and Classification
22.6.2 IoT and Edge Devices for Real-Time Monitoring
22.6.3 Predictive Analytics and Decision Support
22.6.4 Integration with Digital Plant Pathology
22.6.5 Synergies of AI, 5G, and Automation
22.7 5G, Robotics, and Automation Applications
22.8 Practical Outcomes
22.9 Research Gaps and Challenges
22.10 Future Directions
22.11 Conclusion
Acknowledgements
References
23. Strengthening Intercropping through IoT-Based Decision
Support
Tarun Kumar, Sonia Suneja, Jyoti Saini, Kanwal Garg, Dolly Rani, Renu Popli and Kamini
23.1 Background and Motivation
23.2 Why Intercropping, and Why Now?
23.3 Limitations of Conventional Decision-Making
23.4 IoT as an Enabler for Data-Driven Intercropping
23.5 From Monitoring to Decisions: What Changes in Intercropping
23.6 Real-World IoT Deployments in Intercropping
23.7 Practical Implementation of IoT in Intercropping
23.8 Benefits and Challenges
23.9 Sustainability and Equity Lens
23.10 Scope and Contributions
23.11 Visionary Outlook
Acknowledgments
References
24. Smart Intercropping Systems: Leveraging IoT, Big Data,
and AI-Based Predictive Models
Sakshi Gupta, Manorama Patnaik, Mehak Singh, Umesh Gupta, Varun Malik and Mudassir Khan
24.1 Introduction
24.2 Literature Review
24.3 Concept of Intercropping 4.0
24.4 Role of IoT in Modern Intercropping
24.5 Role of Artificial Intelligence in Intercropping Management
24.6 Methodology
24.7 Role of Big Data and Analytics
24.8 Integration of IoT, AI, and Big Data
24.9 Case Studies
24.10 Benefits and Impacts
24.11 Challenges and Limitations
24.12 Future Prospects and Innovations
24.13 Conclusion
Acknowledgments
References
25. Precision-Driven Multi-Cropping Revolution: Integrating IoT, Artificial Intelligence, and Big Data Analytics for Sustainable Intercropping 4.0 Systems
Kalpavi C. Y., Pushpa Mala S., Dilip R., Sujatha B. M., Manjunatha R. C. and Ajit Danti
25.1 Introduction
25.1.1 Challenges and Technological Opportunities in Agricultural Systems
25.1.2 Digital Agriculture Revolution and Technological Convergence
25.1.3 Intercropping 4.0: A New Paradigm for Sustainable Agriculture
25.1.4 Research Objectives and Contributions
25.2 Literature Review
25.2.1 Theoretical Foundations of Intercropping
25.2.2 Digital Technology Integration in Agriculture
25.2.2.1 Internet of Things (IoT) Applications in Agriculture
25.2.2.2 Decision-Making Artificial Intelligence Applications in Agricultural Systems
25.2.2.3 Big Data Analytics Platforms
25.3 Methodology
25.3.1 Literature Search Strategy and Database Selection
25.3.2 Data Extraction Protocol and Quality Assessment
25.4 Problem Statement
25.4.1 Management Complexity
25.4.2 Scale Limitations
25.4.3 Environmental Variability
25.5 Results and Discussion
25.5.1 Comprehensive Yield Performance Analysis
25.5.2 Multi-Dimensional Evaluation of Performance and Optimizing the System
25.5.3 Regional Adoption Patterns and Technology Diffusion
25.5.4 Economic Performance Trends and Financial Viability
25.5.5 Resource Efficiency Benefits and Environmental Benefits
25.5.6 Sustainability Impact Assessment and Environmental Outcomes
25.5.7 Technology Adoption Issues and Implementation Patterns
25.6 Analysis and Implications
25.6.1 Performance Drivers and Technological Mechanisms
25.6.1.1 Management of Resource
25.6.1.2 Data-Driven Decision Making
25.6.1.3 Automated Management Systems Integration
25.6.2 Economic Viability Checkup and Investment Analysis
25.6.2.1 Analysis by Farm Scale for Investment
25.6.2.2 Economic Improvement Moving Forces
25.6.3 Implementation and Factors of Scalability
25.6.3.1 Technical Scalability Considerations
25.6.3.2 Factors of Economic Scalability
25.6.3.3 Social Scalability Requirements
25.6.4 Barriers to Adoption and Mitigation Strategies
25.6.4.1 Technical Barriers and Solutions
25.6.4.2 Economic Barriers and Solutions
25.6.4.3 Knowledge Barriers and Solutions
25.7 Future Scope and Research Directions
25.7.1 Technological Advances and Innovation Opportunities
25.7.2 Research Priorities and Knowledge Gaps
25.7.3 Policy and Governance Considerations
25.8 Conclusion
25.8.1 Implications for Stakeholders
25.8.2 Future Outlook
25.8.3 Final Recommendations and Strategic Priorities
Acknowledgments
References
26. Next-Generation Intercropping: A Data-Driven Approach
Using IoT and AI Technologies
Sarbjeet Singh, Harjinder Kaur, Satveer Kour, Toufik Mzili and Mudassir Khan
26.1 Introduction
26.1.1 The Evolution of Intercropping
26.1.2 The Emergence of Agriculture 4.0
26.1.3 Bridging Tradition and Technology: Intercropping 4.0
26.2 The Internet of Things (IoT) in Intercropping 4.0
26.2.1 The IoT Ecosystem in the Field
26.2.2 In-Field Acoustic Sensing: Listening to the Farm
26.3 Data Fusion Algorithms: Building the Digital Twin
26.3.1 Real-Time Data Collection and Monitoring
26.3.2 Automated Resource Management
26.3.3 Drones and Robotics: The Eyes and Hands of the Farm
26.4 Big Data Infrastructure and Architectures for I4.0
26.4.1 Challenges of Data Volume, Velocity, and Variety in Polyculture Systems
26.4.2 Cloud–Edge–Device Collaborative Computing Paradigm
26.5 Cloud–Edge–Device Collaborative Computing Paradigm
26.5.1 The Device Layer (Sensors and Actuators)
26.5.2 The Edge Layer (Local Processing and Immediate Action)
26.5.3 The Cloud Layer (Global Optimization and Long-Term
Intelligence)
26.5.4 Challenges in Deployment
26.6 Data Interoperability, Standardization, and Hierarchical Data Fusion
26.7 Artificial Intelligence (AI) and Machine Learning (ML) for Intercrop Optimization
26.7.1 Predictive Analytics for Yield and Resource Needs
26.7.2 Computer Vision for Crop Health Monitoring
26.7.3 AI-Powered Decision Support Systems
26.7.4 Optimizing Plant and Row Configurations
26.8 Big Data Analytics: The Foundation for Precision Intercropping
26.8.1 The Intercropping Data Tsunami
26.8.2 Data Integration and Centralized Platforms
26.8.3 Insights from Historical and Real-Time Data
26.8.4 Challenges of Big Data in Intercropping
26.9 Case Studies and Applications
26.10 Conclusion: The Sustainable Future of Farming
Acknowledgements
References
Index
Back to Top
Preface
1. Introduction to Intercropping 4.0
Akila M., Yazhini S.P., Ibtissam Chouja and Tanveer Ahmad
1.1 Introduction
1.1.1 Evolution of Traditional Intercropping to Smart Agriculture
1.1.1.1 From Traditional to Smart Intercropping
1.1.2 Benefits of Intercropping in Sustainable Agriculture
1.2 Technological Foundations of Intercropping 4.0
1.2.1 Role of Big Data and Machine Learning
1.2.1.1 Applications in Agriculture
1.2.1.2 Challenges in Adoption
1.2.1.3 Future Prospects
1.2.1.4 Significance for Intercropping 4.0
1.2.2 Application of IoT and Smart Sensors in Crop Management as an Intercrop
1.2.3 Major Elements of IoT-Based Crop Management
1.2.3.1 Physical Devices
1.2.3.2 Monitoring Systems
1.2.3.3 Data Acquisition and Analytics
1.2.3.4 Biosensors for Crop Health
1.2.4 The Advantages of IoT and Smart Sensors in Intercropping
1.3 Emerging Technologies
1.4 Intercropping, AI, and Big Data
1.4.1 Uses in Intercropping
1.5 Available Tools and Innovations in Intercropping 4.0
1.5.1 GIS and Remote Sensing Applications in Agriculture
1.5.2 Crop Yield Analysis, Damage Assessment, and Forecasting
1.5.3 Stress Management in Nutrients and Water
1.5.4 Flood Management and Water Resource Management
1.5.5 Mapping of Land Use and Land Cover
1.5.6 Agro-Meteorological Applications
1.5.7 Monitoring of Pest Infestation
1.6 Drones That Monitor and Intervene: Real-Time Soil and Crop Healthy Monitoring in Intercropping Systems
1.6.1 Drones Used in Intercropping
1.6.2 Uses of Drones in Intercropping
1.6.3 Intercropping Drone Sensor and Technologies
1.6.4 Advantages of the Intercropping System
1.7 Ecological and Agronomic Benefits of Intercropping
1.7.1 Enhancing Resource-Use Efficiency
1.7.2 Soil Fertility and Enhanced Biodiversity
1.7.3 Climate Adaptation and Resilience
1.7.4 Aspects of Sustainability and Profitability
1.8 Challenges and Constraints in Adoption of Intercropping 4.0
1.8.1 Knowledge, Financial, and Technical Barriers
1.8.2 Smallholder Farmer Constraints of Adopting
1.9 Conclusion
Acknowledgments
References
2. The Evolution of Agriculture 4.0: Intercropping Systems
for Sustainable Agriculture to Enhance Productivity and Resource Use Efficiency
Anita Rani Mehta, Pardeep Kumar, Sanjay Tyagi, Guru Prem and Amrutanshu Tyagi
2.1 Introduction
2.1.1 Overview and Types of Intercropping
2.1.2 Major Intercropping Types
2.2 Crop Geometry, Spatial Arrangement, and Plant Density in Intercropping Systems
2.3 Performance Assessment of Intercropping through Competitive Indices
2.4 Enhancing Resource Use Efficiency through Intercropping
2.4.1 Suppression of Insect Pests, Diseases, and Weeds
2.4.2 Soil Nutrient Dynamics in Legume Intercropping Systems
2.4.3 Conservation of Natural Resources
2.5 Analyzing Intercropping System’s Performance Using Competitive Indices
2.6 Challenges
2.7 Conclusions and Future Scope
Acknowledgments
References
3. Traditional Knowledge and Smart Farming in Transition:
An Interdisciplinary Approach to Sustainable Agriculture
Naresh Prajapati, Pradeep Kulshreshtha, Deepak Kumar Chauhan, Versha Vahini and Kokila Konasinghe
3.1 Introduction
3.2 Traditional Knowledge Systems in Indian Agriculture
3.3 Smart Farming and Digital Agriculture Technologies
3.4 The Sustainability Paradox: Technology vs. Ecology
3.5 Climate Resilience through Indigenous Practices
3.6 Synergistic Integration Models
3.7 Case Studies and Pilot Interventions
3.7.1 Case 1: Digital Green – Local Video for Local Knowledge (India)
3.7.2 Case 2: Potato Park – Blending Ancestral Biodiversity
with Modern Tools (Peru)
3.7.3 Additional Reflections and Comparative Cases
3.7.4 Key Insights
3.8 Legal, Ethical, and Policy Dimensions
3.8.1 Protecting Traditional Knowledge in Indian Law
3.8.2 Global Protocols: Equity and Contestations
3.8.3 Uncharted Territory: Smart Farming and Legal Ambiguity
3.8.4 The Role of Law Schools and Universities
3.9 Toward Agricultural Justice
3.10 Rethinking Innovation: From Top-Down Transfer to Participatory Co-Creation
3.11 Multi-Stakeholder Platforms for Co-Creation
3.12 Farmer–Scientist Collaborations: Reviving Trust
3.13 Equity as the Foundation: Gender and Caste Dynamics
3.14 Commons-Based Tools and Community Innovation
3.15 Toward a Participatory Policy Vision
3.16 Conclusion
Acknowledgments
References
4. Technologies Powering Smart Intercropping
Sandeep Kaur, Satveer Kour, Manjit Sandhu, Toufik Mzili and Amna Ikram
4.1 Introduction
4.1.1 Importance of Smart Technologies in Modern Agriculture
4.1.2 Objectives and Scope
4.2 Fundamentals of Intercropping Systems
4.2.1 Types of Intercropping
4.2.2 Agronomic Benefits and Limitations
4.2.2.1 Benefits of Intercropping
4.2.2.2 Limitations of Intercropping
4.2.3 Role of Biodiversity and Soil Health
4.3 Integration of Smart Technologies in Agriculture
4.3.1 Role of Artificial Intelligence (AI) and Machine Learning (ML)
4.3.2 Deep Learning (DL) for Crop Prediction and Health Analysis
4.3.3 Internet of Things (IoT) in Smart Farming
4.3.4 Remote Sensing and Precision Agriculture Tools
4.4 Smart Intercropping Framework
4.5 Applications of IoT and AI in Intercropping
4.5.1 Crop Pairing and Spatial Optimization
4.5.1.1 Role of IoT
4.5.1.2 Role of AI
4.5.2 Soil and Nutrient Management
4.5.2.1 Role of IoT
4.5.2.2 Role of AI
4.5.3 Water Conservation and Smart Irrigation
4.5.3.1 Role of IoT
4.5.3.2 Role of AI
4.5.4 Pest and Disease Prediction and Control
4.5.4.1 Role of IoT
4.5.4.2 Role of AI
4.6 Emerging Technologies and Innovations
4.6.1 Blockchain for Supply Chain Transparency
4.6.2 Robotics and Automation in Intercropping
4.6.3 Cloud and Edge Computing for Agricultural Intelligence
4.6.4 Mobile-Based Decision Support Platforms
4.7 Challenges and Limitations
4.7.1 Technological and Infrastructure Barriers
4.7.2 Data Privacy and Security Concerns
4.7.3 Climate Variability and Environmental Factors
4.8 Future Research Directions
4.9 Conclusion
Acknowledgments
References
5. Integration and Implementation of IoT in Intercropping
Kuldeep Singh Kaswan, Jagjit Singh Dhatterwal, Kiran Malik, Anju Kapri, Ayman Nayef Ahmad Alhalaybeh and Mohammad Mazhar Nezami
5.1 Introduction
5.2 Fundamentals of Intercropping Systems
5.3 IoT Architecture for Intercropping Applications
5.4 Sensor Technologies in Intercropping
5.5 Data Fusion and Analytics in Multi-Crop Environments
5.6 Actuation and Control Mechanisms
5.7 Energy Management in IoT-Enabled Intercropping
5.8 Conclusion
Acknowledgments
References
6. Intercropping 4.0: Harnessing IoT, AI, and Big Data for Sustainable Farming Practices
C.V. Suresh Babu, Sangeethapriya M. and Malini Premakumari William
6.1 Introduction
6.1.1 Background: Global Food Security and Intercropping Potential
6.1.2 Traditional Intercropping Limitations and Current Barriers
6.1.3 Emergence of Intercropping 4.0: Technological Integration
6.1.4 Research Objectives and Study Scope
6.2 Literature Review and Digital Agriculture Landscape
6.2.1 Intercropping Fundamentals and Agroecological Benefits
6.2.2 Digital Agriculture Adoption and IoT–AI Integration
6.2.3 Comparative Performance: Traditional vs. Technology-Enabled Intercropping
6.3 IoT Framework for Smart Intercropping
6.3.1 Conceptual Framework Architecture
6.3.2 IoT Implementation and Real-Time Monitoring
6.3.3 AI Layer: Predictive Analytics and Decision Support
6.3.4 Big Data Integration and Cross-Scale Intelligence
6.4 Methodology and Evaluation Framework
6.4.1 Research Design
6.4.2 Data Collection and Selection Process
6.4.3 Case Study Framework
6.4.4 Analytical Procedures
6.4.5 Evaluation Metrics and Indicators
6.4.6 Data Validation and Reliability
6.4.7 Ethical Considerations and Data Governance
6.4.8 Methodological Rigor and Limitations
6.5 Challenges and Implementation Roadmap
6.5.1 Technical and Economic Barriers
6.5.2 Data Governance and Scalability Requirements
6.5.3 Implementation Roadmap and Institutional Requirements
6.5.4 Future Viability and Research Priorities
6.6 Conclusions
6.6.1 Key Findings and Implications
6.6.2 Sustainability Implications and Policy Considerations
Acknowledgments
References
7. AIoT-Driven Environmental Intelligence for Sustainable Tea Destinations: Integrating Smart Climate, Soil, and Biodiversity Monitoring
Shuvasree Banerjee, Mohammad Badruddoza Talukder, Tathagata Banerjee and Mehmet Emin Baynazoglu
7.1 Introduction
7.2 Historical Trajectory and Rationale
7.3 An Overview of AIoT in Environmental Monitoring
7.4 Environmental Intelligence Powered by AIoT
7.4.1 What AIoT Is?
7.4.2 Applications in Agriculture and Tourism
7.4.2.1 Agriculture Applications
7.4.2.2 Tourism Applications
7.5 Key Advantages Over Traditional Monitoring
7.6 Technological Components
7.7 AIoT in Environmental Monitoring: A Conceptual Framework
7.7.1 IoT Sensor Networks for Data Collection
7.7.2 AI Processing and Predictive Analytics
7.7.3 Real-Time Dashboards
7.7.4 Decision and Actuation Layer
7.8 Climate Monitoring in Tea Destinations
7.9 Soil Health and Nutrient Management
7.10 Biodiversity Monitoring in Tea Destinations
7.11 Case Studies: AIoT in Sustainable Tea Destinations
7.11.1 Darjeeling
7.11.2 Boseong (South Korea)
7.11.3 Wuyi Mountain (China)
7.12 Challenges and Barriers
7.12.1 Technical
7.12.2 Economic
7.12.3 Social
7.12.4 Ethics and Governance
7.13 Policy and Management Recommendations
7.14 Implementation Roadmap
7.15 Evaluation Metrics and Evidence
7.16 Ethics, Equity, and Data Rights
7.17 Limitations and Research Agenda
7.18 Conclusion
Acknowledgments
References
8. The Need for IoT-Enabled Decision Making in Intercropping
Gayathri Ramesh, Raja Somasundaram, Rithik Prajwal, Madhu Shree Bheeman, Noor Hafizah Hassan and Saurav Mallik
8.1 Introduction
8.2 Limitations of Traditional Decision-Making Approaches
8.2.1 Cognitive Constraints in Complex Systems
8.2.2 Spatial and Temporal Information Deficits
8.2.3 Inadequacy of Conventional Advisory Systems
8.3 Role of IoT in Intercropping
8.3.1 Distributed Sensor Networks
8.3.2 Network Connectivity
8.3.3 Computing System
8.3.4 Decision Support Systems
8.4 Artificial Intelligence and Decision-Making Frameworks
8.4.1 Interaction Prediction in Crop with Machine Learning
8.4.2 Dynamic Resource Allocation by Reinforcement Learning
8.4.3 Edge AI Decision Engines to On-Farm Actions
8.4.4 Data Fusion: Soil, Microclimate, and Plant Phenology
8.4.5 Explainable Artificial Intelligence to Support Farmer-Centric Decisions
8.5 Application Areas and Case Studies
8.5.1 Intercropping between Cereal and Legume
8.5.2 Systems of Agroforestry
8.5.3 Vegetable Intercropping
8.6 Future Perspectives
References
9. Decision-Making for Sustainable Intercropping
Kiran Malik, Jagjit Singh Dhatterwal, Kuldeep Singh Kaswan,
Varun Malik, Saurav Mallik and Maram Bani Younes
9.1 Introduction
9.1.1 Rationale for Intercropping in Sustainable Agroecosystems
9.1.2 Evolution of Decision-Making Approaches in Cropping System Design
9.1.3 Key Challenges in Sustainable Intercropping Decisions
9.2 Conceptual Foundations of Decision-Making in Sustainable Intercropping
9.2.1 Systems Thinking in Intercropping Design
9.2.2 Multi-Dimensional Nature of Sustainability: Ecological, Economic, and Social
9.2.3 Stakeholder Engagement and Participatory Frameworks
9.3 Agronomic and Ecological Considerations
9.3.1 Species Selection: Complementarity, Competition, and Facilitation
9.3.2 Spatiotemporal Arrangement and Resource Partitioning
9.3.3 Soil Health, Nutrient Cycling, and Microbial Synergy in Intercrops
9.3.4 Pest, Disease, and Weed Dynamics in Mixed Cropping Systems
9.4 Decision-Support Frameworks for Intercropping
9.4.1 Multi-Criteria Decision Analysis (MCDA) in Cropping Choices
9.4.2 Analytic Hierarchy Process (AHP) and Pairwise Comparison Methods
9.4.3 Fuzzy Logic and Uncertainty Handling in Intercropping Planning
9.4.4 Optimization Models: Linear, Non-Linear, and Multi-Objective Approaches
9.5 Quantitative Models and Simulation Tools
9.5.1 Process-Based Models for Predicting Intercropping Performance
9.5.2 Agent-Based and System Dynamics Models in Decision-Making
9.5.3 Geospatial and Remote Sensing Inputs for Intercropping Planning
9.5.4 Integrating AI and Machine Learning for Adaptive Decision Systems
9.6 Socioeconomic and Policy Dimensions
9.6.1 Economic Viability Assessment: Profitability and Risk Trade-Offs
9.6.2 Farmer Perceptions and Behavioral Decision Drivers
9.6.3 Incentive Structures and Policy Interventions for Sustainable Intercropping
9.6.4 Market Linkages and Value-Chain Integration
9.7 Metrics and Indicators for Sustainability Evaluation
9.7.1 Environmental Indicators: Biodiversity, Soil Carbon, and Water Footprint
9.7.2 Economic Indicators: Gross Margins, Input Efficiency, and Cost-Benefit Ratios
9.7.3 Social Indicators: Labor Dynamics, Gender, and Community Well-Being
9.7.4 Composite Sustainability Indexes for Intercropping Systems
9.8 Case Studies and Practical Applications
9.8.1 Successful Intercropping Decision-Making Frameworks in Smallholder Systems
9.8.2 Decision Support in Mechanized and Large-Scale Intercropping
9.8.3 Lessons from Indigenous Knowledge and Traditional Multi-Crop Systems
9.9 Challenges, Trade-Offs, and Future Directions
9.9.1 Reconciling Conflicting Objectives in Intercropping Decisions
9.9.2 Data Gaps, Model Limitations, and Scaling Challenges
9.9.3 Integrating Climate Adaptation and Resilience in Decision Frameworks
9.10 Conclusion
Acknowledgments
References
10. Balancing Productivity and Sustainability: Challenges in Modern Agriculture
Yazhini S.P., Akila M. and Neha Gautham
10.1 Introduction
10.1.1 Irony of Higher Productivity vs. Nature Degradation
10.1.2 Sustainability of Agricultural Systems
10.1.3 Digital Technologies in Agricultural Makeovers
10.2 Idea of Sustainable Agriculture
10.2.1 Essentials of Sustainable Agriculture
10.2.1.1 Preservation and Improvement of Natural Resources
10.2.2 The Three Bottom Lines: People, Planet, and Profit
10.2.3 Agroecological Principles of Sustainability
10.2.4 SDGs and Sustainable Agriculture
10.2.5 Intercropping as a Practice in Sustainable Agriculture
10.3 Challenges in Modern Agriculture
10.3.1 The Overreliance on Chemical Inputs and Its Outcomes
10.3.2 Soil Erosion and Lack of Nutrient Balances
10.3.3 Water Shortage and Ineffective Irrigation Activities
10.3.4 Climate Change and Its Effects on Agriculture
10.3.5 Depletion of Biodiversity and Ecosystem Services
10.4 Intercropping as a Sustainable Agriculture Tool
10.5 The Part of Emerging Technologies in Sustainable Agriculture
10.6 Internet of Things (IoT) in Agriculture
10.7 Sustainable Agriculture Artificial Intelligence (AI)
10.8 IoT and AI Smart Intercropping Systems Integration
10.9 Challenges and Limitations
10.10 Prospects and Future Research
10.11 Conclusion
Acknowledgments
References
11. Psychological Resilience and Behavioral Adaptation in AI-Enabled Smart Agriculture
Durgeshwary Kolhe, Arshad Bhat, Firdous Ahmad Malik and Raja Nazakat Ali
11.1 Introduction
11.2 Psychological Resilience in Agricultural Contexts
11.3 AI and IoT in Smart Agriculture
11.4 Behavioral Adaptation to Smart Technologies
11.5 Linking Resilience with Technology Adoption
11.6 Case Studies and Empirical Insights
11.7 Designing Human-Centered Smart Agricultural Systems
11.8 Policy and Extension Interventions
11.9 Conclusion and Future Directions
Acknowledgments
References
12. Integrating Robotics and Automation in Modern Intercropping Systems
Arsh Beri, Anushka Bhatnagar, Gaganpreet Kaur, Mohd Asif Shah and Barga Mohammed Mujahid
12.1 Introduction
12.2 Applications of Robotics in Intercropping Systems
12.2.1 Agricultural Innovation
12.2.2 Accurate Planting and Sowing
12.2.3 Convergence of Livestock and Post-Harvesting
12.2.4 Agriculture and Watering Together
12.3 Benefits and Opportunities of Robotics in Agriculture
12.3.1 Improved Accuracy and Efficiency
12.3.2 Utilizing Resources in a Sustainable Way
12.3.3 Effectiveness of Work and Improvement of Skills
12.3.4 Data-Informed Choices
12.4 Challenges and Limitations of Robotics in Smart Agriculture
12.5 Case Study
12.6 Conclusion
Acknowledgments
References
13. Climate-Smart Intercropping: Adapting to Weather Changes
Rashid Abbas Khan, Zeeshan Ahmad, Rida Batool, Muhammad Iftikhar and Tauqeer Ahmed Qadri
13.1 Introduction
13.1.1 Climate Change and Weather Variability: Implications for Cropping Systems
13.1.1.1 Key Climate Stressors Affecting Cropping Systems
13.1.1.2 Impacts on Yield Stability, Pests, and Soil Health
13.1.2 Principles of Intercropping That Confer Climate Resilience
13.1.2.1 Resource Complementarity and Niche Differentiation
13.1.2.2 Risk Spreading and Buffering through Diversity
13.1.2.3 Biophysical Mechanisms of Climate Resilience
13.1.3 Climate-Smart Intercropping Strategies
13.1.3.1 Design, Pairing, and Temporal Strategies
13.1.3.2 Temporal Strategies: Relay and Simultaneous Intercropping
13.1.3.3 Soil and Water Management Complements
13.1.3.4 Adaptation Objectives of Climate-Smart Intercropping
13.1.3.5 Practical Design Guidelines for Smallholders and Commercial Systems
13.1.4 Role of IoT in Enabling Climate-Smart Intercropping
13.1.4.1 Sensors and Measurements for Climate-Smart
Decisions
13.1.4.2 Networks and Communication Infrastructure
13.1.4.3 Actuation and Precision Interventions
13.1.4.4 Data Flows and Real-Time Adaptive Management
13.1.5 Role of Artificial Intelligence and Decision Support
13.1.5.1 Predictive Models for Anticipating Climate and Crop Responses
13.1.5.2 Prescriptive Analytics and Optimization for Intercropping Systems
13.1.5.3 Image Analysis and Phenotyping for Crop Health and Stress Monitoring
13.1.5.4 Explainability and Farmer-Friendly Decision Support
13.1.6 Implementation Framework and Technology Adoption Pathway
13.1.6.1 Stepwise Pathway for Climate-Smart Intercropping
13.1.6.2 Economic Feasibility and Capacity Building
13.1.6.3 Farmer Training/Capacity Building
13.1.7 Socio-Economic, Institutional, and Policy Considerations
13.1.8 Conclusion
Acknowledgments
References
14. Economic and Environmental Benefits of IoT-Enabled Farming
Sithandekile Sibanda
14.1 Introduction
14.1.1 Global Context and Urgency
14.1.2 Technological Innovation and Quantifiable Sustainability Results
14.2 IoT-Based Agriculture Economic Values
14.2.1 Workforce Cost Saving and Efficiency
14.2.2 Comparative Analysis: Traditional and IoT-Based Operations
14.2.3 Trends in Market Growth and Investment
14.2.4 Productivity Improvement and Quality
14.3 Farming IoT-Enabled: Environmental Benefits
14.3.1 Resource Conversation and Optimization of Inputs
14.3.2 Greenhouse Gas Emission Reduction
14.3.3 Conserving Biodiversity and Ecosystem Health
14.3.4 Environmental Indicator Traditional Farming IoT-Enabled
Farming Improvement Source
14.3.5 Climate Resilience and Adaptation
14.3.6 Sensing Technologies and System Architecture – IoT Core
14.4 Data Acquisition and Sensor Technologies
14.4.1 Network Architecture and Communication Technologies
14.4.2 Commercial Internet of Things Agricultural Applications
14.5 Case Studies: IoT-Enhanced Agriculture Implementation on a Global Scale
14.5.1 Case Study Analysis Regionally
14.5.2 Case Study Insights and Success Factors
14.6 Future Projections: AIoT Integration and Future Innovations
14.6.1 Agriculture-Based Artificial Intelligence of Things
(AIoT)
14.6.2 Edge Computing and Real-Time Analytics
14.6.3 Integrating Blockchain to Provide Transparency and Traceability
14.6.4 Autonomous Farming Systems and Robotics
14.6.5 Advanced Data Sensing and Multi-Mode Data Integration
14.6.6 Improved Connectivity: 5G and Future Networks
14.6.7 Circular Economy and Sustainability Integration
14.7 Difficulties and Obstacles to IoT Implementation
14.7.1 Economic and Financial Obstacles
14.7.2 Social and Human Factors
14.7.3 Data Privacy and Security Concerns
14.7.4 Policy and Regulatory Gaps
14.8 Policy Recommendations and Conclusions
14.8.1 Summary of Key Findings
14.8.2 Policy Recommendations
14.9 Conclusion
Acknowledgments
References
15. Adoption and Impact of AI, 5G, and Autonomous Systems on Intercropping Efficiency: A Cross-Country Analysis of the USA, Japan, China, and India
Saurabh Mishra, Pardeep Bawa Sharma, Vikas Tyagi, András Szeberényi and Nadeesha Muthuthantrige
15.1 Introduction
15.2 Need for the Study
15.3 Literature Review
15.3.1 Artificial Intelligence in Intercropping Systems
15.3.2 5G and Real-Time Communication in Agriculture
15.3.3 Autonomous Farming Technologies and Labor Substitution
15.3.4 Policy, Infrastructure, and Adoption Readiness
15.3.5 Intercropping and Sustainable Intensification
15.3.6 Study Objectives
15.4 Research Methodology
15.4.1 Quantitative Component
15.4.2 Qualitative Component
15.4.3 Data Analysis and Presentation
15.4.4 Ethical Considerations
15.5 Findings
15.6 Recommendations for India
15.7 Limitations and Scope for Future Research
15.8 Conclusion
Acknowledgments
References
16. The Green Paradox in Agriculture 4.0: Sustainability Challenges in Agricultural Practices
Jay Prakash Maurya, Ajay Sharma, Chour Singh Rajpoot, Ghanshyam G. Tejani and Zainab Aizaz
16.1 Introduction
16.1.1 Historical Background Origins of the Green Paradox and Evolution of Agriculture 4.0
16.1.1.1 Need and Importance
16.1.1.2 Applications of Agriculture 4.0 and Potential Paradox Pathways
16.1.1.3 Past Research: Theoretical and Empirical Findings
16.1.1.4 Research Gaps
16.2 Literature Review
16.3 Implementation Method
16.3.1 Policy Announcement and Timing Effects
16.3.2 Technology-Driven Rebound Effect
16.3.3 Capital Lock-In Under Digital Incentives
16.4 Results
16.5 Conclusion
Acknowledgments
References
17. Economic Growth and Environmental Sustainability through IoT-Powered Farming
Amanpreet Singh, Rupinder Singh, Jaswinder Singh, Satveer Kour and R. John Martin
17.1 Introduction
17.2 Motivation and Contribution
17.3 Human Factors and Skill Gaps
17.4 Conclusion and Future Work
Acknowledgments
References
18. Mathematical Modeling and Deep Learning Fusion for Soil
Moisture Forecasting in Precision Agriculture
Randhir Singh Baghel, Govind Shay Sharma, Harkaran Singh and Mudassir Khan
18.1 Introduction
18.2 Literature Review
18.2.1 Soil Moisture Models
18.2.2 Machine Learning and Deep Learning in Agriculture
18.2.3 Previous Hybrid Modeling Attempts
18.2.4 Gaps Identified in Current Research
18.3 Methodology
18.3.1 Mathematical Modeling
18.3.1.1 Governing Equations
18.3.1.2 Boundary and Initial Conditions
18.3.1.3 Numerical Solution Method
18.3.2 Deep Learning Model
18.3.2.1 Model Architecture
18.3.2.2 Input Features
18.3.2.3 Data Pre-Processing
18.3.2.4 Model Training Details
18.3.3 Hybrid Fusion Approach
18.3.3.1 Method of Fusion
18.3.3.2 Mathematical Formulation
18.3.3.3 Training Strategy
18.4 Data and Experimental Setup
18.4.1 Study Site and Simulation Environment
18.4.2 Datasets Used
18.4.2.1 In Situ Soil Moisture Sensors
18.4.2.2 Satellite and Remote Sensing Data
18.4.2.3 Weather Data
18.4.3 Model Training and Testing Procedures
18.5 Results and Discussion
18.5.1 Comparison of Modeling Approaches
18.5.2 Quantitative Evaluation
18.5.3 Visualizations
18.5.4 Impact of Environmental Variables
18.5.5 Generalizability and Robustness
18.6 Conclusion
Acknowledgments
References
19. Supply Chain Optimization for Fresh Agricultural Products: Role of Distribution Centers in Agricultural Superdocking
Anshraj, Rohit Raj, Monika, Govind Shay Sharma and Chatchawan Panraksa
19.1 Introduction
19.2 Literature Review
19.3 Result and Discussion
19.4 Conclusion
19.5 Future Research and Practical Implications
Acknowledgments
References
20. Innovative Robotics and Automation Approaches in Intercropping
Abiban Kumari, Jaswinder Singh, Alok K. Kushwaha and Vadthya Lokya
20.1 Introduction
20.2 Intercropping System
20.3 Advantages of Intercropping
20.4 Automation in Intercropping System
20.4.1 Automated Process in the Intercropping System
20.5 Role of Robotics in the Intercropping System
20.6 Integration of Data-Driven Technologies (AI, Internet of Things, Robotics, and Machine Vision)
20.7 Conclusion
Acknowledgements
References
21. Modernizing Intercropping with IoT, AI, and Big Data
Technologies
Garima, Arti, Somesh and Mudassir Khan
21.1 Introduction
21.2 Literature Review
21.3 Methodology
21.3.1 Research Design
21.3.2 Data Sources
21.3.3 Analytical Framework
21.3.4 Limitations
21.4 Intercropping 4.0 Framework
21.4.1 IoT in Intercropping
21.4.2 AI in Intercropping
21.5 Case Studies
21.6 Challenges and Limitations
21.7 Future Directions
21.8 Conclusion
Acknowledgments
References
22. Rethinking Intercropping with AI, 5G, and Automation:
Towards Data-Driven Sustainable Agriculture
Raj Bhattacharjee, Kanimozhi S., Nayef Ahmad Alhalaybeh and Mohammad Mazhar Nezami
22.1 Introduction
22.2 Related Works
22.3 Intercropping: Principles and Advantages
22.4 Digital Agriculture Foundations
22.5 Case Study Context and Objectives
22.6 Integration of AI and Data-Driven Tools in Intercropping
22.6.1 Crop Monitoring and Classification
22.6.2 IoT and Edge Devices for Real-Time Monitoring
22.6.3 Predictive Analytics and Decision Support
22.6.4 Integration with Digital Plant Pathology
22.6.5 Synergies of AI, 5G, and Automation
22.7 5G, Robotics, and Automation Applications
22.8 Practical Outcomes
22.9 Research Gaps and Challenges
22.10 Future Directions
22.11 Conclusion
Acknowledgements
References
23. Strengthening Intercropping through IoT-Based Decision
Support
Tarun Kumar, Sonia Suneja, Jyoti Saini, Kanwal Garg, Dolly Rani, Renu Popli and Kamini
23.1 Background and Motivation
23.2 Why Intercropping, and Why Now?
23.3 Limitations of Conventional Decision-Making
23.4 IoT as an Enabler for Data-Driven Intercropping
23.5 From Monitoring to Decisions: What Changes in Intercropping
23.6 Real-World IoT Deployments in Intercropping
23.7 Practical Implementation of IoT in Intercropping
23.8 Benefits and Challenges
23.9 Sustainability and Equity Lens
23.10 Scope and Contributions
23.11 Visionary Outlook
Acknowledgments
References
24. Smart Intercropping Systems: Leveraging IoT, Big Data,
and AI-Based Predictive Models
Sakshi Gupta, Manorama Patnaik, Mehak Singh, Umesh Gupta, Varun Malik and Mudassir Khan
24.1 Introduction
24.2 Literature Review
24.3 Concept of Intercropping 4.0
24.4 Role of IoT in Modern Intercropping
24.5 Role of Artificial Intelligence in Intercropping Management
24.6 Methodology
24.7 Role of Big Data and Analytics
24.8 Integration of IoT, AI, and Big Data
24.9 Case Studies
24.10 Benefits and Impacts
24.11 Challenges and Limitations
24.12 Future Prospects and Innovations
24.13 Conclusion
Acknowledgments
References
25. Precision-Driven Multi-Cropping Revolution: Integrating IoT, Artificial Intelligence, and Big Data Analytics for Sustainable Intercropping 4.0 Systems
Kalpavi C. Y., Pushpa Mala S., Dilip R., Sujatha B. M., Manjunatha R. C. and Ajit Danti
25.1 Introduction
25.1.1 Challenges and Technological Opportunities in Agricultural Systems
25.1.2 Digital Agriculture Revolution and Technological Convergence
25.1.3 Intercropping 4.0: A New Paradigm for Sustainable Agriculture
25.1.4 Research Objectives and Contributions
25.2 Literature Review
25.2.1 Theoretical Foundations of Intercropping
25.2.2 Digital Technology Integration in Agriculture
25.2.2.1 Internet of Things (IoT) Applications in Agriculture
25.2.2.2 Decision-Making Artificial Intelligence Applications in Agricultural Systems
25.2.2.3 Big Data Analytics Platforms
25.3 Methodology
25.3.1 Literature Search Strategy and Database Selection
25.3.2 Data Extraction Protocol and Quality Assessment
25.4 Problem Statement
25.4.1 Management Complexity
25.4.2 Scale Limitations
25.4.3 Environmental Variability
25.5 Results and Discussion
25.5.1 Comprehensive Yield Performance Analysis
25.5.2 Multi-Dimensional Evaluation of Performance and Optimizing the System
25.5.3 Regional Adoption Patterns and Technology Diffusion
25.5.4 Economic Performance Trends and Financial Viability
25.5.5 Resource Efficiency Benefits and Environmental Benefits
25.5.6 Sustainability Impact Assessment and Environmental Outcomes
25.5.7 Technology Adoption Issues and Implementation Patterns
25.6 Analysis and Implications
25.6.1 Performance Drivers and Technological Mechanisms
25.6.1.1 Management of Resource
25.6.1.2 Data-Driven Decision Making
25.6.1.3 Automated Management Systems Integration
25.6.2 Economic Viability Checkup and Investment Analysis
25.6.2.1 Analysis by Farm Scale for Investment
25.6.2.2 Economic Improvement Moving Forces
25.6.3 Implementation and Factors of Scalability
25.6.3.1 Technical Scalability Considerations
25.6.3.2 Factors of Economic Scalability
25.6.3.3 Social Scalability Requirements
25.6.4 Barriers to Adoption and Mitigation Strategies
25.6.4.1 Technical Barriers and Solutions
25.6.4.2 Economic Barriers and Solutions
25.6.4.3 Knowledge Barriers and Solutions
25.7 Future Scope and Research Directions
25.7.1 Technological Advances and Innovation Opportunities
25.7.2 Research Priorities and Knowledge Gaps
25.7.3 Policy and Governance Considerations
25.8 Conclusion
25.8.1 Implications for Stakeholders
25.8.2 Future Outlook
25.8.3 Final Recommendations and Strategic Priorities
Acknowledgments
References
26. Next-Generation Intercropping: A Data-Driven Approach
Using IoT and AI Technologies
Sarbjeet Singh, Harjinder Kaur, Satveer Kour, Toufik Mzili and Mudassir Khan
26.1 Introduction
26.1.1 The Evolution of Intercropping
26.1.2 The Emergence of Agriculture 4.0
26.1.3 Bridging Tradition and Technology: Intercropping 4.0
26.2 The Internet of Things (IoT) in Intercropping 4.0
26.2.1 The IoT Ecosystem in the Field
26.2.2 In-Field Acoustic Sensing: Listening to the Farm
26.3 Data Fusion Algorithms: Building the Digital Twin
26.3.1 Real-Time Data Collection and Monitoring
26.3.2 Automated Resource Management
26.3.3 Drones and Robotics: The Eyes and Hands of the Farm
26.4 Big Data Infrastructure and Architectures for I4.0
26.4.1 Challenges of Data Volume, Velocity, and Variety in Polyculture Systems
26.4.2 Cloud–Edge–Device Collaborative Computing Paradigm
26.5 Cloud–Edge–Device Collaborative Computing Paradigm
26.5.1 The Device Layer (Sensors and Actuators)
26.5.2 The Edge Layer (Local Processing and Immediate Action)
26.5.3 The Cloud Layer (Global Optimization and Long-Term
Intelligence)
26.5.4 Challenges in Deployment
26.6 Data Interoperability, Standardization, and Hierarchical Data Fusion
26.7 Artificial Intelligence (AI) and Machine Learning (ML) for Intercrop Optimization
26.7.1 Predictive Analytics for Yield and Resource Needs
26.7.2 Computer Vision for Crop Health Monitoring
26.7.3 AI-Powered Decision Support Systems
26.7.4 Optimizing Plant and Row Configurations
26.8 Big Data Analytics: The Foundation for Precision Intercropping
26.8.1 The Intercropping Data Tsunami
26.8.2 Data Integration and Centralized Platforms
26.8.3 Insights from Historical and Real-Time Data
26.8.4 Challenges of Big Data in Intercropping
26.9 Case Studies and Applications
26.10 Conclusion: The Sustainable Future of Farming
Acknowledgements
References
Index
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