Smart Agritech Robotics, AI, and Internet of Things (IoT) in Agriculture
The main goal of Smart Agritech: Robotics, AI, and Internet of Things (IoT) in Agriculture is to explore how emerging technologies such as robotics, artificial intelligence (AI), and IoT can be leveraged to improve efficiency, sustainability, and productivity in agriculture. Agriculture has always b...
| Otros Autores: | |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Hoboken, NJ :
Scrivener Publishing LLC
[2024]
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| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009852333906719 |
Tabla de Contenidos:
- Cover
- Series Page
- Title Page
- Copyright Page
- Contents
- Preface
- Chapter 1 Introduction to Smart Farming: Definition, Importance and Trends
- 1.1 Introduction
- 1.2 Smart Farming
- 1.3 Internet of Things
- 1.3.1 Fundamentals of IoT Applications in Smart Farming
- 1.4 Technologies Used in Smart Farming
- 1.4.1 Global Positioning System (GPS)
- 1.4.2 Sensor Technologies
- 1.4.3 Variable-Rate of Technology (VRT) and Grid Soil Sampling
- 1.4.4 Geographic Information System (GIS)
- 1.4.5 Crop Management
- 1.4.6 Soil and Plant Sensors
- 1.4.7 Rate Controllers
- 1.4.8 Precision Irrigation in Pressurized Systems
- 1.4.9 Yield Monitor
- 1.4.10 Software
- 1.5 Importance of Smart Farming
- 1.5.1 Soil Mapping and Plant Monitoring
- 1.5.2 Irrigation
- 1.5.3 Site-Specific Nutrient Management
- 1.5.4 Crop Pest and Disease Management
- 1.5.5 Yield Monitoring and Forecasting
- 1.5.6 Enhanced Production Rates
- 1.5.7 Water Conservation
- 1.5.8 Real-Time Data and Insights
- 1.5.9 Reduction in Operation Costs
- 1.5.10 High-Quality Production
- 1.5.11 Accurate Farm and Yield Evaluation
- 1.5.12 Improved Livestock Production
- 1.5.13 Reduced Environmental Footprint Impact
- 1.5.14 Remote Monitoring for Easy Management
- 1.5.15 Expensive Asset Monitoring
- 1.6 Role of IoT in Advanced Farming Practices
- 1.6.1 Greenhouse Farming and Protected Cultivation
- 1.6.2 Hydroponics
- 1.6.3 Vertical Farming
- 1.6.4 Phenotyping
- 1.7 Trends of Smart Farming
- 1.7.1 Precision Agriculture
- 1.7.2 Automation
- 1.7.3 Internet of Things (IoT)
- 1.7.4 Big Data Analytics
- 1.7.5 Vertical Farming
- 1.7.6 Aquaponics
- References
- Chapter 2 Overview of Robotics in Agriculture: Types and Applications
- 2.1 Introduction
- 2.2 Background
- 2.2.1 Historical Perspective of Agriculture and Technology.
- 2.2.2 Overview of the Current State of Agriculture and Technology
- 2.2.3 Benefits and Challenges of Robotics in Agriculture
- 2.3 Types of Robotics in Agriculture
- 2.4 Applications of Robotics in Agriculture
- 2.5 Advantages of Robotics in Agriculture
- 2.6 Limitations of Robotics in Agriculture
- 2.7 Future of Robotics in Agriculture
- 2.8 Case Studies and Examples
- 2.9 Implications and Recommendations
- 2.10 Conclusion
- References
- Chapter 3 Digital Farming: The New Era of Agriculture-Opportunities and Challenges
- 3.1 Introduction
- 3.1.1 Digital Farming
- 3.1.2 Automated Farming
- 3.2 Tools and Methods Used in Digital Farming
- 3.2.1 GPS
- 3.2.2 GIS
- 3.2.3 Grid Sampling
- 3.2.4 Variable Rate Technology
- 3.2.5 Yield Monitors
- 3.2.6 Remote Sensors
- 3.2.7 Auto-Guidance System
- 3.3 Pandemic Effects on Traditional Farming
- 3.4 New Scope at Digital Farming
- 3.5 Challenges or Difficulties for Digital Farming
- 3.6 Future Scope and Benefits
- References
- Chapter 4 Challenges and Barriers to Smart Farming Adaptation: A Technical, Economic, and Social Perspective
- 4.1 Introduction
- 4.1.1 Definition and Importance of Smart Farming
- 4.1.2 Objectives and Scope of the Chapter
- 4.2 Technical Challenges in Smart Farming Adaptation
- 4.2.1 Overview of Smart Farming Technologies
- 4.2.1.1 Precision Agriculture
- 4.2.1.2 Drones
- 4.2.1.3 Robotics
- 4.2.1.4 Livestock Management
- 4.2.1.5 Aquaculture
- 4.2.1.6 Data Analytics
- 4.2.2 Technical Challenges in Data Management and Connectivity
- 4.2.2.1 Data Management
- 4.2.2.2 Connectivity
- 4.2.2.3 Integration
- 4.2.2.4 Data Standards
- 4.2.3 Role of Sensors, Internet of Things, and AI in Smart Farming
- 4.2.3.1 Sensors
- 4.2.3.2 IoT
- 4.2.3.3 AI
- 4.2.3.4 Integration
- 4.3 Economic Barriers to Smart Farming Implementation.
- 4.3.1 Cost of Implementing Smart Farming Technologies
- 4.3.1.1 Hardware Costs
- 4.3.1.2 Data Management Costs
- 4.3.1.3 Training and Support Costs
- 4.3.1.4 Infrastructure Costs
- 4.3.1.5 Financing Costs
- 4.3.2 Financing and Investment Challenges
- 4.3.3 Economic Benefits and Returns on Investment
- 4.4 Social Obstacles to Smart Farming Adoption
- 4.4.1 Lack of Knowledge and Understanding Among Farmers
- 4.4.2 Behavioral and Cultural Barriers to Change
- 4.4.3 Role of Education and Awareness Raising
- 4.5 Environmental Considerations in Smart Farming
- 4.5.1 Sustainable Agricultural Practices and Smart Farming
- 4.5.2 Environmental Benefits and Concerns
- 4.5.3 Climate Change Mitigation and Adaptation
- 4.5.3.1 Mitigating Climate Change
- 4.5.3.2 Adapting to Climate Change
- 4.6 Future Prospects for Smart Farming
- 4.6.1 Emerging Trends and Innovations in Smart Farming
- 4.6.2 Prospects for Overcoming Challenges and Barriers
- 4.6.3 Future Outlook for Sustainable Agriculture
- 4.7 Conclusion and Recommendations
- 4.7.1 Summary of Key Points
- 4.7.2 Policy Recommendations for Promoting Smart Farming
- 4.7.3 Conclusion and Future Directions for Research
- References
- Chapter 5 Sustainable Development in Agriculture: Soil Management
- 5.1 Introduction
- 5.2 Reviewing the Need for Global Food Production to be Upgraded
- 5.2.1 Growing Population and Changing Dietary Preferences
- 5.2.2 Climate Change and Resource Limitations
- 5.2.3 Environmental Sustainability and Biodiversity Conservation
- 5.2.4 Food Loss and Waste
- 5.2.5 Technological Advancements and Digitalization
- 5.2.6 Socio-Economic Equity and Rural Development
- 5.2.7 Policy Interventions and Governance
- 5.3 Soil Quality and Its Impact
- 5.4 Emerging Technologies
- 5.4.1 AI Revolution
- 5.4.1.1 Precision Farming and Decision Support Systems.
- 5.4.1.2 Crop Monitoring and Disease Detection
- 5.4.1.3 Autonomous Farming and Robotics
- 5.4.1.4 Supply Chain Optimization
- 5.4.1.5 Data-Driven Farm Management
- 5.4.2 Computer Vision
- 5.4.2.1 Crop Monitoring and Disease Detection
- 5.4.2.2 Weed Identification and Management
- 5.4.2.3 Yield Estimation and Quality Assessment
- 5.4.2.4 Smart Irrigation and Resource Management
- 5.4.2.5 Plant Breeding and Genetic Improvement
- 5.4.2.6 Farm Automation and Robotics
- 5.4.2.7 Decision Support Systems
- 5.5 Introduction and Theory of IoT
- 5.5.1 Perception Layer
- 5.5.2 Network Layer
- 5.5.3 Middleware Layer
- 5.5.4 Service Layer
- 5.5.5 Analytics Layer
- 5.5.6 End-User Layer
- 5.6 Several Sensors and How They are Used in Agriculture
- 5.6.1 Level Sensors
- 5.6.1.1 Continuous Measurements
- 5.6.1.2 Point-Level Measurements
- 5.6.2 Temperature Sensors
- 5.6.3 Proximity Sensors
- 5.6.4 Infrared Sensors
- 5.6.5 Touch Sensors
- 5.7 Centralized Agriculture System
- 5.8 Conclusion
- References
- Chapter 6 Concepts of Robotics, AI, and Internet of Things (IoT) in Agriculture
- 6.1 Introduction
- 6.1.1 The Implantation of Information and Commutation Technology in Agriculture Sector
- 6.2 General Challenges Faced in the Agriculture Industry
- 6.2.1 Growing Demand for Food
- 6.2.2 Limited Resources
- 6.2.3 Climate Change
- 6.2.4 Labor Shortages
- 6.2.5 Food Demand
- 6.2.6 Specific Challenges in the Deployment of Technology of Robotics, AI, and IoT in Agriculture
- 6.3 Role of Robotics, AI, and IoT in Agriculture
- 6.4 Benefits of Robotic and AI in Improving Agriculture
- 6.4.1 Increased Efficiency
- 6.4.2 Improved Precision
- 6.4.3 Reduced Labor Costs
- 6.4.4 Sustainability
- 6.5 Collecting Data and Performing Analyses With the Help of the Internet of Things.
- 6.6 Relationships of IoT and AI Strengthen the Agriculture Sector
- 6.7 Benefits of IoT in Agriculture
- 6.8 The Role of AI in Tomorrow's Farming
- 6.8.1 How Artificial Intelligence (AI) Can Help the Farming Industry
- 6.8.2 Selecting Seeds and Plants With the Help of AI
- 6.8.3 Use of Artificial Intelligence in Farming
- 6.8.4 A Predictive Model for Yield Using Artificial Intelligence
- 6.8.5 Implementing AI for Weed and Pest Management
- 6.8.6 AI-Powered Inventory Management and Sales Promotion
- 6.9 Robots in Agriculture-Perceptions and Pros, Cons
- 6.9.1 Farming Equipment With Automated Functions and the Positive Effects
- 6.10 Complications Associated With Robots Used in Agriculture
- 6.10.1 Difficulties and Limitations to be Conquered
- 6.10.2 Successful Applications and Other Case Studies
- 6.10.3 Societal and Ethical Implications
- 6.11 Conclusion
- References
- Chapter 7 Data Analytics in Agriculture: Predictive Models and Real-Time Decision-Making
- 7.1 Introduction
- 7.2 Data Collection and Management in Agriculture
- 7.2.1 Soil Sensors
- 7.2.2 Weather Stations
- 7.2.3 Drones
- 7.2.4 Satellite Imagery
- 7.2.5 Farm Machinery
- 7.2.6 Pest and Disease Monitoring Systems
- 7.2.7 Market Data
- 7.2.8 Crop Records
- 7.2.9 Livestock Records
- 7.3 Challenges in Collecting and Managing Agricultural Data
- 7.4 Strategies for Effective Data Collection and Management
- 7.5 Predictive Models in Agriculture
- 7.5.1 Types of Predictive Models
- 7.6 Applications of Predictive Models in Agriculture
- 7.7 Real-Time Decision-Making in Agriculture
- 7.7.1 Importance of Real-Time Decision-Making in Agriculture
- 7.7.2 Tools and Technologies for Real-Time Decision Making
- 7.8 Integration of Predictive Models and Real-Time Decision Making in Agriculture.
- 7.8.1 Benefits of Integrating Predictive Models and Real-Time Decision-Making.
