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 Architectural Patterns and Techniques for Developing IoT Solutions

You're reading from   Architectural Patterns and Techniques for Developing IoT Solutions Build IoT applications using digital twins, gateways, rule engines, AI/ML integration, and related patterns

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Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781803245492
Length 304 pages
Edition 1st Edition
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Author (1):
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Jasbir Singh Dhaliwal Jasbir Singh Dhaliwal
Author Profile Icon Jasbir Singh Dhaliwal
Jasbir Singh Dhaliwal
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Table of Contents (20) Chapters Close

Preface 1. Part 1: Understanding IoT Patterns
2. Chapter 1: Introduction to IoT Patterns FREE CHAPTER 3. Chapter 2: IoT Patterns for Field Devices 4. Chapter 3: IoT Patterns for the Central Server 5. Part 2: IoT Patterns in Action
6. Chapter 4: Pattern Implementation in the Consumer Domain 7. Chapter 5: Pattern Implementation in the Smart City Domain 8. Chapter 6: Pattern Implementation in the Retail Domain 9. Chapter 7: Pattern Implementation in the Manufacturing Domain 10. Chapter 8: Pattern Implementation in the Agriculture Domain 11. Part 3: Implementation Considerations
12. Chapter 9: Sensor and Actuator Selection Guidelines 13. Chapter 10: Analytics in the IoT Context 14. Chapter 11: Security in the IoT Context 15. Part 4: Extending IoT Solutions
16. Chapter 12: Exploring Synergies with Emerging Technologies 17. Chapter 13: Epilogue 18. Index 19. Other Books You May Enjoy

An overview of smart agriculture

This section covers the key terms/definitions that are used in the smart agriculture domain. Understanding these terms is crucial to design and develop smart agriculture-related solutions.

Key terms/definitions

In this section, we will discuss some key technologies associated with smart agriculture:

  • Artificial Intelligence (AI): Accurate decision-making is the cornerstone of successful farming, and AI effectively complements/supplements a farmer’s ability to make sound judgments. AI can be used in all stages of crop cultivation – for example, if a crop is infected with a disease, AI provides recommendations to reduce crop wastage. AI is also used to estimate optimal farm input, forecast demand, predict price/yield, identify crops that fit the soil/environmental conditions, and determine the right time to harvest.
  • Automation: Automation can be used to start/stop the watering of crops (often remotely), either automatically...
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