Smart Farming 4.0: AI and IoT in Agriculture
About the Book
Smart Farming: Technologies and Innovations for Sustainable Agriculture provides a comprehensive and practical guide to modern agricultural practices powered by digital technologies. The book explores how data-driven approaches, automation, and intelligent systems are transforming traditional farming into efficient, sustainable, and high-yield agricultural systems.
The book begins with the fundamentals of smart farming, introducing key concepts such as precision agriculture, Internet of Things (IoT), and sensor-based monitoring systems. It explains how real-time data collection and analysis enable farmers to make informed decisions regarding irrigation, fertilization, and crop management.
It further explores advanced technologies including drones, satellite imaging, GPS-based field mapping, and automated machinery. These tools help optimize resource utilization, reduce labor dependency, and improve crop productivity while minimizing environmental impact.
A key strength of this book lies in its focus on practical implementation challenges such as climate variability, soil health management, water conservation, and pest control. It discusses how smart farming solutions can address these issues through predictive analytics, AI-based decision systems, and integrated farm management platforms.
The book also highlights the role of data analytics, machine learning, and cloud computing in modern agriculture. It demonstrates how these technologies enable yield prediction, disease detection, and risk management, ultimately improving farm efficiency and profitability.
In addition, the book emphasizes sustainability and future trends in agriculture, including climate-smart farming, vertical farming, and smart irrigation systems. It equips readers with both theoretical knowledge and practical insights needed to adopt innovative farming practices in a rapidly evolving agricultural landscape.
Key Features
- Comprehensive coverage of smart farming concepts and technologies
- Integration of IoT, AI, and data analytics in agriculture
- Detailed insights into precision agriculture and automated farming systems
- Use of drones, sensors, and satellite technologies for farm monitoring
- Focus on sustainability, water management, and climate-smart practices
- Application of machine learning for yield prediction and disease detection
- Real-world case studies and practical implementation strategies
- Insights into future trends such as vertical farming and smart irrigation
Target Audience
This book is intended for students, researchers, agricultural professionals, farmers, agronomists, data analysts, and academicians. It is particularly useful for those interested in adopting modern technologies to improve agricultural productivity, sustainability, and decision-making.
Keywords
Keywords: Smart Farming, Precision Agriculture, IoT in Agriculture, Agricultural Technology, Drones in Farming, Machine Learning in Agriculture, Crop Monitoring, Smart Irrigation, Sustainable Agriculture, Soil Health, Climate-Smart Farming, Farm Automation, Digital Agriculture, Agri-Tech
Contents
| Chapter | Title | Page |
|---|---|---|
| CHAPTER 1 | FOUNDATIONAL QUANTITATIVE (TIME SERIES) FORECASTING METHODS | 11 |
| ABSTRACT | 12 | |
| 1.1 | SIMPLE MOVING AVERAGE | 12 |
| 1.2 | WEIGHTED MOVING AVERAGE | 14 |
| 1.3 | SINGLE EXPONENTIAL SMOOTHING | 15 |
| 1.4 | HOLT’S LINEAR TREND METHOD | 17 |
| 1.5 | HOLT-WINTERS METHOD | 18 |
| 1.6 | CLASSICAL TIME SERIES DECOMPOSITION | 20 |
| 1.7 | ARIMA MODELS | 21 |
| 1.8 | SARIMA MODELS | 23 |
| 1.9 | SARIMAX MODELS | 24 |
| CONCLUSION | 26 | |
| CHAPTER 2 | CAUSAL (EXPLANATORY) FORECASTING METHODS | 27 |
| ABSTRACT | 28 | |
| 2.1 | LINEAR REGRESSION | 28 |
| 2.2 | MULTIPLE REGRESSION | 30 |
| 2.3 | LOGISTIC REGRESSION | 33 |
| 2.4 | ECONOMETRIC MODELS | 36 |
| 2.5 | DYNAMIC REGRESSION MODELS | 38 |
| CONCLUSION | 41 | |
| CHAPTER 3 | ADVANCED STATISTICAL AND MACHINE LEARNING FORECASTING METHODS | 42 |
| ABSTRACT | 43 | |
| 3.1 | MACHINE LEARNING REGRESSION MODELS | 44 |
| 3.2 | ARTIFICIAL NEURAL NETWORKS | 46 |
| 3.3 | RECURRENT NEURAL NETWORKS | 49 |
| 3.4 | LONG SHORT-TERM MEMORY NETWORKS | 51 |
| 3.5 | SUPPORT VECTOR REGRESSION | 53 |
| 3.6 | PROPHET | 56 |
| 3.7 | ENSEMBLE FORECASTING METHODS | 59 |
| 3.8 | M5 COMPETITION-INSPIRED METHODOLOGIES | 61 |
| CONCLUSION | 65 | |
| CHAPTER 4 | INVENTORY AND SUPPLY CHAIN–SPECIFIC FORECASTING METHODS | 67 |
| ABSTRACT | 68 | |
| 4.1 | CROSTON’S METHOD | 68 |
| 4.2 | TSB METHOD | 71 |
| 4.3 | BOOTSTRAPPING TECHNIQUES | 73 |
| 4.4 | DISTRIBUTION REQUIREMENTS PLANNING | 76 |
| 4.5 | PIPELINE FORECASTING | 79 |
| CONCLUSION | 83 | |
| CHAPTER 5 | JUDGMENTAL AND QUALITATIVE FORECASTING METHODS | 85 |
| ABSTRACT | 85 | |
| 5.1 | SALES FORCE COMPOSITE | 85 |
| 5.2 | EXECUTIVE OPINION AND DELPHI METHOD | 90 |
| 5.3 | MARKET RESEARCH-BASED FORECASTING | 94 |
| 5.4 | HISTORICAL ANALOGY | 97 |
| 5.5 | SCENARIO PLANNING | 101 |
| CONCLUSION | 105 | |
| REFERENCES | 217 |
Recommended Citation
APA 7th Edition