| Description |
1 online resource (315 p.) |
| Contents |
Cover -- Half Title -- Title -- Copyright -- Table of Contents -- About the Authors -- Preface -- Chapter 1 Introduction to Smart Farming -- 1.1 Introduction -- 1.2 Traditional Farming Preliminaries -- 1.2.1 Resource-Intensive Practices -- 1.2.2 Climate Vulnerability and Uncertainty -- 1.2.3 High Labor Dependency -- 1.2.4 Limited Data-Driven Decision-Making -- 1.2.5 Economic Pressures and Market Volatility -- 1.2.6 Land Degradation and Loss of Biodiversity -- 1.2.7 Lack of Access to Modern Agricultural Knowledge and Technology -- 1.2.8 Long-Term Sustainability and Climate Resilience |
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1.3 Principles of Smart Farming -- 1.4 Smart Farming Technologies -- 1.4.1 Smart Farming Cycle -- 1.4.2 Other Auxiliary Smart Farming Technologies -- 1.5 Cybersecurity and Privacy-Related Issues -- 1.6 Emerging Smart Farming Techniques -- 1.6.1 Hydroponics -- 1.6.2 Vertical Farming -- 1.6.3 Phenotyping -- 1.6.4 Aerial Imaging and Drone Technology -- 1.6.5 Precision Livestock Farming -- 1.6.6 Blockchain in Agriculture -- References -- Chapter 2 Big Data in Smart Farming -- 2.1 Introduction -- 2.2 Differences between Traditional and Smart Farming -- 2.3 Types of Smart Farming Data |
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2.3.1 Data Types According to Farming Practices -- 2.3.2 Data Types According to Structure -- 2.4 Big Data Background -- 2.4.1 "Vs3" Dimension Model -- 2.4.2 "Vs5" Dimension Model -- 2.4.3 General Framework of Big Data -- 2.5 The Primary Production Framework of Big Data in Smart Farming -- 2.5.1 Edge Computing -- 2.5.2 Fog Computing -- 2.5.3 Cloud Computing -- 2.6 The Socioeconomic Framework of Big Data in Smart Farming -- 2.6.1 Farming Business Processes -- 2.6.2 Stakeholders -- 2.6.3 Network Administration -- 2.6.4 Auxiliary Drivers -- 2.6.5 Challenges of Smart Farming -- References |
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Chapter 3 Conceptualization of Machine Learning for Smart Farming -- 3.1 Introduction -- 3.2 Definition of Machine Learning -- 3.3 Data and Preprocessing -- 3.3.1 Numerical Data -- 3.3.2 Categorical Data -- 3.3.3 Time Series Data -- 3.3.4 Image Data -- 3.3.5 Textual Data -- 3.5.6 Spatial Data -- 3.5.7 Graph Data -- 3.4 Classification of Machine Learning -- 3.4.1 Supervised Learning -- 3.4.2 Unsupervised Learning -- 3.4.3 Reinforcement Learning -- 3.5 From Neural Networks to Deep Learning -- 3.6 The Role of Machine Learning in Smart Agriculture |
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3.6.1 Automatic Identification of Ripened Crops and Drought Patterns -- 3.6.2 Real-Time Chatbot-Based Answers to Agricultural Challenges -- 3.6.3 Optimization of the Farming Process by ML-Powered Solutions -- 3.6.4 Crop Yield Optimization -- 3.6.5 More Advancement -- References -- Chapter 4 From Field to Database: Sensors, Data Collection, and Efficient Management in Smart Farming -- 4.1 Introduction to Sensor Technology in Smart Farming -- 4.2 Taxonomy of Agricultural Sensors -- 4.2.1 Environmental Sensors -- 4.2.2 Plant Health Sensors -- 4.2.3 Nutrient Sensors -- 4.2.4 Crop Yield Sensors |
| Summary |
This book provides a broad overview of the areas of AI that can be used for smart farming applications, either through successful engineering or ground-breaking research. Among them, the highlighted tactics are soil management, water management, crop management, livestock management, harvesting, and the integration of IoT in smart farming |
| Notes |
Description based upon print version of record |
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4.2.5 Animal Monitoring Sensors |
| Genre/Form |
Electronic books
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| Form |
Electronic book
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| Author |
Hawash, Hossam
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Abdel-Fatah, Laila
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| ISBN |
9781003861850 |
|
1003861857 |
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