Smart Agriculture Automation Using Advanced Technologies: Data Analytics and Machine Learning, Cloud Architecture, Automation and IoT (Transactions on Computer Systems and Networks)

✍ Scribed by Amitava Choudhury (editor), Arindam Biswas (editor), T. P. Singh (editor), Santanu Kumar Ghosh (editor)

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 236
• Category: Library

✦ Synopsis

This book addresses the challenges for developing and emerging trends in Internet-of-Things (IoT) for smart agriculture platforms. It also describes data analytics & machine learning, cloud architecture, automation & robotics and aims to overcome existing barriers for smart agriculture with commercial viability. It discusses IoT-based monitoring systems for analyzing the crop environment, and methods for improving the efficiency of decision-making based on the analysis of harvest statistics. The book explores a range of applications including intelligent field monitoring, intelligent data processing and sensor technologies, predictive analysis systems, crop monitoring, and weather data-enabled analysis in IoT agro-systems. This volume will be helpful for engineering and technology experts and researchers, as well as for policy-makers.

✦ Table of Contents

Preface
Contents
Editors and Contributors
1 Smart Agriculture Using IoT and Machine Learning
1.1 Introduction
1.1.1 IoT Ecosystem
1.1.2 IoT Applied in the Field of Agriculture
1.1.3 Machine Learning Applications in IoT-Based Agriculture
1.1.4 Several Methods Have Been Incorporated in Machine Learning Which Has Been Listed Below
References
2 Precision Farming and Its Application
2.1 Introduction
2.2 Why Precision Farming
2.3 Apparatus and Instruments
2.3.1 GPS (Global Positioning System)
2.3.2 VRT (Variable-Rate Technology)
2.3.3 Soil Sampling by Using Grid Technology
2.3.4 GIS (Geographic Information System)
2.3.5 Crop and Soil Sensors
2.3.6 Sensor-Based Equipment
2.3.7 Monitoring of Crop Yield
2.4 Image-Based Sensing
2.4.1 Satellite Platforms
2.4.2 UAV (Unmanned Aerial Vehicle) Platforms
2.4.3 Airborne Platforms
2.5 Application of WSN in Agriculture
2.5.1 Advanced Pest Management Technology and Early Disease Prediction Methodology
2.5.2 Precise Irrigation Approach by Using Advanced Computational Techniques
2.5.3 Morden Fertilization Approach Based on IoT and Sensors Techniques
2.6 Major Problems of Precision Farming
2.6.1 Data Management
2.6.2 Environmental Variations
2.6.3 Accessibility of Farm Insight Information
2.6.4 Hardware Cost
2.6.5 Data Security
2.6.6 Exchange of Information
2.6.7 System (Computing) Sustainability
2.7 Conclusion and Future Prospects
References
3 Smart Dairy Farming Overview: Innovation, Algorithms and Challenges
3.1 Introduction
3.2 Smart Dairy Farming
3.2.1 Functions of Technology in Dairy Production
3.2.2 Elements of a Smart Dairy Farm
3.3 Innovations
3.4 Innovations in Smart Dairy Farming
3.5 Algorithms
3.6 Deployment and Challenges
3.7 Conclusion
References
4 Precision Farming in Modern Agriculture
4.1 Introduction
4.2 Related Works
4.3 Role of IoT in Precision Agriculture
4.4 Role of Artificial Intelligence in Precision Farming
4.4.1 Pest Management
4.4.2 Crop Management
4.4.3 Soil and Irrigation Management
4.4.4 Agriculture Product Monitoring and Storage Control
4.4.5 Disease Management
4.4.6 Yield Prediction
4.4.7 Weed Management
4.5 Role of Other Important Tools and Techniques Used in Precision Farming
4.5.1 Remote Sensors
4.5.2 Geographical Information System (GIS)
4.5.3 Global Positioning System (GPS)
4.5.4 Role of Nano Technology in Precision Farming
4.6 Real-Time Applications and Instruments Used in Precision Farming
4.6.1 Use of Robotics in Precision Farming
4.6.2 Usage of Drones in Precision Farming
4.7 Benefits of Precision Farming
4.8 Conclusion
References
5 ML-Based Smart Farming Using LSTM
5.1 Introduction
5.2 Background and Related Works
5.3 Proposed Model
5.3.1 pH Sensor
5.3.2 Temperature and Humidity Sensor
5.3.3 Soil Moisture Sensor
5.3.4 RGB-D Sensor
5.3.5 Arduino Uno
5.3.6 nRF24L01 Transceiver
5.3.7 Raspberry Pi
5.3.8 Machine Learning
5.3.9 Thing Speak
5.4 Methodology
5.5 Performance Analysis
5.6 Future Research Direction
5.7 Conclusion
References
6 IoT Doordarshi: Smart Weather Monitoring System Using Sense Hat for Improving the Quality of Crops
6.1 Introduction
6.2 Methodology
6.2.1 Components Used for Implementation of System
6.3 System Implementation
6.4 Conclusion
References
7 IoT-Enabled Smart Farming: Challenges and Opportunities
7.1 Introduction to IoT-Based Smart Farming
7.1.1 Key Drivers of IoT Technology in Smart Farming
7.1.2 Challenges Faced to Implement IoT Technology in Smart Farming
7.2 Major Applications of IoT in Smart Farming
7.3 Equipment and Technologies for Smart Farming
7.3.1 Wireless Sensors
7.3.2 Communication Technologies
7.4 Role of Big Data in Smart Farming
7.4.1 Big Data Tools for IoT Applications
7.4.2 Benefits of Integrating IoT with Big Data Tools
7.4.3 Key Challenges in Implementation of IoT and Big Data Tools Applications
7.5 Future Opportunities in the Domain of Smart Farming
References
8 Fermat Point-Based Wireless Sensor Networks: A Default Choice for Measuring and Reporting Farm Parameters in Precision Agriculture
8.1 Introduction
8.2 Literature Survey
8.3 Proposed Framework
8.4 Results
8.5 Conclusion
References
9 Application of IoT-Enabled 5G Network in the Agricultural Sector
9.1 Introduction
9.2 IoT in Smart Agriculture
9.2.1 Advantages of Smart Farming System
9.2.2 IoT-Based Agricultural System
9.3 The Use of 5G in Agriculture
9.3.1 Advantages of 5G Network Over Other Conventional Networks
9.3.2 Application of Drones in the Agricultural Sector Based on 5G Networks
9.3.3 5G Network-Based Augmented Reality and Virtual Reality in the Agricultural Sector
9.4 Data Analysis and Discussion
9.5 Artificial Intelligence-Powered Robots
9.6 Conclusion
References
10 An Economical Helping Hand for Farmers—Agricultural Drone
10.1 Introduction
10.2 Design and Working
10.3 Fuzzy Logic
10.3.1 Methodology
10.3.2 Results
10.4 Spraying Mechanism
10.4.1 Pump ON/OFF Control
10.4.2 Spraying Speed Control
10.4.3 Tank Status
10.5 Future Scope
10.6 Other Uses
10.7 Conclusion
References
11 On Securing Smart Agriculture Systems: A Data Aggregation Security Perspective
11.1 Introduction
11.2 Risk Assessment
11.2.1 Overview
11.2.2 Risk Assessment
11.2.3 Assets Identification and Prioritization
11.2.4 Malicious Actors and Threats Identification
11.2.5 Vulnerability Identification and Quantization
11.2.6 Risk Estimation and Calculation
11.3 Designing Secure CPS
11.3.1 Architecture of the CPS System Overview
11.3.2 Risk of Interaction with External Systems
11.3.3 Architecture Changes Over Time
11.3.4 CPS Security Model
11.3.5 Assets to Security Model Tracing
11.4 CPS Security Analysis
11.4.1 Analysis of Selection
11.4.2 Security Analysis Results
11.4.3 Security Risks
11.4.4 Potential Vulnerability in the Subdomain
11.4.5 Project-Specific Checklist
11.5 Conclusion
References
12 Urea Spreaders for Improving the Crop Productivity in Agriculture: Recent Developments
12.1 Introduction
12.2 Different Types of Urea Spreader Mechanisms
12.2.1 Traditional Method
12.2.2 Tank Based Fertilizer Spreading Machine
12.2.3 Manually Operated Based Fertilizer Spreader
12.2.4 Wheel Based Automatic Fertilizer Spreader
12.2.5 Trolley Mounted Fertilizer Spreader
12.2.6 Solar Based Fertilizer Spreader
12.2.7 Tractor Mounted Fertilizer Spreader
12.3 Challenges and Issues
12.4 Conclusion
References
13 Agricultural Informatics and practices—The Concerns in Developing and Developed Countries
13.1 Introduction
13.2 Objective
13.3 Agricultural Informatics: Inception and Context of Developing and Developed Countries
13.3.1 ICT in Agriculture in European Union
13.3.2 Community Empowerment Initiatives
13.4 Africa, Digital Divide, and Agro Informatics
13.5 ICT in Agriculture: Developing Countries Context
13.5.1 Inadequate Personnel and IT Infrastructure
13.5.2 Power Supply, ICT Skills, Communication Skills
13.5.3 Improving Market Access and Financial Aspects
13.5.4 Climate and Early Warning Systems and Developing Countries
13.6 ICT in Agriculture in Developed Countries in Respect Developing Countries
13.7 Conclusion
References