Оглавление
Группа авторов. The Digital Agricultural Revolution
Table of Contents
List of Tables
List of Illustrations
Guide
Pages
The Digital Agricultural Revolution. Innovations and Challenges in Agriculture through Technology Disruptions
Preface
1. Scope and Recent Trends of Artificial Intelligence in Indian Agriculture
1.1 Introduction
1.2 Different Forms of AI
1.3 Different Technologies in AI
1.3.1 Machine Learning
1.3.1.1 Data Pre-processing
1.3.1.2 Feature Extraction
1.3.1.3 Working With Data Sets
1.3.1.4 Model Development
i. Support Vector Machine
ii. Regression Algorithm
iii. Decision Tree
iv. K-means Clustering
v. Association Algorithm
1.3.1.5 Improving the Model With New Data
1.3.2 Artificial Neural Network
1.3.2.1 ANN in Agriculture
1.3.3 Deep Learning for Smart Agriculture
1.3.3.1 Data Pre-processing
1.3.3.2 Data Augmentation
1.3.3.3 Different DL Models
1.4 AI With Big Data and Internet of Things
1.5 AI in the Lifecycle of the Agricultural Process
1.5.1 Improving Crop Sowing and Productivity
1.5.2 Soil Health Monitoring
1.5.3 Weed and Pest Control
1.5.4 Water Management
1.5.5 Crop Harvesting
1.6 Indian Agriculture and Smart Farming
1.6.1 Sensors for Smart Farming
1.7 Advantages of Using AI in Agriculture
1.8 Role of AI in Indian Agriculture
1.9 Case Study in Plant Disease Identification Using AI Technology—Tomato and Potato Crops
1.10 Challenges in AI
1.11 Conclusion
References
2. Comparative Evaluation of Neural Networks in Crop Yield Prediction of Paddy and Sugarcane Crop
2.1 Introduction
2.2 Introduction to Artificial Neural Networks. 2.2.1 Overview of Artificial Neural Networks
2.2.2 Components of Neural Networks
2.2.3 Types and Suitability of Neural Networks
2.3 Application of Neural Networks in Agriculture. 2.3.1 Potential Applications of Neural Networks in Agriculture
2.3.2 Significance of Neural Networks in Crop Yield Prediction
2.4 Importance of Remote Sensing in Crop Yield Estimation
2.5 Derivation of Crop-Sensitive Parameters From Remote Sensing for Paddy and Sugarcane Crops. 2.5.1 Study Area
2.5.2 Materials and Methods. 2.5.2.1 Data Acquisition and Crop Parameters Retrieval From Remote Sensing Images
2.5.3 Results and Conclusions
2.6 Neural Network Model Development, Calibration and Validation. 2.6.1 Materials and Methods. 2.6.1.1 ANN Model Design
2.6.1.2 Model Training
2.6.1.3 Model Validation
2.6.2 Results and Conclusions
2.7 Conclusion
References
3. Smart Irrigation Systems Using Machine Learning and Control Theory
3.1 Machine Learning for Irrigation Systems
3.2 Control Theory for Irrigation Systems
3.2.1 Application Literature
3.2.2 An Evaluation of Machine Learning–Based Irrigation Control Applications
3.2.3 Remote Control Extensions
3.3 Conclusion and Future Directions
References
4. Enabling Technologies for Future Robotic Agriculture Systems: A Case Study in Indian Scenario
4.1 Need for Robotics in Agriculture
4.2 Different Types of Agricultural Bots
4.2.1 Field Robots
4.2.2 Drones
4.2.3 Livestock Drones
4.2.4 Multirobot System
4.3 Existing Agricultural Robots
4.4 Precision Agriculture and Robotics
4.5 Technologies for Smart Farming
4.5.1 Concepts of Internet of Things
4.5.2 Big Data
4.5.3 Cyber Physical System
4.5.4 Cloud Computing
4.6 Impact of AI and Robotics in Agriculture
4.7 Unmanned Aerial Vehicles (UAV) in Agriculture
4.8 Agricultural Manipulators
4.9 Ethical Impact of Robotics and AI
4.10 Scope of Agribots in India
4.11 Challenges in the Deployment of Robots
4.12 Future Scope of Robotics in Agriculture
4.13 Conclusion
References
5. The Applications of Industry 4.0 (I4.0) Technologies in the Palm Oil Industry in Colombia (Latin America)
5.1 Introduction
5.2 Methodology. 5.2.1 Sample Selection
5.3 Results Analysis
5.3.1 Data Visualization
5.3.2 Cooccurrence
5.3.3 Coauthorship
5.3.4 Citation
5.3.5 Cocitation
5.4 Colombia PO Industry
5.5 The PO Industry and the Circular Economy
5.6 Conclusion
5.7 Further Recommendations for the Colombian PO Industry
Acknowledgments
References
6. Intelligent Multiagent System for Agricultural Management Processes (Case Study: Greenhouse)
Abbreviations
6.1 Introduction
6.2 Modern Agricultural Methods
6.3 Internet of Things Applications in Smart Agriculture
6.4 Artificial Intelligence. 6.4.1 Overview of AI
6.4.2 Branches of DAI
6.4.3 The Differences Between MAS and Computing Paradigms
6.5 MAS. 6.5.1 Overview of MAS
6.5.2 MAS Simulation
6.6 Design and Implementation. 6.6.1 Conception of the Solution
6.6.1.1 The Existing Study
6.6.1.2 Agents List
6.6.2 Introduction to the System Implementation. 6.6.2.1 Environment
6.6.2.2 Group Communication (Multicast)
6.6.2.3 Message Transport
6.6.2.4 Data Exchange Format
6.6.2.5 Cooperation
6.6.2.6 Coordination
6.6.2.7 Negotiation
6.7 Analysis and Discussion
6.8 Conclusion
References
7. Smart Irrigation System for Smart Agricultural Using IoT: Concepts, Architecture, and Applications
7.1 Introduction
7.2 Irrigation Systems
7.2.1 Agricultural Irrigation Techniques
7.2.2 Surface Irrigation Systems
7.2.3 Sprinkler Irrigation
7.2.4 Micro-Irrigation Systems
7.2.5 Comparison of Irrigation Methods
7.2.6 Efficiency of Irrigation Systems
7.3 IoT
7.3.1 IoT History
7.3.2 IoT Architecture
7.3.3 Examples of Uses for the IoT
7.3.4 IoT Importance in Different Sectors
7.4 IoT Applications in Agriculture
7.4.1 Precision Cultivation
7.4.2 Agricultural Unmanned Aircraft
7.4.3 Livestock Control
7.4.4 Smart Greenhouses
7.5 IoT and Water Management
7.6 Introduction to the Implementation
7.7 Analysis and Discussion
7.8 Conclusion
References
8. The Internet of Things (IoT) for Sustainable Agriculture
8.1 Introduction
8.2 ICT in Agriculture
8.3 Internet of Things in Agriculture and Allied Sector
8.3.1 Precision Farming
8.3.2 Agriculture Drones
8.3.3 Livestock Monitoring
8.3.4 Smart Greenhouses
8.4 Geospatial Technology
8.4.1 Remote Sensing
8.4.2 Geographic Information System
8.4.3 GPS for Agriculture Resources Mapping
8.5 Summary and Conclusion
References
9. Advances in Bionic Approaches for Agriculture and Forestry Development
9.1 Introduction
9.2 Precision Farming
9.2.1 Nanosensors and Its Role in Agriculture
9.2.1.1 Nanobiosensor Use for Heavy Metal Detection
9.2.1.2 Nanobiosensors Use for Urea Detection
9.2.1.3 Nanosensors for Soil Analysis
9.2.1.4 Nanosensors for Disease Assessment
9.3 Powerful Role of Drones in Agriculture
9.3.1 Unmanned Aerial Vehicle Providing Crop Data
9.3.2 Using Raw Data to Produce Useful Information
9.3.3 Crop Health Surveillance and Monitoring
9.4 Nanobionics in Plants
9.5 Role of Nanotechnology in Forestry
9.5.1 Chemotaxonomy
9.5.2 Wood and Paper Processing
9.6 Conclusion
References
10. Simulation of Water Management Processes of Distributed Irrigation Systems
10.1 Introduction
10.2 Modeling of Water Facilities
10.3 Processing and Conducting Experiments
10.4 Conclusion
References
11. Conceptual Principles of Reengineering of Agricultural Resources: Open Problems, Challenges and Future Trends
11.1 Introduction
11.2 Modern Agronomy and Approaches for Environment Sustenance
11.2.1 Sustainable Agriculture
11.3 International Federation of Organic Agriculture Movements (IFOAM) and Significance
11.4 Low Cost versus Sustainable Agricultural Production
11.5 Change of Trends in Agriculture
References
12. Role of Agritech Start-Ups in Supply Chain—An Organizational Approach of Ninjacart
12.1 Introduction
12.2 How Does the Chain Work?
12.3 Undisrupted Chain of Ninjacart During Pandemic-19
12.4 Conclusion
References
13. Institutional Model of Integrating Agricultural Production Technologies with Accounting and Information Systems
13.1 Introduction
13.2 Research Methodology
13.3 The General Model of a New Informational Paradigm of Agricultural Activities’ Organization
13.4 The Model of Institutional Interaction of Information Agents in Agricultural Production
13.5 Conclusions
References
14. Relevance of Artificial Intelligence in Wastewater Management
14.1 Introduction
14.2 Digital Technologies and Industrial Sustainability
14.3 Artificial Neural Networks and Its Categories
14.4 AI in Technical Performance
14.5 AI in Economic Performance
14.6 AI in Management Performance
14.7 AI in Wastewater Reuse
14.8 Conclusion
References
15. Risks of Agrobusiness Digital Transformation
15.1 Modern Global Trends in Agriculture
15.2 The Global Innovative Differentiation
15.3 National Indicative Planning of Innovative Transformations
15.4 Key Myths and Risks of Digitalization of Agrobusiness
15.5 Examples of Use of Digital Technologies in Agriculture
15.6 Imperatives of Transforming the Region into a Cost-Effective Ecosystem of Digital Highly Productive and Risk-Free Agriculture
15.7 Conclusion
References
16. Water Resource Management in Distributed Irrigation Systems
16.1 Introduction
16.2 Types of Mathematical Models for Modeling the Process of Managing Irrigation Channels
16.3 Building a River Model
16.3.1 Classification of Models by Solution Methods
16.3.2 Method of Characteristics
16.3.3 Hydrological Analogy Method
16.3.4 Analysis of Works on the Formulation of Boundary Value Problems
16.4 Spatial Hierarchy of River Terrain
16.4.1 Small Drainage Basin Study Scheme
16.4.2 Modeling Water Management in Uzbekistan
16.4.3 Stages of Developing a Water Resources Management Model
16.5 Organizations in the Structure of Water Resources Management
16.6 Conclusion
References
17. Digital Transformation via Blockchain in the Agricultural Commodity Value Chain
17.1 Introduction
17.2 Precision Agriculture for Food Supply Security
17.2.1 Smart Agriculture Business
17.2.2 Trading Venues for Contract Farming, Crowdfunding and E-Trades
17.3 Blockchain Technology Practices and Literature Reviews on Food Supply Chain
17.3.1 Food Supply Chain
17.3.2 Smart Contracts
17.4 Agricultural Sector Value Chain Digitalization
17.4.1 Digital Solution for Contract Farming
17.4.2 Commodity Funding
17.4.2.1 Smart Contracts
17.4.2.2 Crowdfunding Token Trading
17.4.3 Digital Transfer System
17.5 Conclusion
References
18. Role of Start-Ups in Altering Agrimarket Channel (Input-Output)
18.1 Introduction
18.2 Agriculture Supply Chain Management
18.3 How Start-Ups Fill the Concerns and Gaps in Agri Input Supply Chain?
18.4 Output Supply Chain
18.5 How Start-Ups are Filling the Concerns and Gaps in Agri Output Supply Chain
18.6 Conclusion
References
19. Development of Blockchain Agriculture Supply Chain Framework Using Social Network Theory: An Empirical Evidence Based on Malaysian Agriculture Firms
19.1 Introduction
19.2 Literature Review. 19.2.1 Agriculture Malaysia
19.2.2 Agriculture Supply Chain
19.2.3 Blockchain Technology
19.2.4 Blockchain Agriculture Supply Chain Management
19.2.5 Social Network Theory
19.2.6 Social Network Analysis
19.3 Methodology. 19.3.1 Blockchain Agriculture Supply Chain Management Framework
19.3.2 Research Design
19.4 Results and Discussion. 19.4.1 Demographic Profiles
19.4.2 Social Network Analysis Results
19.5 Conclusion
19.6 Acknowledgment
References
20. Potential Options and Applications of Machine Learning in Soil Science
20.1 Introduction: A Deep Insight on Machine Learning, Deep Learning and Artificial Intelligence
20.2 Application of ML in Soil Science
20.3 Classification of ML Techniques
20.3.1 Supervised ML
20.3.2 Unsupervised ML
20.3.3 Reinforcement ML
20.4 Artificial Neural Network
20.5 Support Vector Machine
20.6 Conclusion
References
Index
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