into a Cost-Effective Ecosystem of Digital Highly Productive and Risk-Free Agriculture
15.7 Conclusion
References
20 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 16.4 Spatial Hierarchy of River Terrain 16.5 Organizations in the Structure of Water Resources Management 16.6 Conclusion References
21 17 Digital Transformation via Blockchain in the Agricultural Commodity Value Chain 17.1 Introduction 17.2 Precision Agriculture for Food Supply Security 17.3 Blockchain Technology Practices and Literature Reviews on Food Supply Chain 17.4 Agricultural Sector Value Chain Digitalization 17.5 Conclusion References
22 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
23 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 19.3 Methodology 19.4 Results and Discussion 19.5 Conclusion 19.6 Acknowledgment References
24 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 20.4 Artificial Neural Network 20.5 Support Vector Machine 20.6 Conclusion References
25 Index
26 Wiley End User License Agreement
List of Tables
1 Chapter 1Table 1.1 Distinguishing feature of subfields of AI.
2 Chapter 2Table 2.1 Land use of the study area in the year 2014–2015.Table 2.2 Sample normalized input data of FFBPNN yield estimation model of paddy...Table 2.3 Sample and training result of FFBPNN yield prediction model of paddy c...Table 2.4 Statistical analysis of neural network training and testing of season ...
3 Chapter 3Table 3.1 Abbreviations of machine learning techniques.Table 3.2 Recent machine learning practices of the irrigation systems.Table 3.3 Abbreviations of control theory techniques.Table 3.4 Recent control theory applications of the irrigation systems.Table 3.5 Aforementioned remote control extensions for irrigation systems.
4 Chapter 5Table 5.1 Top 10 PO world production.Table 5.2 Top 10 PO world exports 2020.Table 5.3 Top 10 palm oil world imports 2020.Table 5.4 Focal articles selected.Table 5.5 Top 10 journals publishing the focal articles, citations, and impact f...Table 5.6 Top 10 most-cited focal articles.Table 5.7 Colombia’s palm oil plantations.Table 5.8 Colombia’s palm oil technologies.
5 Chapter 6Table 6.1 Extract from system agents.Table 6.2 Extract agents from the solution.
6 Chapter 7Table 7.1 Value in% of field efficiency [33].Table 7.2 AT commands dedicated to the SMS service.
7 Chapter 9Table 9.1 List of various application of UAV in agriculture sector.Table 9.2 Agricultural and forestry applications of e-noses.
8 Chapter 10Table 10.1 Average annual water consumption in Karasu, Parkent, Handam in 2011–2...
9 Chapter 14Table 14.1 AI application in pollutant removal during wastewater treatment.
10 Chapter 15Table 15.1 The main features of technological systems [20].
