Digital Agricultural Revolution

Roheet Bhatnagar, PhD, is a professor in the Department of Computer Science & Engineering, Manipal University Jaipur, India. He has published over 60 research papers in reputed conferences and journals, and edited five books.Nitin Kumar Tripathi, PhD, is a professor in Remote Sensing (RS) and Geographical Information Systems (GIS) at the Asian Institute of Technology (AIT), Thailand. He has supervised 42 Doctoral and 142 Masters theses where a majority of the research topics focused on the applications of GIS and RS in Climate Change impacts on water resources, agriculture, and health. Dr. Tripathi has a total of 182 publications to his credit (two books, 11 chapters in books, 109 research papers in peer-reviewed Journals, and 60 conference papers). Chandan Kumar Panda, PhD, is an assistant professor and research scientist in the Department of Extension Education at Bihar Agricultural University, Sabour, India. Nitu Bhatnagar, PhD, is an associate professor in the Department of Chemistry of the Faculty of Science at Manipal University Jaipur.

• Preface xv1  Scope and Recent Trends of Artificial Intelligence in Indian Agriculture 1X. Anitha Mary, Vladimir Popov, Kumudha Raimond, I. Johnson and S. J.Vijay1.1 Introduction 21.2 Different Forms of AI 21.3 Different Technologies in AI 31.3.1 Machine Learning 41.3.1.1 Data Pre-processing 51.3.1.2 Feature Extraction 51.3.1.3 Working With Data Sets 61.3.1.4 Model Development 61.3.1.5 Improving the Model With New Data 81.3.2 Artificial Neural Network 81.3.2.1 ANN in Agriculture 91.3.3 Deep Learning for Smart Agriculture 91.3.3.1 Data Pre-processing 101.3.3.2 Data Augmentation 101.3.3.3 Different DL Models 101.4 AI With Big Data and Internet of Things 111.5 AI in the Lifecycle of the Agricultural Process 121.5.1 Improving Crop Sowing and Productivity 121.5.2 Soil Health Monitoring 131.5.3 Weed and Pest Control 141.5.4 Water Management 141.5.5 Crop Harvesting 151.6 Indian Agriculture and Smart Farming 151.6.1 Sensors for Smart Farming 161.7 Advantages of Using AI in Agriculture 171.8 Role of AI in Indian Agriculture 181.9 Case Study in Plant Disease Identification Using AI Technology—Tomato and Potato Crops 191.10 Challenges in AI 201.11 Conclusion 21References 212 Comparative Evaluation of Neural Networks in Crop Yield Prediction of Paddy and Sugarcane Crop 25K. Krupavathi, M. Raghu Babu and A. Mani2.1 Introduction 262.2 Introduction to Artificial Neural Networks 272.2.1 Overview of Artificial Neural Networks 272.2.2 Components of Neural Networks 282.2.3 Types and Suitability of Neural Networks 292.3 Application of Neural Networks in Agriculture 302.3.1 Potential Applications of Neural Networks in Agriculture 302.3.2 Significance of Neural Networks in Crop Yield Prediction 322.4 Importance of Remote Sensing in Crop Yield Estimation 322.5 Derivation of Crop-Sensitive Parameters From Remote Sensing for Paddy and Sugarcane Crops 332.5.1 Study Area 332.5.2 Materials and Methods 352.5.2.1 Data Acquisition and Crop Parameters Retrieval From Remote Sensing Images 352.5.3 Results and Conclusions 372.6 Neural Network Model Development, Calibration and Validation 402.6.1 Materials and Methods 402.6.1.1 ANN Model Design 402.6.1.2 Model Training 422.6.1.3 Model Validation 432.6.2 Results and Conclusions 432.7 Conclusion 50References 503 Smart Irrigation Systems Using Machine Learning and Control Theory 57Meriç Çetin and Selami Beyhan3.1 Machine Learning for Irrigation Systems 583.2 Control Theory for Irrigation Systems 623.2.1 Application Literature 653.2.2 An Evaluation of Machine Learning–Based Irrigation Control Applications 723.2.3 Remote Control Extensions 723.3 Conclusion and Future Directions 75References 794 Enabling Technologies for Future Robotic Agriculture Systems: A Case Study in Indian Scenario 87X. Anitha Mary, Kannan Mani, Kumudha Raimond, Johnson I. and Dinesh Kumar P.4.1 Need for Robotics in Agriculture 884.2 Different Types of Agricultural Bots 894.2.1 Field Robots 894.2.2 Drones 904.2.3 Livestock Drones 914.2.4 Multirobot System 914.3 Existing Agricultural Robots 914.4 Precision Agriculture and Robotics 934.5 Technologies for Smart Farming 944.5.1 Concepts of Internet of Things 944.5.2 Big Data 944.5.3 Cyber Physical System 954.5.4 Cloud Computing 954.6 Impact of AI and Robotics in Agriculture 954.7 Unmanned Aerial Vehicles (UAV) in Agriculture 984.8 Agricultural Manipulators 994.9 Ethical Impact of Robotics and AI 994.10 Scope of Agribots in India 1004.11 Challenges in the Deployment of Robots 1014.12 Future Scope of Robotics in Agriculture 1024.13 Conclusion 103References 1035 The Applications of Industry 4.0 (I4.0) Technologies in the Palm Oil Industry in Colombia (Latin America) 109James Pérez-Morón and Ana Susana Cantillo-Orozco5.1 Introduction 1105.2 Methodology 1135.2.1 Sample Selection 1135.3 Results Analysis 1185.3.1 Data Visualization 1225.3.2 Cooccurrence 1235.3.3 Coauthorship 1235.3.4 Citation 1245.3.5 Cocitation 1255.4 Colombia PO Industry 1265.5 The PO Industry and the Circular Economy 1305.6 Conclusion 1315.7 Further Recommendations for the Colombian PO Industry 132Acknowledgments 133References 1336 Intelligent Multiagent System for Agricultural Management Processes (Case Study: Greenhouse) 143Djamel Saba, Youcef Sahli and Abdelkader HadidiAbbreviations 1446.1 Introduction 1446.2 Modern Agricultural Methods 1466.3 Internet of Things Applications in Smart Agriculture 1486.4 Artificial Intelligence 1496.4.1 Overview of AI 1496.4.2 Branches of DAI 1516.4.3 The Differences Between MAS and Computing Paradigms 1536.5 MAS 1556.5.1 Overview of MAS 1556.5.2 MAS Simulation 1576.6 Design and Implementation 1596.6.1 Conception of the Solution 1596.6.1.1 The Existing Study 1596.6.1.2 Agents List 1606.6.2 Introduction to the System Implementation 1616.6.2.1 Environment 1616.6.2.2 Group Communication (Multicast) 1626.6.2.3 Message Transport 1626.6.2.4 Data Exchange Format 1626.6.2.5 Cooperation 1636.6.2.6 Coordination 1646.6.2.7 Negotiation 1646.7 Analysis and Discussion 1646.8 Conclusion 167References 1687 Smart Irrigation System for Smart Agricultural Using IoT: Concepts, Architecture, and Applications 171Abdelkader Hadidi, Djamel Saba and Youcef Sahli7.1 Introduction 1727.2 Irrigation Systems 1737.2.1 Agricultural Irrigation Techniques 1747.2.2 Surface Irrigation Systems 1747.2.3 Sprinkler Irrigation 1777.2.4 Micro-Irrigation Systems 1787.2.5 Comparison of Irrigation Methods 1787.2.6 Efficiency of Irrigation Systems 1797.3 IoT 1807.3.1 IoT History 1807.3.2 IoT Architecture 1817.3.3 Examples of Uses for the IoT 1827.3.4 IoT Importance in Different Sectors 1837.4 IoT Applications in Agriculture 1847.4.1 Precision Cultivation 1847.4.2 Agricultural Unmanned Aircraft 1847.4.3 Livestock Control 1857.4.4 Smart Greenhouses 1857.5 IoT and Water Management 1857.6 Introduction to the Implementation 1867.7 Analysis and Discussion 1927.8 Conclusion 193References 1948 The Internet of Things (IoT) for Sustainable Agriculture 199Sadiq, M.S., Singh, I.P., Ahmad, M.M. and Karunakaran, N.8.1 Introduction 2008.2 ICT in Agriculture 2028.3 Internet of Things in Agriculture and Allied Sector 2038.3.1 Precision Farming 2058.3.2 Agriculture Drones 2088.3.3 Livestock Monitoring 2098.3.4 Smart Greenhouses 2108.4 Geospatial Technology 2118.4.1 Remote Sensing 2118.4.2 Geographic Information System 2158.4.3 GPS for Agriculture Resources Mapping 2178.5 Summary and Conclusion 222References 2239 Advances in Bionic Approaches for Agriculture and Forestry Development 225Vipin Parkash, Anuj Chauhan, Akshita Gaur and Nishant Rai9.1 Introduction 2269.2 Precision Farming 2279.2.1 Nanosensors and Its Role in Agriculture 2299.2.1.1 Nanobiosensor Use for Heavy Metal Detection 2309.2.1.2 Nanobiosensors Use for Urea Detection 2309.2.1.3 Nanosensors for Soil Analysis 2319.2.1.4 Nanosensors for Disease Assessment 2319.3 Powerful Role of Drones in Agriculture 2319.3.1 Unmanned Aerial Vehicle Providing Crop Data 2329.3.2 Using Raw Data to Produce Useful Information 2339.3.3 Crop Health Surveillance and Monitoring 2399.4 Nanobionics in Plants 2409.5 Role of Nanotechnology in Forestry 2419.5.1 Chemotaxonomy 2439.5.2 Wood and Paper Processing 2449.6 Conclusion 246References 24610 Simulation of Water Management Processes of Distributed Irrigation Systems 255Aysulu Aydarova10.1 Introduction 25510.2 Modeling of Water Facilities 25610.3 Processing and Conducting Experiments 26410.4 Conclusion 266References 26611 Conceptual Principles of Reengineering of Agricultural Resources: Open Problems, Challenges and Future Trends 269Zamlynskyi Viktor, Livinskyi Anatolii, Zamlynska Olha and Minakova Svetlana11.1 Introduction 27011.2 Modern Agronomy and Approaches for Environment Sustenance 27211.2.1 Sustainable Agriculture 27311.3 International Federation of Organic Agriculture Movements (IFOAM) and Significance 27811.4 Low Cost versus Sustainable Agricultural Production 28011.5 Change of Trends in Agriculture 284References 28712 Role of Agritech Start-Ups in Supply Chain—An Organizational Approach of Ninjacart 289D. Rafi and Md. Mubeena12.1 Introduction 29012.2 How Does the Chain Work? 29112.3 Undisrupted Chain of Ninjacart During Pandemic-19 29712.4 Conclusion 298References 29813 Institutional Model of Integrating Agricultural Production Technologies with Accounting and Information Systems 301Nataliya Kantsedal and Oksana Ponomarenko13.1 Introduction 30213.2 Research Methodology 30213.3 The General Model of a New Informational Paradigm of Agricultural Activities’ Organization 30313.4 The Model of Institutional Interaction of Information Agents in Agricultural Production 30513.5 Conclusions 308References 30914 Relevance of Artificial Intelligence in Wastewater Management 311Poornima Ramesh, Kathirvel Suganya, T. Uma Maheswari, S. Paul Sebastian and K. Sara Parwin Banu14.1 Introduction 31214.2 Digital Technologies and Industrial Sustainability 31314.3 Artificial Neural Networks and Its Categories 31514.4 AI in Technical Performance 31614.5 AI in Economic Performance 32214.6 AI in Management Performance 32314.7 AI in Wastewater Reuse 32414.8 Conclusion 325References 32615 Risks of Agrobusiness Digital Transformation 333Inna Riepina, Anastasiia Koval, Olexandr Starikov and Volodymyr Tokar15.1 Modern Global Trends in Agriculture 33415.2 The Global Innovative Differentiation 33715.3 National Indicative Planning of Innovative Transformations 34215.4 Key Myths and Risks of Digitalization of Agrobusiness 34915.5 Examples of Use of Digital Technologies in Agriculture 35015.6 Imperatives of Transforming the Region into a Cost-Effective Ecosystem of Digital Highly Productive and Risk-Free Agriculture 35115.7 Conclusion 354References 35616 Water Resource Management in Distributed Irrigation Systems 359Varlamova Lyudmila P., Yakubov Мaqsadhon S. and Elmurodova Barno E.16.1 Introduction 36016.2 Types of Mathematical Models for Modeling the Process of Managing Irrigation Channels 36016.3 Building a River Model 36216.3.1 Classification of Models by Solution Methods 36416.3.2 Method of Characteristics 36416.3.3 Hydrological Analogy Method 36516.3.4 Analysis of Works on the Formulation of Boundary Value Problems 36716.4 Spatial Hierarchy of River Terrain 36916.4.1 Small Drainage Basin Study Scheme 37116.4.2 Modeling Water Management in Uzbekistan 37116.4.3 Stages of Developing a Water Resources Management Model 37116.5 Organizations in the Structure of Water Resources Management 37416.6 Conclusion 375References 37517 Digital Transformation via Blockchain in the Agricultural Commodity Value Chain 379Necla İ. Küçükçolak and Ali Sabri Taylan17.1 Introduction 38017.2 Precision Agriculture for Food Supply Security 38017.2.1 Smart Agriculture Business 38117.2.2 Trading Venues for Contract Farming, Crowdfunding and E-Trades 38417.3 Blockchain Technology Practices and Literature Reviews on Food Supply Chain 38617.3.1 Food Supply Chain 38817.3.2 Smart Contracts 38917.4 Agricultural Sector Value Chain Digitalization 39117.4.1 Digital Solution for Contract Farming 39117.4.2 Commodity Funding 39217.4.2.1 Smart Contracts 39217.4.2.2 Crowdfunding Token Trading 39317.4.3 Digital Transfer System 39317.5 Conclusion 395References 39518 Role of Start-Ups in Altering Agrimarket Channel (Input-Output) 399D. Rafi and Md. Mubeena18.1 Introduction 40018.2 Agriculture Supply Chain Management 40018.3 How Start-Ups Fill the Concerns and Gaps in Agri Input Supply Chain? 40218.4 Output Supply Chain 40418.5 How Start-Ups are Filling the Concerns and Gaps in Agri Output Supply Chain? 40718.6 Conclusion 408References 40919 Development of Blockchain Agriculture Supply Chain Framework Using Social Network Theory: An Empirical Evidence Based on Malaysian Agriculture Firms 411Muhammad Shabir Shaharudin, Yudi Fernando, Yuvaraj Ganesan and Faizah Shahudin19.1 Introduction 41219.2 Literature Review 41319.2.1 Agriculture Malaysia 41319.2.2 Agriculture Supply Chain 41519.2.3 Blockchain Technology 41619.2.4 Blockchain Agriculture Supply Chain Management 41819.2.5 Social Network Theory 41919.2.6 Social Network Analysis 42019.3 Methodology 42119.3.1 Blockchain Agriculture Supply Chain Management Framework 42119.3.2 Research Design 42319.4 Results and Discussion 42419.4.1 Demographic Profiles 42419.4.2 Social Network Analysis Results 42419.5 Conclusion 44019.6 Acknowledgment 441References 44120 Potential Options and Applications of Machine Learning in Soil Science 447Anandkumar Naorem, Shiva Kumar Udayana and Somasundaram Jayaraman20.1 Introduction: A Deep Insight on Machine Learning, Deep Learning and Artificial Intelligence 44820.2 Application of ML in Soil Science 44920.3 Classification of ML Techniques 45220.3.1 Supervised ML 45320.3.2 Unsupervised ML 45320.3.3 Reinforcement ML 45320.4 Artificial Neural Network 45420.5 Support Vector Machine 45520.6 Conclusion 457References 457Index 461