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The Digital Agricultural Revolution

Innovations and Challenges in Agriculture through Technology Disruptions
1. Aufl.

von: Roheet Bhatnagar, Nitin Kumar Tripathi, Nitu Bhatnagar, Chandan Kumar Panda
173,99 €
Verlag:	Wiley
Format:	EPUB
Veröffentl.:	17.05.2022
ISBN/EAN:	9781119823445
Sprache:	englisch
Anzahl Seiten:	496

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Titelbeschreibung

THE DIGITAL AGRICULTURAL REVOLUTION The book integrates computational intelligence, applied artificial intelligence, and modern agricultural practices and will appeal to scientists, agriculturists, and those in plant and crop science management. There is a need for synergy between the application of modern scientific innovation in the area of artificial intelligence and agriculture, considering the major challenges from climate change consequences viz. rising temperatures, erratic rainfall patterns, the emergence of new crop pests, drought, flood, etc. This volume reports on high-quality research (theory and practice including prototype & conceptualization of ideas, frameworks, real-world applications, policy, standards, psychological concerns, case studies, and critical surveys) on recent advances toward the realization of the digital agriculture revolution as a result of the convergence of different disruptive technologies. The book touches upon the following topics which have contributed to revolutionizing agricultural practices. <ul><li>Applications of Artificial Intelligence in Agriculture (AI models and architectures, system design, real-world applications of AI, machine learning and deep learning in the agriculture domain, integration & coordination of systems and issues & challenges).</li> <li>IoT and Big Data Analytics Applications in Agriculture (theory & architecture and the use of various types of sensors in optimizing agriculture resources and final product, benefits in real-time for crop acreage estimation, monitoring & control of agricultural produce).</li> <li>Robotics & Automation in Agriculture Systems (Automation challenges, need and recent developments and real case studies).</li> <li>Intelligent and Innovative Smart Agriculture Applications (use of hybrid intelligence in better crop health and management).</li> <li>Privacy, Security, and Trust in Digital Agriculture (government framework & policy papers).</li> <li>Open Problems, Challenges, and Future Trends.</li></ul> Audience Researchers in computer science, artificial intelligence, electronics engineering, agriculture automation, crop management, and science.

Inhaltsverzeichnis

Preface xv 1 Scope and Recent Trends of Artificial Intelligence in Indian Agriculture 1 X. Anitha Mary, Vladimir Popov, Kumudha Raimond, I. Johnson and S. J.Vijay 1.1 Introduction 2 1.2 Different Forms of AI 2 1.3 Different Technologies in AI 3 1.3.1 Machine Learning 4 1.3.1.1 Data Pre-processing 5 1.3.1.2 Feature Extraction 5 1.3.1.3 Working With Data Sets 6 1.3.1.4 Model Development 6 1.3.1.5 Improving the Model With New Data 8 1.3.2 Artificial Neural Network 8 1.3.2.1 ANN in Agriculture 9 1.3.3 Deep Learning for Smart Agriculture 9 1.3.3.1 Data Pre-processing 10 1.3.3.2 Data Augmentation 10 1.3.3.3 Different DL Models 10 1.4 AI With Big Data and Internet of Things 11 1.5 AI in the Lifecycle of the Agricultural Process 12 1.5.1 Improving Crop Sowing and Productivity 12 1.5.2 Soil Health Monitoring 13 1.5.3 Weed and Pest Control 14 1.5.4 Water Management 14 1.5.5 Crop Harvesting 15 1.6 Indian Agriculture and Smart Farming 15 1.6.1 Sensors for Smart Farming 16 1.7 Advantages of Using AI in Agriculture 17 1.8 Role of AI in Indian Agriculture 18 1.9 Case Study in Plant Disease Identification Using AI Technology—Tomato and Potato Crops 19 1.10 Challenges in AI 20 1.11 Conclusion 21 References 21 2 Comparative Evaluation of Neural Networks in Crop Yield Prediction of Paddy and Sugarcane Crop 25 K. Krupavathi, M. Raghu Babu and A. Mani 2.1 Introduction 26 2.2 Introduction to Artificial Neural Networks 27 2.2.1 Overview of Artificial Neural Networks 27 2.2.2 Components of Neural Networks 28 2.2.3 Types and Suitability of Neural Networks 29 2.3 Application of Neural Networks in Agriculture 30 2.3.1 Potential Applications of Neural Networks in Agriculture 30 2.3.2 Significance of Neural Networks in Crop Yield Prediction 32 2.4 Importance of Remote Sensing in Crop Yield Estimation 32 2.5 Derivation of Crop-Sensitive Parameters From Remote Sensing for Paddy and Sugarcane Crops 33 2.5.1 Study Area 33 2.5.2 Materials and Methods 35 2.5.2.1 Data Acquisition and Crop Parameters Retrieval From Remote Sensing Images 35 2.5.3 Results and Conclusions 37 2.6 Neural Network Model Development, Calibration and Validation 40 2.6.1 Materials and Methods 40 2.6.1.1 ANN Model Design 40 2.6.1.2 Model Training 42 2.6.1.3 Model Validation 43 2.6.2 Results and Conclusions 43 2.7 Conclusion 50 References 50 3 Smart Irrigation Systems Using Machine Learning and Control Theory 57 Meriç Çetin and Selami Beyhan 3.1 Machine Learning for Irrigation Systems 58 3.2 Control Theory for Irrigation Systems 62 3.2.1 Application Literature 65 3.2.2 An Evaluation of Machine Learning–Based Irrigation Control Applications 72 3.2.3 Remote Control Extensions 72 3.3 Conclusion and Future Directions 75 References 79 4 Enabling Technologies for Future Robotic Agriculture Systems: A Case Study in Indian Scenario 87 X. Anitha Mary, Kannan Mani, Kumudha Raimond, Johnson I. and Dinesh Kumar P. 4.1 Need for Robotics in Agriculture 88 4.2 Different Types of Agricultural Bots 89 4.2.1 Field Robots 89 4.2.2 Drones 90 4.2.3 Livestock Drones 91 4.2.4 Multirobot System 91 4.3 Existing Agricultural Robots 91 4.4 Precision Agriculture and Robotics 93 4.5 Technologies for Smart Farming 94 4.5.1 Concepts of Internet of Things 94 4.5.2 Big Data 94 4.5.3 Cyber Physical System 95 4.5.4 Cloud Computing 95 4.6 Impact of AI and Robotics in Agriculture 95 4.7 Unmanned Aerial Vehicles (UAV) in Agriculture 98 4.8 Agricultural Manipulators 99 4.9 Ethical Impact of Robotics and AI 99 4.10 Scope of Agribots in India 100 4.11 Challenges in the Deployment of Robots 101 4.12 Future Scope of Robotics in Agriculture 102 4.13 Conclusion 103 References 103 5 The Applications of Industry 4.0 (I4.0) Technologies in the Palm Oil Industry in Colombia (Latin America) 109 James Pérez-Morón and Ana Susana Cantillo-Orozco 5.1 Introduction 110 5.2 Methodology 113 5.2.1 Sample Selection 113 5.3 Results Analysis 118 5.3.1 Data Visualization 122 5.3.2 Cooccurrence 123 5.3.3 Coauthorship 123 5.3.4 Citation 124 5.3.5 Cocitation 125 5.4 Colombia PO Industry 126 5.5 The PO Industry and the Circular Economy 130 5.6 Conclusion 131 5.7 Further Recommendations for the Colombian PO Industry 132 Acknowledgments 133 References 133 6 Intelligent Multiagent System for Agricultural Management Processes (Case Study: Greenhouse) 143 Djamel Saba, Youcef Sahli and Abdelkader Hadidi Abbreviations 144 6.1 Introduction 144 6.2 Modern Agricultural Methods 146 6.3 Internet of Things Applications in Smart Agriculture 148 6.4 Artificial Intelligence 149 6.4.1 Overview of AI 149 6.4.2 Branches of DAI 151 6.4.3 The Differences Between MAS and Computing Paradigms 153 6.5 MAS 155 6.5.1 Overview of MAS 155 6.5.2 MAS Simulation 157 6.6 Design and Implementation 159 6.6.1 Conception of the Solution 159 6.6.1.1 The Existing Study 159 6.6.1.2 Agents List 160 6.6.2 Introduction to the System Implementation 161 6.6.2.1 Environment 161 6.6.2.2 Group Communication (Multicast) 162 6.6.2.3 Message Transport 162 6.6.2.4 Data Exchange Format 162 6.6.2.5 Cooperation 163 6.6.2.6 Coordination 164 6.6.2.7 Negotiation 164 6.7 Analysis and Discussion 164 6.8 Conclusion 167 References 168 7 Smart Irrigation System for Smart Agricultural Using IoT: Concepts, Architecture, and Applications 171 Abdelkader Hadidi, Djamel Saba and Youcef Sahli 7.1 Introduction 172 7.2 Irrigation Systems 173 7.2.1 Agricultural Irrigation Techniques 174 7.2.2 Surface Irrigation Systems 174 7.2.3 Sprinkler Irrigation 177 7.2.4 Micro-Irrigation Systems 178 7.2.5 Comparison of Irrigation Methods 178 7.2.6 Efficiency of Irrigation Systems 179 7.3 IoT 180 7.3.1 IoT History 180 7.3.2 IoT Architecture 181 7.3.3 Examples of Uses for the IoT 182 7.3.4 IoT Importance in Different Sectors 183 7.4 IoT Applications in Agriculture 184 7.4.1 Precision Cultivation 184 7.4.2 Agricultural Unmanned Aircraft 184 7.4.3 Livestock Control 185 7.4.4 Smart Greenhouses 185 7.5 IoT and Water Management 185 7.6 Introduction to the Implementation 186 7.7 Analysis and Discussion 192 7.8 Conclusion 193 References 194 8 The Internet of Things (IoT) for Sustainable Agriculture 199 Sadiq, M.S., Singh, I.P., Ahmad, M.M. and Karunakaran, N. 8.1 Introduction 200 8.2 ICT in Agriculture 202 8.3 Internet of Things in Agriculture and Allied Sector 203 8.3.1 Precision Farming 205 8.3.2 Agriculture Drones 208 8.3.3 Livestock Monitoring 209 8.3.4 Smart Greenhouses 210 8.4 Geospatial Technology 211 8.4.1 Remote Sensing 211 8.4.2 Geographic Information System 215 8.4.3 GPS for Agriculture Resources Mapping 217 8.5 Summary and Conclusion 222 References 223 9 Advances in Bionic Approaches for Agriculture and Forestry Development 225 Vipin Parkash, Anuj Chauhan, Akshita Gaur and Nishant Rai 9.1 Introduction 226 9.2 Precision Farming 227 9.2.1 Nanosensors and Its Role in Agriculture 229 9.2.1.1 Nanobiosensor Use for Heavy Metal Detection 230 9.2.1.2 Nanobiosensors Use for Urea Detection 230 9.2.1.3 Nanosensors for Soil Analysis 231 9.2.1.4 Nanosensors for Disease Assessment 231 9.3 Powerful Role of Drones in Agriculture 231 9.3.1 Unmanned Aerial Vehicle Providing Crop Data 232 9.3.2 Using Raw Data to Produce Useful Information 233 9.3.3 Crop Health Surveillance and Monitoring 239 9.4 Nanobionics in Plants 240 9.5 Role of Nanotechnology in Forestry 241 9.5.1 Chemotaxonomy 243 9.5.2 Wood and Paper Processing 244 9.6 Conclusion 246 References 246 10 Simulation of Water Management Processes of Distributed Irrigation Systems 255 Aysulu Aydarova 10.1 Introduction 255 10.2 Modeling of Water Facilities 256 10.3 Processing and Conducting Experiments 264 10.4 Conclusion 266 References 266 11 Conceptual Principles of Reengineering of Agricultural Resources: Open Problems, Challenges and Future Trends 269 Zamlynskyi Viktor, Livinskyi Anatolii, Zamlynska Olha and Minakova Svetlana 11.1 Introduction 270 11.2 Modern Agronomy and Approaches for Environment Sustenance 272 11.2.1 Sustainable Agriculture 273 11.3 International Federation of Organic Agriculture Movements (IFOAM) and Significance 278 11.4 Low Cost versus Sustainable Agricultural Production 280 11.5 Change of Trends in Agriculture 284 References 287 12 Role of Agritech Start-Ups in Supply Chain—An Organizational Approach of Ninjacart 289 D. Rafi and Md. Mubeena 12.1 Introduction 290 12.2 How Does the Chain Work? 291 12.3 Undisrupted Chain of Ninjacart During Pandemic-19 297 12.4 Conclusion 298 References 298 13 Institutional Model of Integrating Agricultural Production Technologies with Accounting and Information Systems 301 Nataliya Kantsedal and Oksana Ponomarenko 13.1 Introduction 302 13.2 Research Methodology 302 13.3 The General Model of a New Informational Paradigm of Agricultural Activities’ Organization 303 13.4 The Model of Institutional Interaction of Information Agents in Agricultural Production 305 13.5 Conclusions 308 References 309 14 Relevance of Artificial Intelligence in Wastewater Management 311 Poornima Ramesh, Kathirvel Suganya, T. Uma Maheswari, S. Paul Sebastian and K. Sara Parwin Banu 14.1 Introduction 312 14.2 Digital Technologies and Industrial Sustainability 313 14.3 Artificial Neural Networks and Its Categories 315 14.4 AI in Technical Performance 316 14.5 AI in Economic Performance 322 14.6 AI in Management Performance 323 14.7 AI in Wastewater Reuse 324 14.8 Conclusion 325 References 326 15 Risks of Agrobusiness Digital Transformation 333 Inna Riepina, Anastasiia Koval, Olexandr Starikov and Volodymyr Tokar 15.1 Modern Global Trends in Agriculture 334 15.2 The Global Innovative Differentiation 337 15.3 National Indicative Planning of Innovative Transformations 342 15.4 Key Myths and Risks of Digitalization of Agrobusiness 349 15.5 Examples of Use of Digital Technologies in Agriculture 350 15.6 Imperatives of Transforming the Region into a Cost-Effective Ecosystem of Digital Highly Productive and Risk-Free Agriculture 351 15.7 Conclusion 354 References 356 16 Water Resource Management in Distributed Irrigation Systems 359 Varlamova Lyudmila P., Yakubov Мaqsadhon S. and Elmurodova Barno E. 16.1 Introduction 360 16.2 Types of Mathematical Models for Modeling the Process of Managing Irrigation Channels 360 16.3 Building a River Model 362 16.3.1 Classification of Models by Solution Methods 364 16.3.2 Method of Characteristics 364 16.3.3 Hydrological Analogy Method 365 16.3.4 Analysis of Works on the Formulation of Boundary Value Problems 367 16.4 Spatial Hierarchy of River Terrain 369 16.4.1 Small Drainage Basin Study Scheme 371 16.4.2 Modeling Water Management in Uzbekistan 371 16.4.3 Stages of Developing a Water Resources Management Model 371 16.5 Organizations in the Structure of Water Resources Management 374 16.6 Conclusion 375 References 375 17 Digital Transformation via Blockchain in the Agricultural Commodity Value Chain 379 Necla İ. Küçükçolak and Ali Sabri Taylan 17.1 Introduction 380 17.2 Precision Agriculture for Food Supply Security 380 17.2.1 Smart Agriculture Business 381 17.2.2 Trading Venues for Contract Farming, Crowdfunding and E-Trades 384 17.3 Blockchain Technology Practices and Literature Reviews on Food Supply Chain 386 17.3.1 Food Supply Chain 388 17.3.2 Smart Contracts 389 17.4 Agricultural Sector Value Chain Digitalization 391 17.4.1 Digital Solution for Contract Farming 391 17.4.2 Commodity Funding 392 17.4.2.1 Smart Contracts 392 17.4.2.2 Crowdfunding Token Trading 393 17.4.3 Digital Transfer System 393 17.5 Conclusion 395 References 395 18 Role of Start-Ups in Altering Agrimarket Channel (Input-Output) 399 D. Rafi and Md. Mubeena 18.1 Introduction 400 18.2 Agriculture Supply Chain Management 400 18.3 How Start-Ups Fill the Concerns and Gaps in Agri Input Supply Chain? 402 18.4 Output Supply Chain 404 18.5 How Start-Ups are Filling the Concerns and Gaps in Agri Output Supply Chain? 407 18.6 Conclusion 408 References 409 19 Development of Blockchain Agriculture Supply Chain Framework Using Social Network Theory: An Empirical Evidence Based on Malaysian Agriculture Firms 411 Muhammad Shabir Shaharudin, Yudi Fernando, Yuvaraj Ganesan and Faizah Shahudin 19.1 Introduction 412 19.2 Literature Review 413 19.2.1 Agriculture Malaysia 413 19.2.2 Agriculture Supply Chain 415 19.2.3 Blockchain Technology 416 19.2.4 Blockchain Agriculture Supply Chain Management 418 19.2.5 Social Network Theory 419 19.2.6 Social Network Analysis 420 19.3 Methodology 421 19.3.1 Blockchain Agriculture Supply Chain Management Framework 421 19.3.2 Research Design 423 19.4 Results and Discussion 424 19.4.1 Demographic Profiles 424 19.4.2 Social Network Analysis Results 424 19.5 Conclusion 440 19.6 Acknowledgment 441 References 441 20 Potential Options and Applications of Machine Learning in Soil Science 447 Anandkumar Naorem, Shiva Kumar Udayana and Somasundaram Jayaraman 20.1 Introduction: A Deep Insight on Machine Learning, Deep Learning and Artificial Intelligence 448 20.2 Application of ML in Soil Science 449 20.3 Classification of ML Techniques 452 20.3.1 Supervised ML 453 20.3.2 Unsupervised ML 453 20.3.3 Reinforcement ML 453 20.4 Artificial Neural Network 454 20.5 Support Vector Machine 455 20.6 Conclusion 457 References 457 Index 461

Autorenportrait

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.

Back cover copy

The book integrates computational intelligence, applied artificial intelligence, and modern agricultural practices and will appeal to scientists, agriculturists, and those in plant and crop science management. There is a need for synergy between the application of modern scientific innovation in the area of artificial intelligence and agriculture, considering the major challenges from climate change consequences viz. rising temperatures, erratic rainfall patterns, the emergence of new crop pests, drought, flood, etc. This volume reports on high-quality research (theory and practice including prototype & conceptualization of ideas, frameworks, real-world applications, policy, standards, psychological concerns, case studies, and critical surveys) on recent advances toward the realization of the digital agriculture revolution as a result of the convergence of different disruptive technologies. The book touches upon the following topics which have contributed to revolutionizing agricultural practices. <ul><li>Applications of Artificial Intelligence in Agriculture (AI models and architectures, system design, real-world applications of AI, machine learning and deep learning in the agriculture domain, integration & coordination of systems and issues & challenges).</li> <li>IoT and Big Data Analytics Applications in Agriculture (theory & architecture and the use of various types of sensors in optimizing agriculture resources and final product, benefits in real-time for crop acreage estimation, monitoring & control of agricultural produce).</li> <li>Robotics & Automation in Agriculture Systems (Automation challenges, need and recent developments and real case studies).</li> <li>Intelligent and Innovative Smart Agriculture Applications (use of hybrid intelligence in better crop health and management).</li> <li>Privacy, Security, and Trust in Digital Agriculture (government framework & policy papers). </li> <li>Open Problems, Challenges, and Future Trends.</li></ul> Audience Researchers in computer science, artificial intelligence, electronics engineering, agriculture automation, crop management, and science.