| Description |
1 online resource (200 pages) |
| Series |
River Publishers series in computing and information science and technology |
|
River Publishers series in computing and information science and technology
|
| Contents |
Preface xv List of Contributors xvii List of Figures xxi List of Tables xxv List of Abbreviations xxvii 1 Introduction to Smart Agriculture 1 1.1 Introduction to Agriculture Process 2 1.1.1 Soil preparation 2 1.1.2 Sowing 2 1.1.3 Manuring 4 1.1.4 Irrigation 4 1.1.5 Weeding 4 1.1.6 Harvesting 5 1.1.7 Storage 6 1.2 Role of Smart Agriculture in Soil Preparation 6 1.2.1 Activities of IoT sensors 6 1.3 Role of IoT Devices in Smart Agriculture 12 1.3.1 Robotics in agriculture 13 1.3.2 Drones in agriculture 15 1.3.3 Remote sensing sensors in agriculture 15 1.3.4 Computer imaging in agriculture 16 1.4 Role of Irrigation in Smart Agriculture 17 1.4.1 Soil sensors 17 1.4.2 Weather sensors 17 1.4.3 Plant sensors 18 1.4.4 Capabilities of a smart irrigation solution 18 1.4.5 Technology used in smart irrigation system 18 1.5 Role of Harvesting in Smart Agriculture 20 1.6 Advantages of Smart Agriculture 20 1.7 Challenges in Smart Agriculture 21 1.7.1 Hardware 21 1.7.2 Brain 21 1.7.3 Maintenance 22 1.7.4 Mobility 22 1.7.5 Infrastructure 22 1.7.6 Connectivity 22 1.7.7 Data gathering interval 22 1.7.8 Data security in the agriculture industry 23 1.8 Limitations of Smart Agriculture 23 1.9 Future Trends in Smart Agriculture 24 1.9.1 The Internet of food things (IoFT) 24 1.9.2 Advanced green revolution 24 1.9.3 Biologically edited crops 25 1.9.4 The robot farmers 25 1.9.5 The automated greenhouse effect technology 25 1.9.6 Climate smart agriculture 29 1.10 Conclusion 29 References 29 2 Modern Agriculture Farming: Rack and Pinion Mechanism-based Remote Controlled Seed Sowing Robot 31 2.1 Introduction 32 2.1.1 Traditional sowing method 33 2.1.2 Problem statement 33 2.1.3 Objectives 33 2.1.4 Literature survey 35 2.2 Proposed Work 37 2.2.1 Block diagram 37 2.2.2 Design of robot 38 2.2.3 Seed sowing mechanism 39 2.2.4 Dispensing of seeds 39 2.2.5 TIVA C series 40 2.2.6 BoosterPacks 40 2.2.7 Software used: ENERGIA integrated development environment (IDE) 41 2.2.8 Components used 42 2.2.9 Performance analysis 47 2.3 Conclusion 51 Acknowledgements 52 References 53 3 Crop Management System 55 3.1 Introduction 56 3.2 Insights of Deep Learning 58 3.3 Crop Management System: A Deep Learning Approach 60 3.3.1 Soil health monitoring system 61 3.3.2 Sorting of seeds based on deep learning 62 3.3.3 Seed sowing using deep learning 63 3.3.4 Smart irrigation system using deep learning 63 3.3.5 Crop growth recognition using deep learning 64 3.3.6 Fertilizer estimation using deep learning 65 3.3.7 Crop harvesting using deep learning 66 3.3.8 Crop recommendation system using deep learning 66 3.4 Scope and Challenges in Crop Management System 69 3.4.1 Role of artificial intelligence in smart farming 69 3.4.2 Farmers challenges in adopting new technologies 69 3.4.3 Challenges in deep learning 70 3.4.4 Recent deep learning algorithms in smart farming 72 3.5 Conclusion and Future Scope 73 References 75 4 Autonomous Devices in Smart Farming 81 4.1 Introduction 83 4.2 Related Works 85 4.3 Proposed Methodology 93 4.4 Results and Discussion 97 4.5 Conclusion 99 References 101 5 Predictive Analysis in Smart Agriculture 105 5.1 Introduction 106 5.2 Big Data in Agriculture 107 5.2.1 Big data’s impact on agriculture 107 5.2.2 Sources and methods for big data 112 5.2.3 Methods and software for agricultural big data analysis 114 5.3 Case Studies of Big Data Analytical Methods in Agriculture 116 5.3.1 Case study 1 116 5.3.2 Case study 2 118 5.4 Agricultural Big Data Analytics: Related Research Fields 121 5.4.1 Open problems 122 5.4.2 Restrictions on big data analysis methods in agriculture 122 5.4.3 Resolving complex issues and overcoming obstacles 123 5.4.4 Potential use of big data analysis in agriculture 124 5.5 Conclusions 125 References 126 6 Machine Learning in Smart Agriculture 129 6.1 Introduction 130 6.2 Start of Agriculture 132 6.2.1 Technological advancement 133 6.2.2 Benefits of smart agriculture 139 6.3 Machine Learning 139 6.3.1 Approaches of machine learning 140 6.3.2 Machine learning in agriculture 142 6.4 Applications of ML in Smart Agriculture 142 6.4.1 Plant breeding 143 6.4.2 Species management 143 6.4.3 Field conditions management 144 6.4.4 Crop management 145 6.4.5 Disease management 145 6.4.6 Livestock management 147 6.4.7 Ranching 148 6.4.8 Agrochemical production and application 149 6.4.9 Remote weather monitoring 149 6.4.10 Farmer’s little helper 150 6.5 Conclusion 150 References 150 7 Deep Learning in Smart Agriculture 153 7.1 Introduction 154 7.1.1 Applications of DL algorithms 154 7.2 Deep Learning Algorithm 156 7.2.1 Convolutional neural networks 156 7.2.2 Long short-term memory networks 157 7.2.3 Recurrent neural networks 157 7.2.4 Generative adversarial networks 158 7.2.5 Radial basis function networks 159 7.2.6 Multi-layer perceptrons 160 7.2.7 Self-organizing maps (SOMs) 160 7.2.8 Deep belief networks 161 7.2.9 Restricted Boltzmann machines 162 7.3 DL Applications in Agriculture 163 7.4 DL Information Retrieval Methods 165 7.5 Classes of DL Networks 166 7.6 Deep Autoencoders 168 7.7 Information based on Images 170 7.8 DL Frameworks 170 7.9 Land Cover Classification (LCC) Methods 171 7.10 Conclusions and Future Works 172 References 173 8 Image Analysis for Better Yield in Farming 177 8.1 Introduction 177 8.2 Image Processing of Crop Yield 180 8.2.1 Crop yield detection 181 8.2.2 Special features 181 8.2.3 Smart farming with autonomous movers 183 8.2.4 Applications of image processing 184 8.2.5 Multi-storied cropping system 185 8.2.6 Tools to evaluate the performance of multi-story cropping system 188 8.2.7 Reason for the need of multi-story cropping system in India 188 8.2.8 The multi-story garden farming 190 8.2.9 Challenges of multi-story cropping 191 8.3 Conclusion 191 References 191 9 Precision Farming for Crop Prediction 195 9.1 Introduction 197 9.2 Literature Review 199 9.3 Proposed Methodology 206 9.4 Results and Discussion 210 9.5 Conclusion 211 References 215 10 Decision-Making Support in Smart Farming 219 10.1 Introduction 219 10.2 Related Works 222 10.3 System Design and Implementation 223 10.4 Execution Outputs 230 10.5 Conclusion 234 References 236 11 Indigenous Knowledge in Smart Agriculture 241 11.1 Introduction 242 11.1.1 Indigenous knowledge is found to be: 242 11.1.2 Why is indigenous knowledge needed for sustainable agriculture? 243 11.1.3 Major IK practices for sustainable agriculture 244 11.1.4 Smart agriculture using WSN and IoT 246 11.1.5 Applications of IoT in smart farming with the adoption of IoT 247 11.1.6 Implementation challenges of IoT in smart farming 249 11.1.7 Role of machine learning in smart agriculture 252 11.2 Conclusion 255 References 255 12 Climate Change and Its Impact on Agriculture 259 12.1 Introduction 260 12.2 Global Scenario and Evolving Context 260 12.2.1 Climate change 260 12.2.2 Agriculture 262 12.2.3 Impact of agriculture on climate change 263 12.3 An Overview of the Indian Scenario 265 12.3.1 Climate change in India 265 12.3.2 Agriculture in India 265 12.4 Impact of Climate Change 266 12.4.1 On agriculture land 266 12.4.2 Effects on crops, water, livestock, fisheries, and pest diseases 267 12.5 Technology Used to Overcome Problems in Farming 268 12.5.1 Overview of IoT 269 12.5.2 IoT’s importance and benefits to businesses 269 12.5.3 Internet of Things (IoT) in farming 270 12.5.4 Application of IoT and WSN in farming 271 12.5.5 IoT technologies in predicting climate change 274 12.6 Initiatives Measured by the Cultivators 276 12.6.1 Adaptation to climate change in agriculture 276 12.6.2 Farmer’s predictions and adaptation to technology 278 12.7 Conclusion 279 Acknowledgement 280 References 280 13 Cropping Pattern in Farming 283 13.1 Introduction 284 13.2 Overview of Cropping Patterns 285 13.3 Types of Cropping Pattern 286 13.3.1 Monocropping 286 13.3.2 Mixed cropping 287 13.3.3 Intercropping 287 13.4 Factors Affecting Cropping Patterns 289 13.5 Crop Production and Management 291 13.5.1 Soil preparation 291 13.5.2 Sowing 292 13.5.3 Incorporating manure and fertilizers 293 13.5.4 Irrigation 293 13.5.5 Weeds protection 294 13.5.6 Harvesting 294 13.5.7 Storage 295 13.6 Modern Agriculture Technologies 295 13.6.1 Semi-automatic robots 295 13.6.2 Drones 295 13.6.3 IoT-based remote sensing 296 13.6.4 Computer imaging 296 13.7 Benefits of Implementing the Smart Solution in Farms 297 13.8 Conclusion 298 References 298 14 Crop Welfare and Security to Farmers 301 14.1 Introduction 302 14.2 Making of Soil Management to Increase Yields 305 14.3 Image Processing in Farming 307 14.4 IoT and AI Usage in Farming 310 14.5 Conclusions 317 References 317 15 Urban Farming: Case Study 321 15.1 Introduction 321 15.2 Smart Urban Farming System Configuration 323 15.2.1 An optimal solution to monitor smart farming conditions using IoT 326 15.3 IoT in Smart Farming 326 15.3.1 Benefits of smart farming 327 15.3.2 Shortfalls of smart farming 327 15.3.3 Components used in smart farming 327 15.4 Design Concept to Control and Monitor Greenhouse Temperature by an Intelligent IoT-based System 328 15.4.1 Big data 328 15.4.2 Security 330 15.5 Overview of Indian Smart Agriculture by IoT 330 15.5.1 Methodologies 332 15.5.2 Components and services 333 15.6 Conclusion 334 References 335 16 IoT: Applications and Case Study in Smart Farming 339 16.1 Introduction 340 16.2 Background Study 341 16.3 Applications and Use Cases 342 16.3.1 Role of drones in agricultural field 342 16.3.2 Predictive analytics for smart farming, such as crop harvesting time, the risks of diseases and infestations, and yield volume 343 16.4 Digital Twins 344 16.4.1 Product life cycle phases 344 16.4.2 Virtual control of farming enabled by digital twins 345 16.4.3 Basic control model 345 16.4.4 Conceptual model based on digital twins 346 16.4.5 Simplified con .. |
| Summary |
This book brings new smart farming methodologies to the forefront, sparked by pervasive applications with automated farming technology. New indigenous expertise on smart agricultural technologies is presented along with conceptual prototypes showing how the Internet of Things, cloud computing, machine learning, deep learning, precision farming, crop management systems, etc., will be used in large-scale production in the future. The necessity of available welfare systems for farmers’ well-being is also discussed in the book. It draws the conclusion that there is a greater need and demand today for smart farming methodologies driven by technology than ever before |
| Notes |
Description based on print version record |
| Subject |
Agriculture -- Data processing.
|
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Artificial intelligence -- Agricultural applications.
|
|
TECHNOLOGY / Agriculture / Crop Science
|
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Agriculture -- Data processing.
|
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Artificial intelligence -- Agricultural applications.
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| Form |
Electronic book
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| Author |
Kalaiselvi, K., editor
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Anand, A. Jose, editor
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Tanwar, Poonam, 1979- editor.
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Raza, Haider, editor
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| ISBN |
9788770228831 |
|
8770228833 |
|
1003810403 |
|
9781003810407 |
|
9781032628745 |
|
103262874X |
|
9781003810445 |
|
1003810446 |
|
