Buch, Englisch, 352 Seiten, Gewicht: 726 g
Blockchain, AI and Advanced Innovations in Agriculture
Buch, Englisch, 352 Seiten, Gewicht: 726 g
ISBN: 978-1-394-31060-9
Verlag: Wiley
Cultivate a more profitable and sustainable future for your agricultural operations with this essential book, which provides expert insights and real-world examples of how blockchain technology can revolutionize food safety, supply chain transparency, and market access for farmers globally.
As global populations grow and environmental concerns rise, agriculture faces the dual challenges of increasing productivity and sustainability. Blockchain technology offers innovative solutions to these challenges by enhancing traceability, efficiency, and transparency in agricultural processes. This book delves into how blockchain can revolutionize various aspects of agriculture, from supply chain management to farm operations and market access. It addresses critical topics such as improving food safety through real-time traceability of produce from farm to fork, reducing fraud by securely recording transactions, and facilitating fair trade practices by providing transparent access to information across the value chain. The book also examines the economic implications of blockchain in agriculture, highlighting how this technology can help reduce costs, increase profitability, and provide small-scale farmers with better access to global markets. Additionally, it discusses the role of smart contracts in automating agricultural agreements and payments, reducing the need for intermediaries and enhancing the efficiency of operations. By focusing on practical applications and forward-looking innovations, this book aims to inform and inspire stakeholders in the agricultural sector to embrace blockchain technologies. Through a blend of expert insights and real-world examples, it paints a vivid picture of how blockchain can cultivate a more efficient, transparent, and sustainable future for agriculture.
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
- Wirtschaftswissenschaften Wirtschaftssektoren & Branchen Primärer Sektor Agrarökonomie, Ernährungswirtschaft
- Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Lebensmitteltechnologie und Getränketechnologie
Weitere Infos & Material
Preface xiii
Part I: Blockchain Innovations in Agricultural Practices 1
1 Agriculture Meets Blockchain for Crop Monitoring and Prediction Using Machine Learning Techniques 3
D. Kavitha, Merin Varghese and Parth Vadera
1.1 Introduction 4
1.2 Related Works 5
1.3 Dataset 15
1.4 Data Analysis 15
1.5 Methodology 18
1.6 Architecture Diagram 19
1.7 Results and Discussions 21
1.8 Conclusion 25
References 25
2 Role of Machine Learning in Blockchain for Predictive Analysis 29
Dhivya Bharathi M., Leninisha Shanmugam and M. Sandhya
2.1 Introduction 30
2.2 Related Research 32
2.3 Existing System 33
2.4 System Hardware 34
2.5 Project Analysis 35
2.6 Problem Statement 35
2.7 Proposed Framework 36
2.8 IoT-ML Predictive Analysis 37
2.9 Blockchain Technology with IoT and Machine Learning 38
2.10 Benefits of Blockchain in Predictive Analysis 38
2.11 Artificial Intelligence and IoT in Smart Farming 38
2.12 Result Analysis 41
2.13 Conclusion 41
2.14 Future Work 42
References 42
3 Agriculture Manure Data Analysis Using Real-Time Cryptocurrency 45
Parvathi R., Pattabiraman V. and Xiaohui Yuan
3.1 Introduction 46
3.1.1 Motivation 47
3.1.2 Objective 48
3.2 Review of Literature 49
3.3 Materials and Methods 52
3.3.1 Dataset Collection and Description 52
3.3.2 Data Analysis 55
3.3.3 Information About Models 57
3.3.3.1 Model Planning 57
3.3.4 Model Building 58
3.3.5 Architecture Diagram and Explanation 60
3.4 Proposed Work 61
3.4.1 Research Gap and Novelty 62
3.5 Results and Discussion 63
3.5.1 Results and Explanation 63
3.5.2 Visualization 64
3.6 Conclusion 72
References 74
4 Future Agricultural Landscape Development: A MADM Model for Analysis 77
Ramakrishna Regulagadda, Syed Ziaur Rahman, Nallamala Sri Hari, Valeti Nagarjuna, Kolliboyina Hari and Sivudu Macherla
4.1 Introduction 78
4.2 Related Works 80
4.3 Multi-Attribute Decision Making (MADM) 81
4.4 Model Description 83
4.5 Implementing a Simulator of Alternative Futures 85
4.6 Conclusions 94
References 95
5 Cultivating Connectivity: Bridging Communities Through Farm Management Systems 99
Leninisha S., Riya Bansal V., Sai Lakshana S. and Krijay M.
5.1 Introduction 99
5.2 Literature Survey 101
5.3 Proposed System 102
5.4 System Design 105
5.5 Conclusion 106
5.6 Future Work 106
Bibliography 107
Part II: Blockchain in Agricultural Supply Chain and Traceability 109
6 Comprehensive Review of Blockchain-Oriented Methods in Agricultural Supply Chain Management 111
Pandiyaraju V., Thangaramya K., Kannan A. and Nikhil Nair
6.1 Introduction 112
6.1.1 Challenges in Agriculture Data Maintenance 113
6.1.1.1 Land Availability Data 114
6.1.1.2 Seed Problems 114
6.1.1.3 Usage of Fertilizer 114
6.1.1.4 Soil Erosion 114
6.1.1.5 Instability 115
6.1.1.6 Water Quality 115
6.1.1.7 Pest Management 115
6.1.1.8 Production Methods 116
6.1.1.9 Cropping Pattern 116
6.1.2 Agriculture Supply Chain 116
6.1.3 Need for Survey on Blockchain-Based Agricultural Supply Chain Management 118
6.2 Existing Works on Agricultural Supply Chain Management Using Blockchain 118
6.2.1 Works on Technology in Agriculture 119
6.2.2 Works on Artificial Intelligence in Agriculture 119
6.2.3 Works on Agricultural Supply Chain Management 120
6.2.4 Works on Use of Blockchain in Agriculture Supply Chain Management 121
6.2.5 Works on Blockchain Security Methods for Agricultural Data Maintenance 121
6.3 Proposed Work 122
6.3.1 DHASH Algorithm 122
6.3.2 Rule-Based Two-Phase Commit Protocol 123
6.4 Results and Discussions 123
6.5 Conclusions 126
References 126
7 Revolutionizing Agricultural Supply Chains with Blockchain for Enhancing Transparency, Efficiency, and Traceability 131
Arun Kumar Sivaraman, Rajiv Vincent, Janakiraman Nithiyanantham, Thirumurugan Shanmugam, Kong Fah Tee and Ajmery Sultana
7.1 Introduction 132
7.2 Understanding Blockchain Technology 136
7.3 Enhancing Transparency in Agricultural Supply Chains 140
7.4 Improving Efficiency in Agricultural Supply Chains 143
7.5 Enhancing Traceability in Agricultural Supply Chains 145
7.6 Real-World Applications of Blockchain in Agricultural Supply Chains 148
7.7 Challenges and Considerations for Blockchain Adoption 150
7.8 Future Trends and Developments 151
7.9 Conclusion 152
References 153
8 Cultivating Trust: How Blockchain is Reshaping Agriculture’s Supply Chain Landscape 155
Kalyanasundaram V., Keerthi A.J. and G. Prethija
8.1 Introduction to Blockchain’s Impact on Agriculture Supply Chains 156
8.1.1 The Role of Blockchain in Modern Agriculture 156
8.1.2 Key Challenges in Agricultural Supply Chains 157
8.1.3 Opportunities for Innovation 157
8.2 Enhancing Traceability with Blockchain 158
8.2.1 Recording Seed Origins, Cultivation Practices, and Harvest Yields 158
8.2.2 Real-Time Product Tracking Across Supply Chains 159
8.2.3 Building Consumer Trust through Transparency 160
8.3 Empowering Farmers and Communities 161
8.3.1 Blockchain for Financial Inclusion 161
8.3.1.1 Security through Advanced Encryption 162
8.3.1.2 Immutable Records for Transparency 162
8.3.2 Peer-to-Peer Lending and Crowdfunding Platforms 162
8.3.2.1 Direct Access to Capital 163
8.3.2.2 Enhanced Security 163
8.3.2.3 Consensus Mechanisms for Trust 163
8.3.3 Promoting Sustainable Agricultural Practices 163
8.3.3.1 Traceability in the Supply Chain 164
8.3.3.2 Incentivizing Sustainable Practices 164
8.3.3.3 Zero-Knowledge Proofs for Privacy 165
8.3.3.4 Integrating Technology for Sustainable Growth 166
8.4 Decentralized Transactions and Smart Contracts 167
8.4.1 Overview of Blockchain-Based Transaction Mechanisms 167
8.4.2 Ganache: A Local Blockchain Platform for Agriculture 168
8.5 Blockchain’s Role in Quality Assurance and Market Access 172
8.5.1 Combatting Counterfeit Products and Fraud 172
8.5.2 Ensuring Product Quality through Secure Records 176
8.6 Future Perspectives and Innovations 177
8.6.1 Integrating Blockchain with IoT and AI in Agriculture 177
8.6.1.1 Blockchain and IoT 178
8.6.1.2 Blockchain and AI 178
8.6.2 Challenges in Scaling Blockchain Solutions 179
8.6.2.1 Adoption Barriers 179
8.6.3 Policies and Frameworks for Widespread Adoption 179
8.6.3.1 Standardization and Certification 180
References 181
9 Deep Learning-Based Supply-Chain Re-Traceability of Tea Leaves in a Permissioned Blockchain 183
Sandhya P., Ganesan R., Kalyanasundaram V., R. Srivats and Amogh Singh
9.1 Introduction 184
9.2 Literature Review 189
9.3 Proposed System 192
9.3.1 Architecture 192
9.3.2 Working 195
9.3.3 Participants 197
9.3.4 Operations 199
9.3.5 Advantages and Benefits 200
9.4 Results/Discussion 201
9.5 Conclusion 202
9.6 Future Work 203
References 204
10 Prohibition of Illegal Movement of Sandalwood from Reserve Forests through Retracing Supply Chain on a Permissioned Blockchain 207
Sandhya P., Ganesan R., Rama Parvathy L., R. Srivats, Kalyanasundaram V. and Amogh Singh
10.1 Introduction 208
10.2 Literature Review 212
10.3 Proposed System 215
10.3.1 Architecture 215
10.3.2 Working 219
10.3.3 Participants 222
10.3.4 Operations 224
10.3.5 Advantages and Benefits 226
10.4 Results/Discussion 227
10.5 Conclusion 231
10.6 Future Work 232
References 233
Part III: Advanced Technologies in Smart Agriculture 235
11 Enhanced Food Calorie Estimation: Multi-Layer Perceptron Versus K-Nearest Neighbors 237
Affan S.K. and Muneeshwari P.
Introduction 237
Materials and Methods 241
Research Environment 241
Sample Size Calculation 241
Implementation Framework 241
Programming and Dataset 242
Novel Enhanced Multi-Layer Perceptron Algorithm 242
K-Nearest Neighbor Algorithm 242
Statistical Analysis 243
Results 243
Discussion 246
Conclusion 246
References 247
12 Accuracy Comparison of Enhanced Multi-Layer Perceptron and Polynomial Regression in Food Calorie Measurement 249
Affan S.K. and Muneeshwari P.
Introduction 250
Materials and Methods 254
Novel Enhanced Multi-Layer Perceptron 254
Polynomial Regression 255
Statistical Analysis 255
Results 255
Conclusion 258
References 259
13 Effective Recommendation of Nutritious Food Using Random Forest Classifier in Comparison with Multi-Layer Perceptron Classifier Algorithm 263
J. Rishi Kannan and N. Bharatha Devi
Introduction 264
Materials and Methods 265
Study Design and Sample Selection 266
Tools and Technologies 266
Implementation of Novel Random Forest and MLP Classifiers 266
Novel Random Forest Classifier 267
Multi-Layer Perceptron Classifier 267
Statistical Analysis 268
Results and Discussion 268
Conclusion 271
References 272
14 Smart Pest Identification in Agriculture: Leveraging CNN Classifier Over SVM for Leaf Health Analysis 275
Bobbilla Ramya Sri and V. Karthick
Introduction 276
Materials and Methods 276
Support Vector Machine (SVM) Classifier Algorithm 277
Convolutional Neural Network (CNN) Classifier Algorithm 278
Statistical Analysis 278
Results 278
Discussion 281
Conclusion 282
References 282
15 Role of Artificial Intelligence in Weed Detection and Prevention 285
K. Arunkumar, S. Leninisha and M. Sandhya
Introduction 286
Various Methods of Weed Control 287
Introduction to UAV 291
Sensors and Their Usage 293
Dataset 293
Data Augmentation 295
Evaluation Parameters 296
Machine Learning 296
Deep Learning 304
Convolutional Neural Network 304
VGG Net Model 309
Inception and ResNet Module 309
DenseNet 311
Yolo 311
Blockchain 311
Discussions and Conclusion 312
Bibliography 313
About the Editors 325
Index 327
