Harvesting Data

Narayanan Ganesh, PhD is a Senior Associate Professor at the Vellore Institute of Technology’s Chennai Campus with nearly two decades of experience in teaching, training, and research. He has published more than 30 articles, written eight textbooks, and filed two Australian patents. His research encompasses a range areas, including software engineering, agile software development, prediction and optimization techniques, deep learning, image processing, and data analytics. Kanak Kalita, PhD is an Associate Professor in the Department of Mechanical Engineering at Vel Tech University with more than ten years of experience. He has authored more than 200 articles and edited more than eight book volumes. His research interests encompass machine learning, fuzzy decision making, metamodeling, process optimization, and composites.

• Preface xiiiPart I: Blockchain Innovations in Agricultural Practices 11 Agriculture Meets Blockchain for Crop Monitoring and Prediction Using Machine Learning Techniques 3D. Kavitha, Merin Varghese and Parth Vadera1.1 Introduction 41.2 Related Works 51.3 Dataset 151.4 Data Analysis 151.5 Methodology 181.6 Architecture Diagram 191.7 Results and Discussions 211.8 Conclusion 25References 252 Role of Machine Learning in Blockchain for Predictive Analysis 29Dhivya Bharathi M., Leninisha Shanmugam and M. Sandhya2.1 Introduction 302.2 Related Research 322.3 Existing System 332.4 System Hardware 342.5 Project Analysis 352.6 Problem Statement 352.7 Proposed Framework 362.8 IoT-ML Predictive Analysis 372.9 Blockchain Technology with IoT and Machine Learning 382.10 Benefits of Blockchain in Predictive Analysis 382.11 Artificial Intelligence and IoT in Smart Farming 382.12 Result Analysis 412.13 Conclusion 412.14 Future Work 42References 423 Agriculture Manure Data Analysis Using Real-Time Cryptocurrency 45Parvathi R., Pattabiraman V. and Xiaohui Yuan3.1 Introduction 463.1.1 Motivation 473.1.2 Objective 483.2 Review of Literature 493.3 Materials and Methods 523.3.1 Dataset Collection and Description 523.3.2 Data Analysis 553.3.3 Information About Models 573.3.3.1 Model Planning 573.3.4 Model Building 583.3.5 Architecture Diagram and Explanation 603.4 Proposed Work 613.4.1 Research Gap and Novelty 623.5 Results and Discussion 633.5.1 Results and Explanation 633.5.2 Visualization 643.6 Conclusion 72References 744 Future Agricultural Landscape Development: A MADM Model for Analysis 77Ramakrishna Regulagadda, Syed Ziaur Rahman, Nallamala Sri Hari, Valeti Nagarjuna, Kolliboyina Hari and Sivudu Macherla4.1 Introduction 784.2 Related Works 804.3 Multi-Attribute Decision Making (MADM) 814.4 Model Description 834.5 Implementing a Simulator of Alternative Futures 854.6 Conclusions 94References 955 Cultivating Connectivity: Bridging Communities Through Farm Management Systems 99Leninisha S., Riya Bansal V., Sai Lakshana S. and Krijay M.5.1 Introduction 995.2 Literature Survey 1015.3 Proposed System 1025.4 System Design 1055.5 Conclusion 1065.6 Future Work 106Bibliography 107Part II: Blockchain in Agricultural Supply Chain and Traceability 1096 Comprehensive Review of Blockchain-Oriented Methods in Agricultural Supply Chain Management 111Pandiyaraju V., Thangaramya K., Kannan A. and Nikhil Nair6.1 Introduction 1126.1.1 Challenges in Agriculture Data Maintenance 1136.1.1.1 Land Availability Data 1146.1.1.2 Seed Problems 1146.1.1.3 Usage of Fertilizer 1146.1.1.4 Soil Erosion 1146.1.1.5 Instability 1156.1.1.6 Water Quality 1156.1.1.7 Pest Management 1156.1.1.8 Production Methods 1166.1.1.9 Cropping Pattern 1166.1.2 Agriculture Supply Chain 1166.1.3 Need for Survey on Blockchain-Based Agricultural Supply Chain Management 1186.2 Existing Works on Agricultural Supply Chain Management Using Blockchain 1186.2.1 Works on Technology in Agriculture 1196.2.2 Works on Artificial Intelligence in Agriculture 1196.2.3 Works on Agricultural Supply Chain Management 1206.2.4 Works on Use of Blockchain in Agriculture Supply Chain Management 1216.2.5 Works on Blockchain Security Methods for Agricultural Data Maintenance 1216.3 Proposed Work 1226.3.1 DHASH Algorithm 1226.3.2 Rule-Based Two-Phase Commit Protocol 1236.4 Results and Discussions 1236.5 Conclusions 126References 1267 Revolutionizing Agricultural Supply Chains with Blockchain for Enhancing Transparency, Efficiency, and Traceability 131Arun Kumar Sivaraman, Rajiv Vincent, Janakiraman Nithiyanantham, Thirumurugan Shanmugam, Kong Fah Tee and Ajmery Sultana7.1 Introduction 1327.2 Understanding Blockchain Technology 1367.3 Enhancing Transparency in Agricultural Supply Chains 1407.4 Improving Efficiency in Agricultural Supply Chains 1437.5 Enhancing Traceability in Agricultural Supply Chains 1457.6 Real-World Applications of Blockchain in Agricultural Supply Chains 1487.7 Challenges and Considerations for Blockchain Adoption 1507.8 Future Trends and Developments 1517.9 Conclusion 152References 1538 Cultivating Trust: How Blockchain is Reshaping Agriculture’s Supply Chain Landscape 155Kalyanasundaram V., Keerthi A.J. and G. Prethija8.1 Introduction to Blockchain’s Impact on Agriculture Supply Chains 1568.1.1 The Role of Blockchain in Modern Agriculture 1568.1.2 Key Challenges in Agricultural Supply Chains 1578.1.3 Opportunities for Innovation 1578.2 Enhancing Traceability with Blockchain 1588.2.1 Recording Seed Origins, Cultivation Practices, and Harvest Yields 1588.2.2 Real-Time Product Tracking Across Supply Chains 1598.2.3 Building Consumer Trust through Transparency 1608.3 Empowering Farmers and Communities 1618.3.1 Blockchain for Financial Inclusion 1618.3.1.1 Security through Advanced Encryption 1628.3.1.2 Immutable Records for Transparency 1628.3.2 Peer-to-Peer Lending and Crowdfunding Platforms 1628.3.2.1 Direct Access to Capital 1638.3.2.2 Enhanced Security 1638.3.2.3 Consensus Mechanisms for Trust 1638.3.3 Promoting Sustainable Agricultural Practices 1638.3.3.1 Traceability in the Supply Chain 1648.3.3.2 Incentivizing Sustainable Practices 1648.3.3.3 Zero-Knowledge Proofs for Privacy 1658.3.3.4 Integrating Technology for Sustainable Growth 1668.4 Decentralized Transactions and Smart Contracts 1678.4.1 Overview of Blockchain-Based Transaction Mechanisms 1678.4.2 Ganache: A Local Blockchain Platform for Agriculture 1688.5 Blockchain’s Role in Quality Assurance and Market Access 1728.5.1 Combatting Counterfeit Products and Fraud 1728.5.2 Ensuring Product Quality through Secure Records 1768.6 Future Perspectives and Innovations 1778.6.1 Integrating Blockchain with IoT and AI in Agriculture 1778.6.1.1 Blockchain and IoT 1788.6.1.2 Blockchain and AI 1788.6.2 Challenges in Scaling Blockchain Solutions 1798.6.2.1 Adoption Barriers 1798.6.3 Policies and Frameworks for Widespread Adoption 1798.6.3.1 Standardization and Certification 180References 1819 Deep Learning-Based Supply-Chain Re-Traceability of Tea Leaves in a Permissioned Blockchain 183Sandhya P., Ganesan R., Kalyanasundaram V., R. Srivats and Amogh Singh9.1 Introduction 1849.2 Literature Review 1899.3 Proposed System 1929.3.1 Architecture 1929.3.2 Working 1959.3.3 Participants 1979.3.4 Operations 1999.3.5 Advantages and Benefits 2009.4 Results/Discussion 2019.5 Conclusion 2029.6 Future Work 203References 20410 Prohibition of Illegal Movement of Sandalwood from Reserve Forests through Retracing Supply Chain on a Permissioned Blockchain 207Sandhya P., Ganesan R., Rama Parvathy L., R. Srivats, Kalyanasundaram V. and Amogh Singh10.1 Introduction 20810.2 Literature Review 21210.3 Proposed System 21510.3.1 Architecture 21510.3.2 Working 21910.3.3 Participants 22210.3.4 Operations 22410.3.5 Advantages and Benefits 22610.4 Results/Discussion 22710.5 Conclusion 23110.6 Future Work 232References 233Part III: Advanced Technologies in Smart Agriculture 23511 Enhanced Food Calorie Estimation: Multi-Layer Perceptron Versus K-Nearest Neighbors 237Affan S.K. and Muneeshwari P.Introduction 237Materials and Methods 241Research Environment 241Sample Size Calculation 241Implementation Framework 241Programming and Dataset 242Novel Enhanced Multi-Layer Perceptron Algorithm 242K-Nearest Neighbor Algorithm 242Statistical Analysis 243Results 243Discussion 246Conclusion 246References 24712 Accuracy Comparison of Enhanced Multi-Layer Perceptron and Polynomial Regression in Food Calorie Measurement 249Affan S.K. and Muneeshwari P.Introduction 250Materials and Methods 254Novel Enhanced Multi-Layer Perceptron 254Polynomial Regression 255Statistical Analysis 255Results 255Conclusion 258References 25913 Effective Recommendation of Nutritious Food Using Random Forest Classifier in Comparison with Multi-Layer Perceptron Classifier Algorithm 263J. Rishi Kannan and N. Bharatha DeviIntroduction 264Materials and Methods 265Study Design and Sample Selection 266Tools and Technologies 266Implementation of Novel Random Forest and MLP Classifiers 266Novel Random Forest Classifier 267Multi-Layer Perceptron Classifier 267Statistical Analysis 268Results and Discussion 268Conclusion 271References 27214 Smart Pest Identification in Agriculture: Leveraging CNN Classifier Over SVM for Leaf Health Analysis 275Bobbilla Ramya Sri and V. KarthickIntroduction 276Materials and Methods 276Support Vector Machine (SVM) Classifier Algorithm 277Convolutional Neural Network (CNN) Classifier Algorithm 278Statistical Analysis 278Results 278Discussion 281Conclusion 282References 28215 Role of Artificial Intelligence in Weed Detection and Prevention 285K. Arunkumar, S. Leninisha and M. SandhyaIntroduction 286Various Methods of Weed Control 287Introduction to UAV 291Sensors and Their Usage 293Dataset 293Data Augmentation 295Evaluation Parameters 296Machine Learning 296Deep Learning 304Convolutional Neural Network 304VGG Net Model 309Inception and ResNet Module 309DenseNet 311Yolo 311Blockchain 311Discussions and Conclusion 312Bibliography 313About the Editors 325Index 327