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Submit a ProposalHarvesting Data
 | Blockchain, AI and Advanced Innovations in Agriculture Edited by Narayanan Ganesh and Kanak Kalita Copyright: 2026 | Expected Pub Date: 2026 ISBN: 9781394310609 | Hardcover | 346 pages Price: $225 USD  |
One Line Description
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.
Audience
Agricultural professionals, agribusiness executives, technology developers, academics, policymakers and government agencies, and sustainability advocates keen to explore the transformative impact of blockchain on agricultural practices.
Agricultural professionals, agribusiness executives, technology developers, academics, policymakers and government agencies, and sustainability advocates keen to explore the transformative impact of blockchain on agricultural practices.
Description
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.
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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.
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Author / Editor Details
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.
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.
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Table of Contents
Preface
Part I: Blockchain Innovations in Agricultural Practices
1. Agriculture Meets Blockchain for Crop Monitoring and Prediction Using Machine Learning Techniques
D. Kavitha, Merin Varghese and Parth Vadera
1.1 Introduction
1.2 Related Works
1.3 Dataset
1.4 Data Analysis
1.5 Methodology
1.6 Architecture Diagram
1.7 Results and Discussions
1.8 Conclusion
References
2. Role of Machine Learning in Blockchain for Predictive Analysis
Dhivya Bharathi M., Leninisha Shanmugam and M. Sandhya
2.1 Introduction
2.2 Related Research
2.3 Existing System
2.4 System Hardware
2.5 Project Analysis
2.6 Problem Statement
2.7 Proposed Framework
2.8 IoT-ML Predictive Analysis
2.9 Blockchain Technology with IoT and Machine Learning
2.10 Benefits of Blockchain in Predictive Analysis
2.11 Artificial Intelligence and IoT in Smart Farming
2.12 Result Analysis
2.13 Conclusion
2.14 Future Work
References
3. Agriculture Manure Data Analysis Using Real-Time Cryptocurrency
Parvathi R., Pattabiraman V. and Xiaohui Yuan
3.1 Introduction
3.1.1 Motivation
3.1.2 Objective
3.2 Review of Literature
3.3 Materials and Methods
3.3.1 Dataset Collection and Description
3.3.2 Data Analysis
3.3.3 Information About Models
3.3.3.1 Model Planning
3.3.4 Model Building
3.3.5 Architecture Diagram and Explanation
3.4 Proposed Work
3.4.1 Research Gap and Novelty
3.5 Results and Discussion
3.5.1 Results and Explanation
3.5.2 Visualization
3.6 Conclusion
References
4. Future Agricultural Landscape Development: A MADM Model for Analysis
Ramakrishna Regulagadda, Syed Ziaur Rahman, Nallamala Sri Hari, Valeti Nagarjuna, Kolliboyina Hari and Sivudu Macherla
4.1 Introduction
4.2 Related Works
4.3 Multi-Attribute Decision Making (MADM)
4.4 Model Description
4.5 Implementing a Simulator of Alternative Futures
4.6 Conclusions
References
5. Cultivating Connectivity: Bridging Communities Through Farm Management Systems
Leninisha S., Riya Bansal V., Sai Lakshana S. and Krijay M.
5.1 Introduction
5.2 Literature Survey
5.3 Proposed System
5.4 System Design
5.5 Conclusion
5.6 Future Work
Bibliography
Part II: Blockchain in Agricultural Supply Chain and Traceability
6. Comprehensive Review of Blockchain-Oriented Methods in Agricultural Supply Chain Management
Pandiyaraju V., Thangaramya K., Kannan A. and Nikhil Nair
6.1 Introduction
6.1.1 Challenges in Agriculture Data Maintenance
6.1.1.1 Land Availability Data
6.1.1.2 Seed Problems
6.1.1.3 Usage of Fertilizer
6.1.1.4 Soil Erosion
6.1.1.5 Instability
6.1.1.6 Water Quality
6.1.1.7 Pest Management
6.1.1.8 Production Methods
6.1.1.9 Cropping Pattern
6.1.2 Agriculture Supply Chain
6.1.3 Need for Survey on Blockchain-Based Agricultural Supply Chain Management
6.2 Existing Works on Agricultural Supply Chain Management Using Blockchain
6.2.1 Works on Technology in Agriculture
6.2.2 Works on Artificial Intelligence in Agriculture
6.2.3 Works on Agricultural Supply Chain Management
6.2.4 Works on Use of Blockchain in Agriculture Supply Chain Management
6.2.5 Works on Blockchain Security Methods for Agricultural Data Maintenance
6.3 Proposed Work
6.3.1 DHASH Algorithm
6.3.2 Rule-Based Two-Phase Commit Protocol
6.4 Results and Discussions
6.5 Conclusions
References
7. Revolutionizing Agricultural Supply Chains with Blockchain for Enhancing Transparency, Efficiency, and Traceability
Arun Kumar Sivaraman, Rajiv Vincent, Janakiraman Nithiyanantham, Thirumurugan Shanmugam, Kong Fah Tee and Ajmery Sultana
7.1 Introduction
7.2 Understanding Blockchain Technology
7.3 Enhancing Transparency in Agricultural Supply Chains
7.4 Improving Efficiency in Agricultural Supply Chains
7.5 Enhancing Traceability in Agricultural Supply Chains
7.6 Real-World Applications of Blockchain in Agricultural Supply Chains
7.7 Challenges and Considerations for Blockchain Adoption
7.8 Future Trends and Developments
7.9 Conclusion
References
8. Cultivating Trust: How Blockchain is Reshaping Agriculture’s Supply Chain Landscape
Kalyanasundaram V., Keerthi A.J. and G. Prethija
8.1 Introduction to Blockchain’s Impact on Agriculture Supply Chains
8.1.1 The Role of Blockchain in Modern Agriculture
8.1.2 Key Challenges in Agricultural Supply Chains
8.1.3 Opportunities for Innovation
8.2 Enhancing Traceability with Blockchain
8.2.1 Recording Seed Origins, Cultivation Practices, and Harvest Yields
8.2.2 Real-Time Product Tracking Across Supply Chains
8.2.3 Building Consumer Trust through Transparency
8.3 Empowering Farmers and Communities
8.3.1 Blockchain for Financial Inclusion
8.3.1.1 Security through Advanced Encryption
8.3.1.2 Immutable Records for Transparency
8.3.2 Peer-to-Peer Lending and Crowdfunding Platforms
8.3.2.1 Direct Access to Capital
8.3.2.2 Enhanced Security
8.3.2.3 Consensus Mechanisms for Trust
8.3.3 Promoting Sustainable Agricultural Practices
8.3.3.1 Traceability in the Supply Chain
8.3.3.2 Incentivizing Sustainable Practices
8.3.3.3 Zero-Knowledge Proofs for Privacy
8.3.3.4 Integrating Technology for Sustainable Growth
8.4 Decentralized Transactions and Smart Contracts
8.4.1 Overview of Blockchain-Based Transaction Mechanisms
8.4.2 Ganache: A Local Blockchain Platform for Agriculture
8.5 Blockchain’s Role in Quality Assurance and Market Access
8.5.1 Combatting Counterfeit Products and Fraud
8.5.2 Ensuring Product Quality through Secure Records
8.6 Future Perspectives and Innovations
8.6.1 Integrating Blockchain with IoT and AI in Agriculture
8.6.1.1 Blockchain and IoT
8.6.1.2 Blockchain and AI
8.6.2 Challenges in Scaling Blockchain Solutions
8.6.2.1 Adoption Barriers
8.6.3 Policies and Frameworks for Widespread Adoption
8.6.3.1 Standardization and Certification
References
9. Deep Learning-Based Supply-Chain Re-Traceability of Tea Leaves in a Permissioned Blockchain
Sandhya P., Ganesan R., Kalyanasundaram V., R. Srivats and Amogh Singh
9.1 Introduction
9.2 Literature Review
9.3 Proposed System
9.3.1 Architecture
9.3.2 Working
9.3.3 Participants
9.3.4 Operations
9.3.5 Advantages and Benefits
9.4 Results/Discussion
9.5 Conclusion
9.6 Future Work
References
10. Prohibition of Illegal Movement of Sandalwood from Reserve Forests through Retracing Supply Chain on a Permissioned Blockchain
Sandhya P., Ganesan R., Rama Parvathy L., R. Srivats, Kalyanasundaram V. and Amogh Singh
10.1 Introduction
10.2 Literature Review
10.3 Proposed System
10.3.1 Architecture
10.3.2 Working
10.3.3 Participants
10.3.4 Operations
10.3.5 Advantages and Benefits
10.4 Results/Discussion
10.5 Conclusion
10.6 Future Work
References
Part III: Advanced Technologies in Smart Agriculture
11. Enhanced Food Calorie Estimation: Multi-Layer Perceptron Versus K-Nearest Neighbors
Affan S.K. and Muneeshwari P.
Introduction
Materials and Methods
Research Environment
Sample Size Calculation
Implementation Framework
Programming and Dataset
Novel Enhanced Multi-Layer Perceptron Algorithm
K-Nearest Neighbor Algorithm
Statistical Analysis
Results
Discussion
Conclusion
References
12. Accuracy Comparison of Enhanced Multi-Layer Perceptron and Polynomial Regression in Food Calorie Measurement
Affan S.K. and Muneeshwari P.
Introduction
Materials and Methods
Novel Enhanced Multi-Layer Perceptron
Polynomial Regression
Statistical Analysis
Results
Conclusion
Bibliography
13. Effective Recommendation of Nutritious Food Using Random Forest Classifier in Comparison with Multi-Layer Perceptron Classifier Algorithm
J. Rishi Kannan and N. Bharatha Devi
Introduction
Materials and Methods
Study Design and Sample Selection
Tools and Technologies
Implementation of Novel Random Forest and MLP Classifiers
Novel Random Forest Classifier
Multi-Layer Perceptron Classifier
Statistical Analysis
Results and Discussion
Conclusion
References
14. Smart Pest Identification in Agriculture: Leveraging CNN Classifier Over SVM for Leaf Health Analysis
Bobbilla Ramya Sri and V. Karthick
Introduction
Materials and Methods
Support Vector Machine (SVM) Classifier Algorithm
Convolutional Neural Network (CNN) Classifier Algorithm
Statistical Analysis
Results
Discussion
Conclusion
References
15. Role of Artificial Intelligence in Weed Detection and Prevention
K. Arunkumar, S. Leninisha and M. Sandhya
Introduction
Various Methods of Weed Control
Introduction to UAV
Sensors and Their Usage
Dataset
Data Augmentation
Evaluation Parameters
Machine Learning
Deep Learning
Convolutional Neural Network
VGG Net Model
Inception and ResNet Module
DenseNet
YOLO
Blockchain
Discussions and Conclusion
Bibliography
About the Editors
Index
Back to Top
Preface
Part I: Blockchain Innovations in Agricultural Practices
1. Agriculture Meets Blockchain for Crop Monitoring and Prediction Using Machine Learning Techniques
D. Kavitha, Merin Varghese and Parth Vadera
1.1 Introduction
1.2 Related Works
1.3 Dataset
1.4 Data Analysis
1.5 Methodology
1.6 Architecture Diagram
1.7 Results and Discussions
1.8 Conclusion
References
2. Role of Machine Learning in Blockchain for Predictive Analysis
Dhivya Bharathi M., Leninisha Shanmugam and M. Sandhya
2.1 Introduction
2.2 Related Research
2.3 Existing System
2.4 System Hardware
2.5 Project Analysis
2.6 Problem Statement
2.7 Proposed Framework
2.8 IoT-ML Predictive Analysis
2.9 Blockchain Technology with IoT and Machine Learning
2.10 Benefits of Blockchain in Predictive Analysis
2.11 Artificial Intelligence and IoT in Smart Farming
2.12 Result Analysis
2.13 Conclusion
2.14 Future Work
References
3. Agriculture Manure Data Analysis Using Real-Time Cryptocurrency
Parvathi R., Pattabiraman V. and Xiaohui Yuan
3.1 Introduction
3.1.1 Motivation
3.1.2 Objective
3.2 Review of Literature
3.3 Materials and Methods
3.3.1 Dataset Collection and Description
3.3.2 Data Analysis
3.3.3 Information About Models
3.3.3.1 Model Planning
3.3.4 Model Building
3.3.5 Architecture Diagram and Explanation
3.4 Proposed Work
3.4.1 Research Gap and Novelty
3.5 Results and Discussion
3.5.1 Results and Explanation
3.5.2 Visualization
3.6 Conclusion
References
4. Future Agricultural Landscape Development: A MADM Model for Analysis
Ramakrishna Regulagadda, Syed Ziaur Rahman, Nallamala Sri Hari, Valeti Nagarjuna, Kolliboyina Hari and Sivudu Macherla
4.1 Introduction
4.2 Related Works
4.3 Multi-Attribute Decision Making (MADM)
4.4 Model Description
4.5 Implementing a Simulator of Alternative Futures
4.6 Conclusions
References
5. Cultivating Connectivity: Bridging Communities Through Farm Management Systems
Leninisha S., Riya Bansal V., Sai Lakshana S. and Krijay M.
5.1 Introduction
5.2 Literature Survey
5.3 Proposed System
5.4 System Design
5.5 Conclusion
5.6 Future Work
Bibliography
Part II: Blockchain in Agricultural Supply Chain and Traceability
6. Comprehensive Review of Blockchain-Oriented Methods in Agricultural Supply Chain Management
Pandiyaraju V., Thangaramya K., Kannan A. and Nikhil Nair
6.1 Introduction
6.1.1 Challenges in Agriculture Data Maintenance
6.1.1.1 Land Availability Data
6.1.1.2 Seed Problems
6.1.1.3 Usage of Fertilizer
6.1.1.4 Soil Erosion
6.1.1.5 Instability
6.1.1.6 Water Quality
6.1.1.7 Pest Management
6.1.1.8 Production Methods
6.1.1.9 Cropping Pattern
6.1.2 Agriculture Supply Chain
6.1.3 Need for Survey on Blockchain-Based Agricultural Supply Chain Management
6.2 Existing Works on Agricultural Supply Chain Management Using Blockchain
6.2.1 Works on Technology in Agriculture
6.2.2 Works on Artificial Intelligence in Agriculture
6.2.3 Works on Agricultural Supply Chain Management
6.2.4 Works on Use of Blockchain in Agriculture Supply Chain Management
6.2.5 Works on Blockchain Security Methods for Agricultural Data Maintenance
6.3 Proposed Work
6.3.1 DHASH Algorithm
6.3.2 Rule-Based Two-Phase Commit Protocol
6.4 Results and Discussions
6.5 Conclusions
References
7. Revolutionizing Agricultural Supply Chains with Blockchain for Enhancing Transparency, Efficiency, and Traceability
Arun Kumar Sivaraman, Rajiv Vincent, Janakiraman Nithiyanantham, Thirumurugan Shanmugam, Kong Fah Tee and Ajmery Sultana
7.1 Introduction
7.2 Understanding Blockchain Technology
7.3 Enhancing Transparency in Agricultural Supply Chains
7.4 Improving Efficiency in Agricultural Supply Chains
7.5 Enhancing Traceability in Agricultural Supply Chains
7.6 Real-World Applications of Blockchain in Agricultural Supply Chains
7.7 Challenges and Considerations for Blockchain Adoption
7.8 Future Trends and Developments
7.9 Conclusion
References
8. Cultivating Trust: How Blockchain is Reshaping Agriculture’s Supply Chain Landscape
Kalyanasundaram V., Keerthi A.J. and G. Prethija
8.1 Introduction to Blockchain’s Impact on Agriculture Supply Chains
8.1.1 The Role of Blockchain in Modern Agriculture
8.1.2 Key Challenges in Agricultural Supply Chains
8.1.3 Opportunities for Innovation
8.2 Enhancing Traceability with Blockchain
8.2.1 Recording Seed Origins, Cultivation Practices, and Harvest Yields
8.2.2 Real-Time Product Tracking Across Supply Chains
8.2.3 Building Consumer Trust through Transparency
8.3 Empowering Farmers and Communities
8.3.1 Blockchain for Financial Inclusion
8.3.1.1 Security through Advanced Encryption
8.3.1.2 Immutable Records for Transparency
8.3.2 Peer-to-Peer Lending and Crowdfunding Platforms
8.3.2.1 Direct Access to Capital
8.3.2.2 Enhanced Security
8.3.2.3 Consensus Mechanisms for Trust
8.3.3 Promoting Sustainable Agricultural Practices
8.3.3.1 Traceability in the Supply Chain
8.3.3.2 Incentivizing Sustainable Practices
8.3.3.3 Zero-Knowledge Proofs for Privacy
8.3.3.4 Integrating Technology for Sustainable Growth
8.4 Decentralized Transactions and Smart Contracts
8.4.1 Overview of Blockchain-Based Transaction Mechanisms
8.4.2 Ganache: A Local Blockchain Platform for Agriculture
8.5 Blockchain’s Role in Quality Assurance and Market Access
8.5.1 Combatting Counterfeit Products and Fraud
8.5.2 Ensuring Product Quality through Secure Records
8.6 Future Perspectives and Innovations
8.6.1 Integrating Blockchain with IoT and AI in Agriculture
8.6.1.1 Blockchain and IoT
8.6.1.2 Blockchain and AI
8.6.2 Challenges in Scaling Blockchain Solutions
8.6.2.1 Adoption Barriers
8.6.3 Policies and Frameworks for Widespread Adoption
8.6.3.1 Standardization and Certification
References
9. Deep Learning-Based Supply-Chain Re-Traceability of Tea Leaves in a Permissioned Blockchain
Sandhya P., Ganesan R., Kalyanasundaram V., R. Srivats and Amogh Singh
9.1 Introduction
9.2 Literature Review
9.3 Proposed System
9.3.1 Architecture
9.3.2 Working
9.3.3 Participants
9.3.4 Operations
9.3.5 Advantages and Benefits
9.4 Results/Discussion
9.5 Conclusion
9.6 Future Work
References
10. Prohibition of Illegal Movement of Sandalwood from Reserve Forests through Retracing Supply Chain on a Permissioned Blockchain
Sandhya P., Ganesan R., Rama Parvathy L., R. Srivats, Kalyanasundaram V. and Amogh Singh
10.1 Introduction
10.2 Literature Review
10.3 Proposed System
10.3.1 Architecture
10.3.2 Working
10.3.3 Participants
10.3.4 Operations
10.3.5 Advantages and Benefits
10.4 Results/Discussion
10.5 Conclusion
10.6 Future Work
References
Part III: Advanced Technologies in Smart Agriculture
11. Enhanced Food Calorie Estimation: Multi-Layer Perceptron Versus K-Nearest Neighbors
Affan S.K. and Muneeshwari P.
Introduction
Materials and Methods
Research Environment
Sample Size Calculation
Implementation Framework
Programming and Dataset
Novel Enhanced Multi-Layer Perceptron Algorithm
K-Nearest Neighbor Algorithm
Statistical Analysis
Results
Discussion
Conclusion
References
12. Accuracy Comparison of Enhanced Multi-Layer Perceptron and Polynomial Regression in Food Calorie Measurement
Affan S.K. and Muneeshwari P.
Introduction
Materials and Methods
Novel Enhanced Multi-Layer Perceptron
Polynomial Regression
Statistical Analysis
Results
Conclusion
Bibliography
13. Effective Recommendation of Nutritious Food Using Random Forest Classifier in Comparison with Multi-Layer Perceptron Classifier Algorithm
J. Rishi Kannan and N. Bharatha Devi
Introduction
Materials and Methods
Study Design and Sample Selection
Tools and Technologies
Implementation of Novel Random Forest and MLP Classifiers
Novel Random Forest Classifier
Multi-Layer Perceptron Classifier
Statistical Analysis
Results and Discussion
Conclusion
References
14. Smart Pest Identification in Agriculture: Leveraging CNN Classifier Over SVM for Leaf Health Analysis
Bobbilla Ramya Sri and V. Karthick
Introduction
Materials and Methods
Support Vector Machine (SVM) Classifier Algorithm
Convolutional Neural Network (CNN) Classifier Algorithm
Statistical Analysis
Results
Discussion
Conclusion
References
15. Role of Artificial Intelligence in Weed Detection and Prevention
K. Arunkumar, S. Leninisha and M. Sandhya
Introduction
Various Methods of Weed Control
Introduction to UAV
Sensors and Their Usage
Dataset
Data Augmentation
Evaluation Parameters
Machine Learning
Deep Learning
Convolutional Neural Network
VGG Net Model
Inception and ResNet Module
DenseNet
YOLO
Blockchain
Discussions and Conclusion
Bibliography
About the Editors
Index
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