Harvesting Data
Blockchain, AI and Advanced Innovations in Agriculture
1. Auflage Februar 2026
352 Seiten, Hardcover
Wiley & Sons Ltd
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.
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
2 Role of Machine Learning in Blockchain for Predictive Analysis 29
Dhivya Bharathi M., Leninisha Shanmugam and M. Sandhya
3 Agriculture Manure Data Analysis Using Real-Time Cryptocurrency 45
Parvathi R., Pattabiraman V. and Xiaohui Yuan
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
5 Cultivating Connectivity: Bridging Communities Through Farm Management Systems 99
Leninisha S., Riya Bansal V., Sai Lakshana S. and Krijay M.
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
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
8 Cultivating Trust: How Blockchain is Reshaping Agriculture's Supply Chain Landscape 155
Kalyanasundaram V., Keerthi A.J. and G. Prethija
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
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
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.
12 Accuracy Comparison of Enhanced Multi-Layer Perceptron and Polynomial Regression in Food Calorie Measurement 249
Affan S.K. and Muneeshwari P.
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
14 Smart Pest Identification in Agriculture: Leveraging CNN Classifier Over SVM for Leaf Health Analysis 275
Bobbilla Ramya Sri and V. Karthick
15 Role of Artificial Intelligence in Weed Detection and Prevention 285
K. Arunkumar, S. Leninisha and M. Sandhya
Discussions and Conclusion 312
Bibliography 313
About the Editors 325
Index 327
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.