دانلود کتاب جمع‌آوری داده‌ها بلاکچین، هوش مصنوعی و نوآوری‌های پیشرفته در کشاورزی

In recent years, the agricultural sector has undergone tremendous transformation due to the groundbreaking advancements in technology. The increasing demand for food, coupled with the pressures of climate change and resource depletion, has necessitated innovative solutions to improve agricultural practices. At the forefront of these innovations are blockchain, artificial intelligence (AI), and machine learning, all of which have shown great potential in enhancing productivity, ensuring sustainability and reshaping agricultural systems globally. This volume, Harvesting Data: Blockchain, AI and Advanced Innovations in Agriculture, provides a comprehensive exploration of how these cutting-edge technologies are being integrated into various facets of agriculture. The contributions from leading experts in the field offer a unique and multifaceted perspective on the transformative power of these innovations. The book is divided into three parts, each focusing on key areas where blockchain and AI are revolutionizing the agricultural landscape. Part I: Blockchain Innovations in Agricultural Practices The first part of the book investigates the role of blockchain in modern agricultural practices. Blockchain technology, with its inherent characteristics of transparency, security, and decentralization, is proving to be a game-changer in agricultural operations. The chapters in this section highlight how blockchain is being utilized for crop monitoring and prediction, driven by machine learning techniques that enable more accurate and efficient agricultural forecasting. The application of machine learning for predictive analysis, as discussed in this part, offers insights into how farmers can use realtime data to anticipate weather patterns, optimize crop yields, and mitigate risks associated with climate variability. Additionally, the use of cryptocurrency for manure data analysis introduces an innovative approach to tracking and managing agricultural inputs, further demonstrating the diverse applications of blockchain in agriculture. One of the critical challenges in agriculture is the effective management of resources and land use. The MADM (Multi-Attribute Decision- Making) model presented in the following chapter offers a novel approach to landscape development, assisting decision-makers in evaluating multiple factors to plan and implement more sustainable farming practices. The section concludes with a look at farm management systems, which are bridging the gap between farmers and technological solutions, creating a connected ecosystem that promotes better collaboration and data-sharing for improved agricultural outcomes. Part II: Blockchain in Agricultural Supply Chain and Traceability The second part of the book explores the integration of blockchain in the agricultural supply chain and traceability. Transparency and efficiency in the food supply chain are critical concerns that have long plagued the agricultural sector. Blockchain, with its ability to provide immutable and transparent records, is helping solve these challenges by ensuring the integrity and traceability of agricultural products from farm to table. The chapters in this section offer a thorough examination of blockchain-oriented methods in agricultural supply chain management. By revolutionizing supply chain transparency, blockchain technology is enabling more efficient processes, reducing fraud and improving the overall quality of agricultural products. The use of blockchain for enhancing traceability is also crucial for ensuring food safety, enabling consumers and regulatory bodies to trace the origin of products, verify claims, and ensure compliance with safety standards. One of the standout contributions in this section is the application of blockchain in the quality analysis of tea leaves. By incorporating deep learning techniques, this chapter illustrates how blockchain can be used to create a persistent, secure record of product quality, facilitating more accurate monitoring and control of supply chains. Another insightful case study focuses on the prohibition of illegal movements of sandalwood from reserve forests, demonstrating how blockchain can ensure the legality and traceability of sensitive commodities, thus helping to prevent illegal trade and exploitation. Part III: Advanced Technologies in Smart Agriculture The final part of the book is dedicated to the advanced technologies driving the future of smart agriculture. Artificial intelligence, machine learning and data analytics are paving the way for more precise, sustainable, and intelligent farming methods. The chapters in this section focus on the role of AI in various aspects of agricultural management, from food calorie measurement to pest detection and prevention. One of the most innovative applications explored is the use of enhanced multi-layer perceptrons (MLP) for food calorie measurement. The chapter compares the accuracy of MLP with K-Nearest Neighbors (KNN) and polynomial regression, demonstrating how AI can improve the precision and efficiency of food analysis. Similarly, AI-powered recommendation systems are discussed in the context of suggesting nutritious foods, with a focus on how novel random forest classifiers can be used to achieve higher precision rates compared to traditional algorithms like MLP. Pest management is another area where AI is having a significant impact. Using convolutional neural networks (CNNs), AI is helping to identify and classify pests with greater accuracy, enabling farmers to take timely action to prevent crop damage. The role of AI in weed detection is also explored, with a focus on using machine learning techniques to develop automated systems that can detect and prevent the spread of weeds, ensuring optimal crop health and yield. The advancements covered in this book are not just theoretical concepts but represent real-world applications that are actively shaping the future of agriculture. As the global population continues to grow and the need for sustainable agricultural practices becomes more pressing, the integration of blockchain, AI, and machine learning will be pivotal in creating more efficient, transparent, and sustainable agricultural systems. The contributions in this book demonstrate the boundless potential of these technologies, offering new avenues for research and development. They provide practical insights into how agriculture can benefit from data-driven decision-making, greater transparency and enhanced resource management. From improving crop yields to ensuring food safety and traceability, the future of agriculture is being redefined by these innovations. Harvesting Data: Blockchain, AI and Advanced Innovations in Agriculture is a timely and essential resource for researchers, practitioners, and policymakers seeking to understand the transformative impact of technology on agriculture. By bringing together diverse perspectives and cutting-edge research, this book lays the foundation for the next generation of agricultural practices that will meet the challenges of the future while ensuring food security and sustainability for generations to come. As you explore the chapters, you will discover how the fusion of blockchain, AI, and advanced technologies is driving agriculture toward a smarter, more sustainable future. These innovations will not only reshape the agricultural industry but also help improve the quality of life for farmers, consumers, and communities worldwide. The journey has only just begun and this book marks an important step in that transformative path.