Computational Intelligence in Internet of Agricultural Things
Description
This book focuses on the integration of IoT and AI techniques to generate greater data-driven solutions for the agriculture industry. It also focuses on computational intelligence (CI), machine learning, and AI techniques along with current applications, obstacles, and potential challenges and solutions for agricultural industries. These technologies have the potential to curtail resource wastage and contribute to addressing the challenges of feeding the expanding global population. This book acts as a resource to augment the reader's comprehension of the role of emerging IT technologies in the agricultural sector. This book also covers key technologies and techniques such as AI, ML, and IoT in the development of smart agriculture and provides information on various types of smart farming technology, platforms, and machine learning algorithms with case studies based on real-time problems.
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Content
.- 1: Computational Intelligence and Internet of Things in the Agriculture Sector: An Introduction.
.- 2: Role of Big Data Analytics in Intelligent Agriculture.
.- 3: Machine learning-based remote monitoring and predictive analytics system for apple harvest storage: A statistical model based approach.
.- 4: Revolutionizing Agriculture: Integrating IoT Cloud, And Machine Learning for Smart Farm Monitoring and Precision Agriculture.
.- 5: Impact of Advanced Sensing Technologies in Agriculture with Soil, Crop, Climate and Farmland-based approaches using Internet of Things.
.- 6: An analytical approach and concept mapping of agricultural issues using deep learning techniques.
.- 7: Explainable AI for next generation Agriculture - Current Scenario and Future Prospects.
.- 8: Barriers to implementing computational intelligence-based agriculture system.
.- 9: Agri-Chain: A Blockchain-Empowered Smart Solution for Agricultural Industry.
.- 10: Exploiting Internet of Things and AI-Enabled for Real-Time Decision Support in Precision Farming Practices.
.- 11: Advancing Plant Disease Detection with Hybrid Models: Vision Transformer and CNN-Based Approaches.
.- 12: Optimizing Agricultural Risk Management with Hybrid Block-chain and Fog Computing Architectures for Secure and Efficient Data Handling.
.- 13: Innovating with Quantum Computing Approaches in Block-chain for Enhanced Security and Data Privacy in Agricultural IoT Systems.
.- 14: Implementing Fog Computing in Precision Agriculture for Real-time Soil Health Monitoring and Data Management.
.- 15: Empowering Farmers: An AI-Based Solution for Agricultural Challenges.
.- 16: Artificial Intelligence in Agriculture: Potential Applications and Future Aspects.
.- 17: Case study on Smart irrigation using Internet of Things and XAI Techniques.
