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Full Description
This book highlights the integration of cloud, edge, and network computing for sustainable farming and future trends in smart farming including the role of UAVs, 5G, and advanced artificial intelligence. It covers the use of real-time environmental data for forecasting and tracking agricultural products and livestock health monitoring.
Offers a detailed exploration of the integration of Internet of Things, artificial intelligence, and multimodal intelligence in precision agriculture, covering a wide range of applications from crop management to livestock monitoring.
Identifies and discusses the challenges of using artificial intelligence and multimodal data in agriculture, providing solutions and techniques to overcome these obstacles. • Discusses advanced technologies like multispectral and hyperspectral imaging, Internet of Things sensors, and data fusion techniques.
Highlights emerging trends and future directions in smart farming, including UAVs, 5G, cloud-edge continuum integration, and federated learning.
Includes case studies and practical examples demonstrating successful applications of multimodal artificial intelligence in precision farming.
The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science and engineering, agricultural engineering, and information technology.
Contents
1. Introduction. 2. Use of Data Fusion Techniques for Optimizing Agricultural Practices. 3. Use of Multimedia Technologies/ Multimodal Intelligence for Crop Monitoring and Management. 4. Implementation of Machine Learning and Deep Learning Techniques for Disease Detection and Classification. 5. Analysis of Soil Properties using Internet of Things Sensors and Multimodal Data Analytics. 6. Employing Integrated Data to Study the Impact of Climate Change on Agriculture. 7. Using Multimodal Data to Monitor Environmental Conditions and their Effects on Crop Production. 8. Use of Historical Data and Multimedia Inputs to Model and Forecast Crop Yields. 9. Utilization of Real-time Environmental Data for Crop Harvest Forecasting and Prediction. 10. Utilizing Sensor Data for Tracking and Tracing Agricultural Products from Farm to Market. 11. Using Multimedia and Audio Sensor Data for Livestock Health Monitoring. 12. Developing Mobile and Web Applications to Provide Farmers Recommendations for Efficient Farming Practices. 13. Future Trends in Multimedia and Multimodal Intelligence for Smart Farming. 14. The Importance of Integrating Federated Learning in Contemporary Farming Practices. 15. Precision Agriculture Utilizing Emerging Edge, Cloud and Network Computing. 16. Challenges and Future Trends w.r.t. Integration of Edge and Cloud Computing in Precision Agriculture. 17. Conclusion.
