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Section 1: IoT and edge Foundations and Framework
1. IoT - Foundations and applications
2. Edge Computing - Foundations and applications
3. Functional Framework for IoT-based agricultural system
4. Functional Framework for Edge-based agricultural system
Section 2: IoT use cases in Smart Farming and Smart Agriculture
5. Climate condition monitoring and automate system
6. Crop management system
7. IoT Based Smart Irrigation Monitoring and Controlling System
8. IoT-Based Drone for Improvement of Crop Quality in Agricultural Field
9. Monitoring and Discrimination of Plant Disease and Insect Pests based on agricultural IoT
10. The Internet of Things in Agriculture for Sustainable Rural Development
11. IoT in urban Greening
12. Smart E-Agriculture Monitoring System
13. IoT in greenhouse horticulture
14. Yield prediction and optimization
15. Energy-preserving protocols in IoT-enabled agricultural system
Section 3: Edge computing use cases in Smart Farming and Smart Agriculture
17. Farm Automation
18. Disaster Protection
Section 4: Sensor Network use cases in Smart Farming and Smart Agriculture
19. Agriculture Sensor Network: Infrastructure, protocols and standards
20. Implementation of sensors and RFID for disease and pest control
21. Sensor-based Precision agriculture, Sensor data acquisition
22. Sensor based expert systems for soil management: Ph. Control, Moisture content
23. A Crop Monitoring System Based on Wireless Sensor Network
24. Integration of RFID and Sensor in Agriculture Using IoT
Section 5: AI and Data Analytics in Agriculture
25. Prediction of Plant Diseases and Crop
26. Weather support system: online weather data monitoring, AI-based emergency alert system
27. Remote Monitoring and predictive analysis system: Crop monitoring, Livestock monitoring
28. Intelligent Agro-Food chain system
29. Data Analysis and prediction using Big data Analytics
30. Precision Agriculture: Weather Forecasting for Future Farming
31. Decision making system for crop selection based on soil
32. Vegetable Production and Distribution System
33. Cyber-Espionage, Agroterrorism, Confidential Information and Intellectual Property
Section 6: IoT in Aquaculture
34. Internet of Things on Sustainable Aquaculture System
35. Transformation of Fish Farming Industry using IoT
36. Predictive Platform for Shrimps
37. IoT Cloud Solution for Aquaculture
AI, Edge, and IoT Smart Agriculture integrates applications of IoT, edge computing, and data analytics for sustainable agricultural development and introduces Edge of Thing-based data analytics and IoT for predictability of crop, soil, and plant disease occurrence for improved sustainability and increased profitability. The book also addresses precision irrigation, precision horticulture, greenhouse IoT, livestock monitoring, IoT ecosystem for agriculture, mobile robot for precision agriculture, energy monitoring, storage management, and smart farming. The book provides an overarching focus on sustainable environment and sustainable economic development through smart and e-agriculture.
Providing a medium for the exchange of expertise and inspiration, contributions from both smart agriculture and data mining researchers around the world provide foundational insights. The book provides practical application opportunities for the resolution of real-world problems, including contributions from the data mining, data analytics, Edge of Things, and cloud research communities working in the farming production sector. The book offers broad coverage of the concepts, themes, and instruments of this important and evolving area of IOT-based agriculture, Edge of Things and cloud-based farming, Greenhouse IOT, mobile agriculture, sustainable agriculture, and big data analytics in agriculture toward smart farming.
- Integrates sustainable agriculture, Greenhouse IOT, precision agriculture, crops monitoring, crops controlling to prediction, livestock monitoring, and farm management
- Presents data mining techniques for precision agriculture, including weather prediction, plant disease prediction, and decision support for crop and soil selection
- Promotes the importance and uses in managing the agro ecosystem for food security
- Emphasizes low energy usage options for low cost and environmental sustainability
Researchers in various fields including computer science, researchers working in Agriculture domain, Application of IOT in agriculture, artificial intelligence, machine learning, image processing, agricultural big data analytics, smart agriculture, sustainable agriculture, e-agriculture, green IOT, agricultural supply chain. Graduates, post graduates and researchers of Agricultural Engineering/ Computer Science/Mechanical Engineering
- No. of pages:
- © Academic Press 2021
- 1st August 2021
- Academic Press
- Paperback ISBN:
Ajith Abraham received his PhD degree in computer science from Monash University, Australia. He is currently the Director of Machine Intelligence Research Labs (MIR Labs), a Not-for-Profit Scientific Network for Innovation and Research Excellence connecting Industry and Academia. As an Investigator/Co-Investigator, he received research grants worth more than 100 Million US dollars from Australia, USA, EU, Italy, Czech Republic, France, Malaysia, and China. He works in a multidisciplinary environment involving machine intelligence, cyber-physical systems, the Internet of Things, network security, sensor networks, web intelligence, web services, data mining, and applied to various real-world problems especially in health sciences. In these areas, he has authored or co-authored more than 1300 research publications. He is currently the Editor-in-Chief of Engineering Applications of Artificial Intelligence (EAAI) and serves/served the Editorial Board for more than 15 international journals. He is actively involved in the organization of several academic conferences, and some of them are now annual events.
Director, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, United States of America
Sujata Dash is Professor of Computer Science at North Orissa University in the Department of Computer Science, Baripada, India. She is a recipient of Titular Fellowship from Association of Commonwealth Universities, UK and was a visiting professor of Computer Science Department of University of Manitoba, Canada. She has published more than 160 technical papers as well as textbooks, monographs and edited books. She is a member of international professional associations and is a reviewer and editorial board member for multiple international journals. Her current research interest includes Machine Learning, Data Mining, Big Data Analytics, Bioinformatics, Fuzzy sets and systems, Rough sets, Soft Computing and Intelligent Agents.
Professor of Computer Science, Department of Computer Application, North Orissa University, Baripada, Mayurbhanj, India
Joel J. P. C. Rodrigues is a professor at the Federal University of Piauí, Brazil, and senior researcher at the Instituto de Telecomunicações, Portugal. He is the leader of the Next Generation Networks and Applications (NetGNA) research group (CNPq), an IEEE Distinguished Lecturer, Member Representative of the IEEE Communications Society on the IEEE Biometrics Council, and the President of the scientific council at ParkUrbis – Covilhã Science and Technology Park. He has been general chair and TPC Chair of many international conferences, including IEEE ICC, IEEE GLOBECOM, IEEE HEALTHCOM, and IEEE LatinCom. He has authored or coauthored over 800 papers in refereed international journals and conferences, 3 books, 2 patents, and 1 ITU-T Recommendation. He had been awarded several Outstanding Leadership and Outstanding Service Awards by IEEE Communications Society and several best papers awards. Prof. Rodrigues is a member of the Internet Society, a senior member ACM, and Fellow of IEEE.
Federal University of Piaui (UFPI), Teresina - PI, Brazil; Instituto de Telecomunicacoes, Portugal
Biswa Ranjan Acharya is an academic currently associated with Kalinga Institute of Industrial Technology Deemed to be University along with pursuing PhD in computer application from Veer Surendra Sai University of Technology (VSSUT), Burla, Odisha, India. He received MCA in 2009 from IGNOU, New Delhi, India and M.Tech in Computer Science and Engineering in the year of 2012 from Biju Pattanaik University of Technology (BPUT), Odisha, India. He is also associated with various educational and research societies like IEEE, IACSIT, CSI, IAENG, and ISC. He has industry experience as a software engineer. He currently is working on research in multiprocessor scheduling along with fields such as Data Analytics, Computer Vision, Machine Learning and IOT.
Department of Computer Science, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India
Subhendu Kumar Pani received his Ph.D. from Utkal University Odisha, India. He is a Professor in the Department of Computer Science & Engineering and also Research coordinator at Orissa Engineering College (OEC) Bhubaneswar. He has more than 16 years of teaching and research Experience His research interests include Data mining, Big Data Analysis, web data analytics, Fuzzy Decision Making and Computational Intelligence. He is a fellow in SSARSC and life member in IE, ISTE, ISCA, OBA.OMS, SMIACSIT, SMUACEE, CSI.
Professor, Department of Computer Science and Engineering / Research Coordinator, Orissa Engineering College (OEC), Bhubaneswar, India
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