About the Book
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.
Table of Contents:
1. Internet of things (IoT) and data analytics in smart agriculture: Benefits and challenges
2. Edge computing - Foundations and applications
3. IoT-based fuzzy logic-controlled novel and multilingual mobile application for hydroponic farming
4. Functional framework for IoT-based agricultural system
5. Functional framework for edge-based agricultural system
6. Precision agriculture: Weather forecasting for future farming
7. Crop management system using IoT
8. Smart irrigation and crop security in agriculture using IoT
9. The Internet of Things in agriculture for sustainable rural development
10. Internet of Things (IoT) in agriculture toward urban greening
11. Smart e-agriculture monitoring systems
12.Smart agriculture using renewable energy and AI-powered IoT
13. Smart irrigation-based behavioral study of Moringa plant for growth monitoring in subtropical desert climatic condition
14. Surveying smart farming for smart cities
15. Farm Automation
16. A fog computing-based IoT framework for prediction of crop disease using big data analytics
17. Agribots: A gateway to the next revolution in agriculture
18. SAW: A real-time surveillance system at an agricultural warehouse using IoT
19. The predictive model to maintain pH levels in hydroponic systems
20. A crop-monitoring system using wireless sensor networking
21. Integration of RFID and sensors in agriculture using IOT
22. Prediction of crop yield and pest-disease infestation
23. Machine learning-based remote monitoring and predictive analytics system for crop and lives
24. Exploring performance and predictive analytics of agriculture data
25. Climate condition monitoring and automated systems
26. Decision-making system for crop selection based on soil
27. Cyberespionage: Socioeconomic implications on sustainable food security
28. Internet of Things on sustainable aquaculture system
29. IoT-based monitoring system for freshwater fish farming: Analysis and design
30. Transforming IoT in aquaculture: A cloud solution
31. Toward the design of an intelligent system for enhancing salt water shrimp production using fuzzy logic
About the Author :
Dr. Ajith Abraham is the Vice Chancellor at Sai University, Chennai. Before joining Sai University, he held the position of vice chancellor at prominent institutions and was also the founding director of Machine Intelligence Research Labs (MIR Labs), a non-profit scientific network for innovation and research excellence with headquarters in Seattle, USA. Dr. Abraham has completed research projects valued at over $110 million as an investigator or co-investigator from the United States, the European Union, Italy, the Czech Republic, France, Malaysia, China, and Australia. He has worked in a multidisciplinary setting for more than 35 years and has authored or co-authored more than 1,500+ research publications in artificial intelligence and related applications in the industry. A handful of his publications have been translated into Chinese and Russian, and one of his books has been translated into Japanese. The Scopus database has approximately 1,400 papers indexed, whereas the Thomson Web of Science has over 1,000 publications indexed.
In addition to other esteemed universities, Dr. Abraham has worked with researchers from MIT (USA), the University of Cambridge (UK), Harvard University (USA), and Oxford University (UK). According to Google Scholar, Dr. Abraham possesses over 63,000 scholarly citations with an H-index of over 118. He has delivered over 250 conference plenary talks and tutorials in more than 20 countries. From 2008 to 2021, Dr. Abraham chaired the IEEE Systems, Man, and Cybernetics Society Technical Committee on Soft Computing, which had more than 200 members. From 2011 to 2013, he represented Europe as a Distinguished Lecturer for the IEEE Computer Society (USA). Dr. Abraham is continuously listed in the Stanford/Elsevier list, highlighting the top 2% of the most cited scientists across the globe. Based on 2024 data, ScholarGPS listed Dr. Abraham as one of the world’s top 0.01% cited scientists in the engineering and computer science fields.
From 2016 to 2021, Dr. Abraham worked as the chief editor of Engineering Applications of Artificial Intelligence (EAAI) at Elsevier, New York. EAAI is one of the oldest journals (founded in 1988) in the artificial intelligencedomain. Additionally, he sat on the editorial boards of more than 15 international journals indexed by Thomson ISI. Dr. Abraham received his Ph.D. degree in artificial intelligence from Monash University, Melbourne, Australia (2001), a Master of Science degree from Nanyang Technological University, Singapore (1998), and a B.Tech (Hons) degree from the University of Calicut in 1990.
Sujata Dash is a Senior Member, IEEE who received the Ph.D. degree in computational modeling from Berhampur University, Orissa, India, in 1995. She is currently an Associate Professor with the P.G. Department of Computer Science and Application, North Orissa University, Baripada, India. She has published more than 150 technical articles in international journals, conferences, and book chapters of reputed publications. She has guided many scholars for their Ph.D. degrees in computer science. She is associated with many professional bodies like IEEE, CSI, ISTE, OITS, OMS, IACSIT, IMS, and IAENG. She is a member of the editorial board of several international journals and also reviewer of many international journals. Her current research interests include machine learning, distributed data mining, bioinformatics, intelligent agent, Web data mining, recommender systems, and image processing. 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. 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. Dr. Subhendu Kumar Pani received his Ph.D. from Utkal University, Odisha, India in the year 2013. He is working as a professor at Krupajal Engineering College under BPUT, Odisha, India. He has more than 20 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 the recipient of 5 researcher awards. In addition to research, he has guided two PhD students and 31 M. Tech students. He has published 150 International Journal papers (100 Scopus index). His professional activities include roles as Book Series Editor (CRC Press, Apple Academic Press, Wiley-Scrivener), Associate Editor, Editorial board member and/or reviewer of various International Journals. He is an Associate with no. of the conference societies. He has more than 250 international publications, 5 authored books, 25 edited and upcoming books; 40 book chapters into his account. He is a fellow in SSARSC and a life member in IE, ISTE, ISCA, and OBA.OMS, SMIACSIT, SMUACEE, CSI.
