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AI, Edge and IoT-based Smart Agriculture
- 1st Edition - November 10, 2021
- Editors: Ajith Abraham, Sujata Dash, Joel J.P.C. Rodrigues, Biswaranjan Acharya, Subhendu Kumar Pani
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 3 6 9 4 - 9
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 3 6 9 5 - 6
AI, Edge, and IoT Smart Agriculture integrates applications of IoT, edge computing, and data analytics for sustainable agricultural development and introduces Edge of Thing-bas… Read more
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Request a sales quoteAI, 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
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Section 1: IoT and edge foundations and framework
- Chapter 1: Internet of things (IoT) and data analytics in smart agriculture: Benefits and challenges
- Abstract
- 1: Introduction
- 2: IoT ecosystem in agriculture
- 3: Benefits of IoT in agriculture
- 4: Open issues and key challenges in the adoption of IoT in agriculture
- 5: Legal issues in regulating AI in agriculture
- 6: Conclusion
- References
- Chapter 2: Edge computing–Foundations and applications
- Abstract
- 1: Introduction
- 2: Edge computing
- 3: Applications of edge computing
- 4: Conclusions
- References
- Chapter 3: IoT-based fuzzy logic-controlled novel and multilingual mobile application for hydroponic farming
- Abstract
- 1: Introduction
- 2: Literature review
- 3: Methodology
- 4: Proposed method
- 5: Results and discussion
- 6: Conclusion
- References
- Chapter 4: Functional framework for IoT-based agricultural system
- Abstract
- 1: Introduction
- 2: Methodology
- 3: Experimental results and discussion
- 4: Results
- 5: Discussion
- 6: Conclusion and future scope
- References
- Chapter 5: Functional framework for edge-based agricultural system
- Abstract
- 1: Introduction
- 2: Relevant technologies
- 3: Edge computing in agricultural sectors
- 4: Edge computing framework design in agriculture
- 5: Edge computing implementation
- 6: Conclusion
- References
- Chapter 6: Precision agriculture: Weather forecasting for future farming
- Abstract
- 1: Introduction
- 2: Weather and climate
- 3: Agricultural implications of climate change
- 4: Modern tools and techniques for precision agriculture
- 5: Conclusion
- References
- Section 2: IoT use cases in smart farming and smart agriculture
- Chapter 7: Crop management system using IoT
- Abstract
- 1: Introduction
- 2: Background and related works
- 3: Proposed model
- 4: Methodology
- 5: Performance analysis
- 6: Future research direction
- 7: Conclusion
- References
- Chapter 8: Smart irrigation and crop security in agriculture using IoT
- Abstract
- 1: Introduction
- 2: Methodology
- 3: Algorithms
- 4: Implementation
- 5: Testing and results
- 6: Conclusion and future scope
- References
- Chapter 9: The Internet of Things in agriculture for sustainable rural development
- Abstract
- 1: Introduction
- 2: Background details
- 3: IoT in agriculture: Use cases
- 4: Case studies: IoT-based agriculture for sustainable rural development
- 5: Impact of IoT on food sustainability and socioeconomic uplift
- 6: Challenges and opportunities
- 7: Conclusion
- References
- Chapter 10: Internet of Things (IoT) in agriculture toward urban greening
- Abstract
- 1: Introduction
- 2: IOT architectures
- 3: G-IOT application
- 4: IOT applications
- 5: G-IOT challenges and opportunities
- 6: Conclusion
- References
- Chapter 11: Smart e-agriculture monitoring systems
- Abstract
- 1: Introduction
- 2: Need for smart e-monitoring system for agriculture
- 3: System architecture
- 4: IoT and data analytics in agriculture
- 5: Different types of solutions available
- 6: Research challenges
- 7: Case study on IoT-based monitoring systems
- 8: Open research issues
- 9: Conclusion
- References
- Chapter 12: Smart agriculture using renewable energy and AI-powered IoT
- Abstract
- 1: Introduction
- 2: Background and related work
- 3: Concept
- 4: Architecture and system design
- 5: User operability
- 6: Application
- 7: Advantages
- 8: Limitations
- References
- Chapter 13: Smart irrigation-based behavioral study of Moringa plant for growth monitoring in subtropical desert climatic condition
- Abstract
- 1: Introduction
- 2: Moringa oleifera as a miracle plant
- 3: Favorable climatic condition
- 4: Motivation and challenges
- 5: Technology used
- 6: Methodology
- 7: Flowchart
- 8: Limitations and area of improvement
- 9: Conclusion
- References
- Chapter 14: Surveying smart farming for smart cities
- Abstract
- 1: Introduction
- 2: Smart farming history
- 3: Smart farming and future trends
- 4: Conclusions
- References
- Section 3: Edge computing use cases in smart farming and smart agriculture
- Chapter 15: Farm automation
- Abstract
- 1: Introduction
- 2: Current trends in smart farming automation systems
- 3: Architecture of edge computing and IoT (E-IoT) platform
- 4: Applications of E-IoT in farm automation
- 5: Discussion
- 6: Future challenges
- 7: Conclusion
- References
- Chapter 16: A fog computing-based IoT framework for prediction of crop disease using big data analytics
- Abstract
- 1: Introduction
- 2: IoT-fog integration in crop disease prediction
- 3: Proposed model
- 4: Conclusion and future work
- References
- Chapter 17: Agribots: A gateway to the next revolution in agriculture
- Abstract
- 1: Introduction
- 2: Specific examples of how agribots could be integrated into a regional IoT-enabled single window for improving collective subsistence agricultural production in rural communities
- 3: Conclusion
- References
- Section 4: Sensor network use cases in smart farming and smart agriculture
- Chapter 18: SAW: A real-time surveillance system at an agricultural warehouse using IoT
- Abstract
- 1: Introduction
- 2: Issues and challenges with a traditional monitoring system in agriculture
- 3: The possibilities of IoT as an alternate to conventional agriculture
- 4: Components used with specifications and applications for IoT-enabled agriculture system
- 5: IoT-enabled autonomous agriculture model (SAW)
- 6: Conclusion
- References
- Chapter 19: The predictive model to maintain pH levels in hydroponic systems
- Abstract
- 1: Introduction
- 2: Hydroponics system discussion
- 3: NFT channels
- 4: DWC hydroponics
- 5: Drip system
- 6: Aeroponics
- 7: pH management automation
- 8: Dataset
- 9: Model generation
- 10: Hydroponics automation
- 11: Conclusion and future scope
- References
- Chapter 20: A crop-monitoring system using wireless sensor networking
- Abstract
- 1: Introduction
- 2: Background and related works
- 3: Proposed model
- 4: Methodology
- 5: Performance analysis
- 6: Future research direction
- 7: Conclusion
- References
- Chapter 21: Integration of RFID and sensors in agriculture using IOT
- Abstract
- 1: Introduction
- 2: Background and related works
- 3: System design and architecture
- 4: Methodology
- 5: Future research direction
- 6: Conclusion
- References
- Section 5: AI and data analytics in agriculture
- Chapter 22: Prediction of crop yield and pest-disease infestation
- Abstract
- 1: Introduction
- 2: Crop yield forecasting models
- 3: Pest and disease forewarning systems
- 4: Conclusion
- References
- Chapter 23: Machine learning-based remote monitoring and predictive analytics system for crop and livestock
- Abstract
- 1: Introduction
- 2: Background study
- 3: Reported work
- 4: Comparative analysis
- 5: Conclusion
- References
- Chapter 24: Exploring performance and predictive analytics of agriculture data
- Abstract
- 1: Introduction
- 2: Literature survey
- 3: The need for data processing
- 4: Big data characteristics
- 5: Techniques and tools for big data processing
- 6: Advantages of data analysis in agriculture
- 7: Classical approach of farming process
- 8: Smart farm management
- 9: Advantages of smart farming
- 10: Analysis of usefulness of various smart farming techniques
- 11: Agricultural big data mining
- 12: Study of agricultural sector using mobile apps
- 13: Study of vegetable production using hydroponics
- 14: Comparative approach of implementation mechanisms
- 15: Literature survey on algorithms
- 16: Comparative approach of the various techniques
- 17: Study of datasets
- 18: Data isualization
- 19: Result and analysis
- 20: Concerns and conclusions
- References
- Chapter 25: Climate condition monitoring and automated systems
- Abstract
- 1: Introduction
- 2: Impacts of climate
- 3: Climate monitoring systems and automation
- 4: Recent developments on applications of climate monitoring systems in environment, health, and agriculture
- 5: Conclusion and future work
- References
- Chapter 26: Decision-making system for crop selection based on soil
- Abstract
- 1: Introduction
- 2: Machine learning role in agriculture: A review
- 3: Soil health and crop production
- 4: Experiment and analysis
- 5: Performance metrics
- 6: Crop selection and soil health recommendation system
- 7: Conclusion and challenges
- References
- Chapter 27: Cyberespionage: Socioeconomic implications on sustainable food security
- Abstract
- 1: Introduction
- 2: Conclusion
- References
- Chapter 28: Internet of Things on sustainable aquaculture system
- Abstract
- 1: Introduction
- 2: Internet of Farming Things
- 3: Internet of Things on sustainable aquaculture system
- 4: Conclusions
- References
- Chapter 29: IoT-based monitoring system for freshwater fish farming: Analysis and design
- Abstract
- 1: Introduction
- 2: Relevance of monitoring devices in freshwater fish farming (including IoT and smart monitoring systems)
- 3: IoT-based monitoring production: Feasibility, requirement planning, analysis, and design
- 4: Building IoT infrastructure for monitoring production: Feasibility, requirement planning, analysis, and design
- 5: Conclusions and recommendations
- References
- Chapter 30: Transforming IoT in aquaculture: A cloud solution
- Abstract
- 1: Introduction
- 2: Cloud computing in IoT
- 3: Types of clouds
- 4: Benefits of cloud platform in IoT
- 5: Integration of cloud computing and IoT
- 6: Cloud platforms
- 7: Cloud-based IoT monitoring aquaculture system
- 8: Security
- 9: Cloud-IoT architecture in shrimp aquaculture
- 10: Introduction of wireless sensor networks (WSN)
- 11: Future trends and conclusion
- References
- Chapter 31: Toward the design of an intelligent system for enhancing salt water shrimp production using fuzzy logic
- Abstract
- 1: Introduction
- 2: Specific examples of intelligent systems for enhancing salt water shrimp production using fuzzy logic
- 3: Conclusion
- References
- Index
- No. of pages: 574
- Language: English
- Edition: 1
- Published: November 10, 2021
- Imprint: Academic Press
- Paperback ISBN: 9780128236949
- eBook ISBN: 9780128236956
AA
Ajith Abraham
Dr. Ajith Abraham is a Pro Vice-Chancellor at Bennette University. He is the director of Machine Intelligence Research Labs (MIR Labs), Australia. MIR Labs are a not-for-profit scientific network for innovation and research excellence connecting industry and academia. His research focuses on real world problems in the fields of machine intelligence, cyber-physical systems, Internet of things, network security, sensor networks, Web intelligence, Web services, and data mining. He is the Chair of the IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing. He is editor-in-chief of Engineering Applications of Artificial Intelligence (EAAI) and serves on the editorial board of several international journals. He received his PhD in Computer Science from Monash University, Melbourne, Australia.
Affiliations and expertise
Pro Vice-Chancellor, Sai University, Chennai, IndiaSD
Sujata Dash
Sujata Dash holds the position of Professor at the Information Technology School of Engineering and Technology, Nagaland University, Dimapur Campus, Nagaland, India, bringing more than three decades of dedicated service in teaching and mentoring students. She has been honoured with the prestigious Titular Fellowship from the Association of Commonwealth Universities, United Kingdom. As a testament to her global contributions, she served as a visiting professor in the Computer Science Department at the University of Manitoba, Canada. With a prolific academic record, she has authored over 200 technical papers published in esteemed international journals, and conference proceedings, and edited book chapters by reputed publishers Serving as a reviewer and Associate Editor for approximately 15 international journals.
Affiliations and expertise
Department of Information Technology, School of Engineering and Technology, Nagaland University, IndiaJR
Joel J.P.C. Rodrigues
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.
Affiliations and expertise
Federal University of Piaui (UFPI), Teresina - PI, Brazil; Instituto de Telecomunicacoes, PortugalBA
Biswaranjan Acharya
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.
Affiliations and expertise
Department of Computer Science, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, IndiaSP
Subhendu Kumar Pani
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.
Affiliations and expertise
Krupajal Engineering College, Prashanti Vihar, Near CIFA, Kausalya Ganga, Bhubaneswar, Khordha, Odisha, IndiaRead AI, Edge and IoT-based Smart Agriculture on ScienceDirect