ADVANCED TECHNOLOGIES FOR SMART AGRICULTURE
This book brings new smart farming methodologies to the forefront, sparked by pervasive applications with automated farming technology. New indigenous expertise on smart agricultural technologies is presented along with conceptual prototypes showing how the Internet of Things, cloud computing, machi...
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| Other Authors: | , , , |
| Format: | Electronic eBook |
| Language: | English |
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[S.l.] :
RIVER PUBLISHERS,
2023.
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| Series: | River Publishers series in computing and information science and technology
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| 520 | |a This book brings new smart farming methodologies to the forefront, sparked by pervasive applications with automated farming technology. New indigenous expertise on smart agricultural technologies is presented along with conceptual prototypes showing how the Internet of Things, cloud computing, machine learning, deep learning, precision farming, crop management systems, etc., will be used in large-scale production in the future. The necessity of available welfare systems for farmers' well-being is also discussed in the book. It draws the conclusion that there is a greater need and demand today for smart farming methodologies driven by technology than ever before. | ||
| 545 | 0 | |a Dr. K. Kalaiselvi is currently working as Professor, School of Computing Sciences, Vels Institute of Science, Technology and Advanced Studies, Chennai, India, a well-known university. She has more than 20 years of teaching experience at both UG and PG level. Her research interests include knowledge management, data mining, embedded systems, wireless sensor networks, big data analytics and knowledge mining. She has published more than 90 research papers in various international journals, including Scopus Indexing, Web of Science and UGC care list Journals. She has also published chapters for various publishers like Springer, Wiley, and Emerald Publishing. She has acted as editor and reviewer for various reputed journals. She acted as editor for the book "Handbook on Intelligent Healthcare Analytics - Knowledge Engineering with Bigdata" in 2022. She has also published books including "Protocol to learn C Programming Language", "Tryambaka Programming Code {C, C++, Java}", and "A Handbook on Python Programming for Naive Seekers to Expertise", and "Programming in Java Beginners Guide". She has received the Best Researcher award, Young Educator and Scholar award, and Best Faculty award from various reputed organizations. Dr. A. Jose Anand received his Diploma from the State Board of Technical Education, Tamil Nadu in 1995, Bachelor of Engineering Degree from Institution of Engineers (INDIA), Calcutta in 2003, Master of Engineering in Embedded System Technologies from Anna University, Chennai in 2006, Master of Arts in Public Administration from Annamalai University in 2000 Master of Business Administration from Alagappa University in 2007, and Ph.D. (Information and Communication Engineering) from Anna University in 2017. He received State 3rd Rank in Bachelor of Engineering. He is a Member of CSI, IEI, IET, IETE, ISTE, INS, QCFI and EWB. He has one year of industrial experience and 23 years of teaching experience. He has presented several papers at National Conferences and International Conferences. He has published several papers in national and international journals, and also books for polytechnic and engineering subjects. He is recognized as a research supervisor by Anna University. Dr. Poonam Tanwar received his B.Tech and M.Tech in Computer Science & Engineering from Maharishi Dayanand University, India in 2001 and 2009 respectively, and his Ph.D. in computer science and engineering from the Uttarakhand Technical University, India in 2015. She has 18 years of teaching experience working as associate professor in Manav Rachna International Institute of Research & Studies, Faridabad, India. She has published more than 50 research papers in various International Journals and Conferences. She has one copyright and filled 6 patents. She edited the book "Big Data Analytics and Intelligence: A Perspective for Health Care" in 2019 published by Emerald. She was Guest Editor for a special issue "Advancement in Machine Learning (ML) and Knowledge Mining (KM)" for International Journal of Recent Patents in Engineering (UAE). She has been awarded the woman researcher award by VDGOODS Academy Chennai. She has organized various Science and Technology awareness programs for rural development. She is a technical program committee member for various international conferences like ICIC 2018, ICFNN, Rome (Italy), European Conference on Natural Language Processing and Information Retrieval, Berlin (Europe), etc. Dr. Haider Raza, School of Computer Science and Electronic Engineering (CSEE), University of Ulster, UK. Haider Raza received a bachelor's degree in computer science and engineering from the Integral University, India in 2008, a master's degree in computer engineering from the Manav Rachna International University, India in 2011, and a PhD in computer science from University of Ulster, UK in 2016. During his PhD, he won the best-literature review award sponsored by McGraw Hill. He is currently a Lecturer in AI for decision making at the School of Computer Science and Electronics Engineering, University of Essex, UK. Dr. Raza has a strong academic track record and a developing international profile. Highlights of his work include research funding (£620k) as PI for a range of research projects in the domains of healthcare analytics, business, and environmental science by Innovate UK, ESRC and NHS Trusts. He has published in peer-reviewed journals such as IEEE TNSRE, IEEE JBHI, and IEEE TCDS, and 16 conference papers in major international conferences. He is currently leading on two knowledge transfer partnership projects (KTP) and supervising two post-doc KTP associates funded by Innovate UK and has led on a project in healthcare with East Suffolk North Essex Foundation Trust on AI-based cancer pathways. | |
| 505 | 0 | |a Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- List of Contributors -- List of Figures -- List of Tables -- List of Abbreviations -- Chapter 1: Introduction to Smart Agriculture -- 1.1: Introduction to Agriculture Process -- 1.1.1: Soil preparation -- 1.1.2: Sowing -- 1.1.3: Manuring -- 1.1.4: Irrigation -- 1.1.5: Weeding -- 1.1.6: Harvesting -- 1.1.7: Storage -- 1.2: Role of Smart Agriculture in Soil Preparation -- 1.2.1: Activities of IoT sensors -- 1.3: Role of IoT Devices in Smart Agriculture -- 1.3.1: Robotics in agriculture | |
| 505 | 8 | |a 1.3.2: Drones in agriculture -- 1.3.3: Remote sensing sensors in agriculture -- 1.3.4: Computer imaging in agriculture -- 1.4: Role of Irrigation in Smart Agriculture -- 1.4.1: Soil sensors -- 1.4.2: Weather sensors -- 1.4.3: Plant sensors -- 1.4.4: Capabilities of a smart irrigation solution -- 1.4.5: Technology used in smart irrigation system -- 1.5: Role of Harvesting in Smart Agriculture -- 1.6: Advantages of Smart Agriculture -- 1.7: Challenges in Smart Agriculture -- 1.7.1: Hardware -- 1.7.2: Brain -- 1.7.3: Maintenance -- 1.7.4: Mobility -- 1.7.5: Infrastructure -- 1.7.6: Connectivity | |
| 505 | 8 | |a 1.7.7: Data gathering interval -- 1.7.8: Data security in the agriculture industry -- 1.8: Limitations of Smart Agriculture -- 1.9: Future Trends in Smart Agriculture -- 1.9.1: The Internet of food things (IoFT) -- 1.9.2: Advanced green revolution -- 1.9.3: Biologically edited crops -- 1.9.4: The robot farmers -- 1.9.5: The automated greenhouse effect technology -- 1.9.6: Climate smart agriculture -- 1.10: Conclusion -- References -- Chapter 2: Modern Agriculture Farming: Rack and Pinion Mechanism-based Remote Controlled Seed Sowing Robot -- 2.1: Introduction -- 2.1.1: Traditional sowing method | |
| 505 | 8 | |a 2.1.2: Problem statement -- 2.1.3: Objectives -- 2.1.4: Literature survey -- 2.2: Proposed Work -- 2.2.1: Block diagram -- 2.2.2: Design of robot -- 2.2.3: Seed sowing mechanism -- 2.2.4: Dispensing of seeds -- 2.2.5: TIVA C series -- 2.2.6: BoosterPacks -- 2.2.7: Software used: ENERGIA integrated development environment (IDE) -- 2.2.8: Components used -- 2.2.9: Performance analysis -- 2.3: Conclusion -- Acknowledgements -- References -- Chapter 3: Crop Management System -- 3.1: Introduction -- 3.2: Insights of Deep Learning -- 3.3: Crop Management System: A Deep Learning Approach | |
| 505 | 8 | |a 3.3.1: Soil health monitoring system -- 3.3.2: Sorting of seeds based on deep learning -- 3.3.3: Seed sowing using deep learning -- 3.3.4: Smart irrigation system using deep learning -- 3.3.5: Crop growth recognition using deep learning -- 3.3.6: Fertilizer estimation using deep learning -- 3.3.7: Crop harvesting using deep learning -- 3.3.8: Crop recommendation system using deep learning -- 3.4: Scope and Challenges in Crop Management System -- 3.4.1: Role of artifcial intelligence in smart farming -- 3.4.2: Farmers challenges in adopting new technologies -- 3.4.3: Challenges in deep learning | |
| 650 | 0 | |a Agriculture |x Data processing. | |
| 650 | 0 | |a Artificial intelligence |x Agricultural applications. | |
| 700 | 1 | |a Kalaiccelvi, Kā., |e editor. | |
| 700 | 1 | |a Anand, A. Jose, |d 1975- |e editor. | |
| 700 | 1 | |a Tanwar, Poonam, |d 1979- |e editor. | |
| 700 | 1 | |a Raza, Haider, |e editor. | |
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