Data Driven Mathematical Modeling in Agriculture (eBook, PDF)

Tools and Technologies
Redaktion: Pramanik, Sabyasachi; Bose, Rajesh; Roy, Sandip

• Format: PDF

Produktbeschreibung

The research in this book looks at the likelihood and level of use of implemented technological components with regard to the adoption of different precision agricultural technologies. To identify the variables affecting farmers' choices to embrace more precise technology, zero-inflated Poisson and negative binomial count data regression models are utilized. Outcomes from the count data analysis of a random sample of various farm operators show that various aspects, including farm dimension, farmer demographics, soil texture, urban impacts, farmer position of liabilities, and position of the farm in a state, were significantly associated with the approval severity and likelihood of precision farming technologies.

Technical topics discussed in the book include:

• Precision agriculture
• Machine learning
• Wireless sensor networks
• IoT
• Deep learning

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 500
• Erscheinungstermin: 23. August 2024
• Englisch
• ISBN-13: 9781040120972
• Artikelnr.: 72274459

Autorenporträt

Sabyasachi Pramanik is a Professional IEEE member. He obtained a Ph.D. in Computer Science and Engineering from Sri Satya Sai University of Technology and Medical Sciences, Bhopal, India. Presently, he is an Associate Professor, Department of Computer Science and Engineering, Haldia Institute of Technology, India. He has many publications in various reputed international conferences and journals, and has written online book chapter contributions (Indexed by SCIE, Scopus, ESCI, etc.). His research is in the fields of artificial intelligence, data privacy, cybersecurity, network security, and machine learning. He serves as an editorial board member of many international journals. He is a reviewer of journal articles from IEEE, Springer, Elsevier, Interscience, IET, and IGI Global. He has reviewed many conference papers, and has been a keynote speaker, session chair and technical program committee member for many international conferences. He has authored a book on Wireless Sensor Network. He has edited books from IGI Global, CRC Press, Springer and Wiley Publications. Dr. Sandip Roy is a Professor & Head of Department of Computational Science of the Brainware University, Kolkata, India. He obtained his Ph.D. in Computer Science & Engineering from University of Kalyani, India in 2018. Dr. Roy received an M.Tech. degree in Computer Science & Engineering in 2011, and a B.Tech. in Information Technology in 2008 from Maulana Abul Kalam Azad University of Technology, West Bengal (formerly known as West Bengal University of Technology). He was a post-doctoral fellow in the Computer Science and Engineering department of Srinivas University, Mangalore, India between January 2020 and January 2021. He also served as research assistant with different collaborative industry projects of Simplex Infrastructures Ltd., Bharti Airtel Ltd., etc. He has authored over 100 papers in peer-reviewed journals and conferences, and was a recipient of the Best Paper Award from ICACEA in 2015. He has also authored 8 books and also granted 15 patents. His main areas of interest are data science, internet of things, cloud computing, green computing, and smart technologies. Dr. Rajesh Bose is currently employed as a Professor and Director of Research at the Department of Computational Science, Brainware University, Kolkata, India, and is a Consultant at R&D Division of Simplex infrastructures Ltd. He graduated with a B.Eng. in Computer Science and Engineering from Biju Patnaik University of Technology (BPUT), Rourkela, Orissa, India in 2004. He went on to complete his degree in M.Tech. in mobile communication and networking from Maulana Abul Kalam Azad University of Technology, West Bengal (formerly known as West Bengal University of Technology - WBUT), India in 2007. He completed his Ph.D. degree from the University of Kalyani in Computer Science and Engineering in 2018 and completed a Post-Doctoral Fellowship in Focusing Artificial Intelligence and Machine Learning in Clinical Practice for an Effective Prognosis at Srinivas University, Karnataka. He also has several global certifications under his belt: CCNA, CCNP-BCRAN, and CCA (Citrix Certified Administrator for Citrix Access Gateway 9 Enterprise Edition), CCA (Citrix Certified Administrator for Citrix Xenapp 5 for Windows Server 2008). His research interests include cloud computing, IoT, wireless communication and networking. He has published more than 200 refereed papers, 4 books and 12 granted patents. Under his guidance 3 Ph.D. scholars have received their Ph.Ds. With over 18 years of experience in industry and education institutions, he is presently recognized by IEEE as an active IEEE Access reviewer. He is experienced in conceptualizing, designing, implementing, and managing projects in Cloud Computing, Datacenter and IoT in the civil construction engineering sector as well as in academic research.

Inhaltsangabe

1. Use of CNNs and their Frameworks for the Detection of Fungal Herb
Disease 2. Technologies based on the IoT and Artificial and Natural
Intelligence for Sustainable Agriculture 3. IoT for Smart Farming
Technology: Practices, Methods and Future 4. Integrating Artificial
Intelligence into Pest Management 5. Practices of Deep Learning in Farming:
What Deep Learning Can Do in Intelligent Agriculture 6. Building a
Solar-powered Greenhouse Having SMS and a Web Information Framework 7.
Agriculture using Digital Technologies 8. Agriculture Digitization:
Perspectives on the Networked World 9. Cucumber in PH Disease Monitoring
Using an IoT-Based Mobile App 10. New Technologies for Sustainable
Agriculture 11. Agriculture Automation 12. Food 4.0: A Survey 13. Crop
Monitoring in Real Time in Agriculture 14. Smart Farming Utilizing Wireless
Sensor Network and Internet of Things 15. Intelligent Agriculture Using
Autonomous UAVs 16. Agriculture using Smart Sensors 17. Technologies that
Work Together for Precision Agriculture 18. Utilizing Smart Farming Methods
to Reduce Water Scarcity 19. Real-time Irrigation Optimization for
Horticulture Crops Using WSN, APSim, and Communication Models 20.
Greenhouse Gas Discharges from Farming Modeled Mathematically for Various
End Users