Machine Learning Strategies and Security Enhancement in Agricultural Sustainability

1. Auflage 2026
ISBN: 978-1-041-06325-4
Verlag: Taylor & Francis Ltd

Buch, Englisch, 368 Seiten, Format (B × H): 156 mm x 234 mm

The book offers a unique fusion of cutting-edge machine-learning applications and security considerations in the context of agricultural sustainability.

- Examine the most recent developments in machine learning techniques designed especially to improve security and sustainable agriculture.

- Discusses machine learning techniques for crop health surveillance and disease early warning systems.

- Covers decision support systems for sustainable farming, and data-driven decision-making in agriculture.

- Presents cybersecurity measures for agricultural systems, implementation of security protocols for data protection, and addressing cybersecurity challenges in agriculture.

- Showcases how smart technologies are influencing agricultural practices, promoting sustainable agriculture, stimulating economic growth, and safeguarding essential agricultural resources.

The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science and engineering, agricultural science, and information technology.

Zielgruppe

Academic, Postgraduate, and Undergraduate Advanced

Autoren/Hrsg.

Ramisetty, Sowjanya

Kumar, A.V. Senthil

Musirin, Ismail

Tomar, Praveen

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
• Naturwissenschaften Agrarwissenschaften Ackerbaukunde, Pflanzenbau
• Mathematik | Informatik EDV | Informatik Technische Informatik

Inhaltsverzeichnis

1.  Reaping the Digital Dividend: Technology, Inclusion and Transformation in Indian Agriculture  2. Precision Agriculture: Optimizing Resource Management 3. AGRICULTURAL PRODUCTIVITY ENHANCEMENT THROUGH MACHINE LEARNING FOR CROP YIELD OPTIMIZATION 4. Data-Driven Remote Sensing for Smart Agriculture Soil Health Management 5. Integration of Remote Sensing in Smart Farming 6. Crop Monitoring and Early Disease Detection 7. Machine Learning in Insect Pest Management 8. Decision Support System for Sustainable Farming 9. Innovative Technologies for Water Management in Agriculture 10.  Machine Learning Sustainable Solutions for Optimizing Water Use on Farms 11. Optimizing Turmeric Farm Performance: A Machine Learning Based Stochastic Frontier Production Function Approach 12.  Cybersecurity Measures for Agricultural Systems: Safeguarding Smart Farming Technologies 13.  Emerging Technologies in Smart Agriculture: Shaping the Future of Farming

Über Autor(innen)

Sowjanya Ramisetty has over fifteen years of teaching experience and eight years of research experience. She is currently working as an Assistant Professor in the Department of Computer Science and Engineering, Faculty of Science and Technology (FST), at ICFAI Foundation for Higher Education, Hyderabad.She completed her postdoctoral research at the International Institute of Information Technology, Hyderabad, in 2024. She obtained her Ph.D. in Computer Science and Engineering from Lovely Professional University.Her research interests include Internet of Things (IoT), Network Security, Machine Learning, Artificial Intelligence, and Cyber Security. She qualified the Andhra Pradesh State Eligibility Test (AP-SET) in 2019. Sowjanya has published fifteen research articles in SCI and Scopus-indexed journals, along with several book chapters and conference proceedings. She has also been granted two design patents. In addition, she is a Microsoft Certified Professional and has completed certification courses in Microsoft®.NET Framework – Application Development Foundation and Microsoft®.NET Framework 2.0 – Web-Based Client Development.

A.V.Senthil Kumar is working as Principal, Nehru Institute of Information Technology and Management, Coimbatore, India. He has worked as Professor and Director, PG and Research Department of Computer Applications, Hindusthan College of Arts & Science, Coimbatore, Tamilnadu for more than 15 years and as Senior Grade Lecturer in CMS College of Science and Commerce for 14 years. To his credit he has industrial experience for five years and teaching experience of 29 years. He has also received his Doctor of Science (D.Sc in Computer Science). He has to his credit 106 Book Chapters, 235 papers in International and National Journals, 92 papers in International Conferences in International and National Conferences, and edited 22 books and 3 Text books. He is as Associate Editor of IEEE Access. He is an Editor-in-Chief for many journals and Key Member for India, Machine Intelligence Research Lab (MIR Labs). He is an Editorial Board Member and Reviewer for various International Journals. He is also a Committee member for various International Conferences. He has guided 16 Ph.D scholars.

Ismail Musirin obtained Bachelor of Electrical Engineering (Hons) in 1990 from Universiti Teknologi Malaysia, MSc in Pulsed Power Technology in 1992 from University of Strathclyde, United Kingdom and PhD in Electrical Engineering from Universiti Teknologi MARA (UiTM), Malaysia in 2005. He is currently a Professor of Power System at the School of Electrical Engineering (formerly known as the Faculty of Electrical Engineering), College of Engineering, UiTM and headed the Power System Operation (POSC) Computational Intelligence Research Group.

He has published over 400 papers in international indexed journals and conferences. He has been given the opportunity to review papers in several reputable publishers. He has chaired more than 20 international conferences since 2007. To date, he has delivered keynote speeches at Cambridge University, United Kingdom, Dubai, Korea, China, India, Indonesia and Malaysia.

His research interest includes Power System Stability, Distributed Generation Optimization, Artificial Intelligence Applications, Optimization Algorithms Derivations and Machine Learning Applications.

Praveen Thomar is a passionate civil servant, researcher, and technology enthusiast with over 23 years of experience in IT transformation, data, AI, and automation. He have a Six Sigma Black Belt certification and multiple publications on low-code digital automation. As the Head of Data Automation (Data and AI) at Ofgem, he lead a team of talented professionals who are passionate about enhancing public service delivery and outcomes by harnessing the power of data and automation and AI, while respecting human dignity and values. In my previous roles, he successfully led and executed multiple digital transformation and automation programs for government departments and a global investment bank using a cloud-first approach, low-code platforms, and data-driven insights. He have also provided expert advisory and consulting support to senior stakeholders, helping them formulate and implement effective digital strategies and efficiency improvement initiatives. Through his work, he contribute to Ofgem's vision of building a better data ecosystem for the UK's energy sector.