Digital Farming

Case Studies and Applications of IoT and AI in Agricultural Industry
1. Auflage 2026
ISBN: 978-1-032-76725-3
Verlag: Taylor & Francis Ltd

Buch, Englisch, 312 Seiten, Format (B × H): 156 mm x 234 mm

Reihe: Chapman & Hall/CRC Internet of Things

This book explores IoT-based ecosystems and AI-driven automation in agriculture to provide solutions for intelligent farming systems and precision agriculture techniques. It provides a comprehensive view of emerging technologies’ integration and the resultant impact on the farming ecosystem. Real-world case studies and examples of IoT and AI applications in agriculture are included to show how IoT and AI have revolutionized conventional agriculture. The book also examines the potential and difficulties of deploying IoT and AI in the agricultural business with case studies.

Features:

- Demonstrates how technologies like precision agriculture, smart sensors, automated systems can be utilized to boost production, reduce waste, and increase sustainability by optimizing resource usage, reducing environmental impact.

- Explains how small sensors, Intelligence drones, or low-power Internet of Things setups for detecting, monitoring, gathering, analyzing, and storing data through cloud platforms are encouraging smart and intelligent agriculture.

- Highlights IoT and AI intervention options for effective assessment Musculoskeletal, ergonomic, and postural analyses are included.

- Explains usage of AI and IoT in precision farming, agricultural drones and hopping systems, livestock monitoring, temperature monitoring, smart greenhouses, and computer imaging.

- Discusses challenges like data security, rural connectivity issues, infrastructure limitations, and the digital divide in rural areas.

The book is a helpful reference for professionals, scholars and students interested in digital farming and sustainable agricultural practices. It acts as a guiding tool for practitioners, researchers, educators, and policymakers, encouraging discussions, innovations, and advancements that will shape a sustainable, efficient, and resilient future for agriculture.

Zielgruppe

Academic

Autoren/Hrsg.

Chandrakumar, T.

Sharma, Bhisham

Chowdhury, Subrata

Rabbie, Khaled

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Ambient Intelligence, RFID, Internet der Dinge
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Cloud-Computing, Grid-Computing
• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit Datensicherheit, Datenschutz
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Optische Nachrichtentechnik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Webprogrammierung
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Netzwerksicherheit
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Naturwissenschaften Agrarwissenschaften Agrarwissenschaften Nachhaltige Landwirtschaft
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken

Inhaltsverzeichnis

Chapter 1. Applications of Artificial Intelligence (AI) and IoT in Smart Farming. Chapter 2. Integrating Blockchain for Enhanced Paddy Disease Traceability: A Conceptual Model. Chapter 3. Glucose Biosensors for Diabetes Monitoring: Fundamentals and Latest Technological Developments. Chapter 4. Agriculture Drones and Hopping systems. Chapter 5. Applications and Challenges of Metaverse in the Agriculture Sector: A Review. Chapter 6. Enhancing a Predictive Model for Forecasting Paddy Seed Yields Using Generative Techniques. Chapter 7. Cost Efficiency and Environmental Impact of Agribots in Modern Farming. Chapter 8. Impact of Agribots on Agricultural Productivity and Market Performance. Chapter 9. Smart Farming and Cyber Security: Safeguarding Agricultural Data and Infrastructure. Chapter 10. A Crop Cultivation Optimization Method Based on Digital Twins. Chapter 11. Revolutionizing Agriculture: Harnessing the Power of AI for Precision Farming and Sustainable Crop Management. Chapter 12. AgriSCM: A Blockchain-Powered Agricultural Supply Chain Management. Chapter 13. RoseLeafNet: A Novel Deep Learning Framework for Real-Time Detection and Classification of Rose Leaf Diseases Based on MobileNetV3

Über Autor(innen)

T. Chandrakumar is a Professor of Data Science in the Department of Applied Mathematics and Computational Science at Thiagarajar College of Engineering (TCE), Madurai, Tamil Nadu, India. His scholarly work spans diverse domains including agricultural informatics, biomedical image processing, enterprise systems, and intelligent computing, reflecting strong interdisciplinary expertise.

Bhisham Sharma is currently working at the Centre of Research Impact and Outcome (CRIO), Chitkara University, Punjab, India. His research interests span Wireless Communication, Wireless Sensor Networks, Wireless Mesh Networks, Next Generation Networking, Network Security, Internet of Things, UAV, Medical Image Processing, Artificial Intelligence, Deep Learning, Machine Learning, and Edge/Fog Computing.

Subrata Chowdhury is an Associate Professor in the Department of Computer Science and Engineering, Sri Venkateswara College of Engineering & Technology, Andhra Pradesh, India.

Khaled Rabie is currently a Distinguished Associate Professor with the Computer Engineering Department at King Fahd University of Petroleum & Minerals (KFUPM), Saudi Arabia. His current research interests focus on designing and developing next-generation wireless communication systems.