Smart Farm Management Systems

Integrating Iot with Big Data Analytics
1. Auflage 2026
ISBN: 978-1-77964-727-6
Verlag: Apple Academic Press

Buch, Englisch, 294 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 562 g

The rapid evolution of digital technology has transformed agriculture from a reliance on intuition and tradition to a data-driven, precise, and sustainable sector, often referred to as Agriculture 4.0. By early 2026, the integration of AI, IoT, and robotics has become foundational rather than a "nice-to-have" for optimizing productivity, reducing waste, and mitigating environmental impact. The farms of today are not only led in conventional ways but are becoming empowered in terms of smart systems, data-driven information, and interconnected devices.

This new volume, Smart Farm Management Systems: Integrating IoT with Big Data Analytics, provides a clear understanding of how the new technologies can transform agricultural operations, increase the farm productivity, and enhance the sustainability of farming processes. It offers an abundance of real-life practical solutions to farm management challenges. With the combination of IoT-based sensing technologies and the enormous potential of big data analytics, contemporary farms will be able to attain a greater level of precision, transparency, and efficiency in its operations.

The book first discusses the challenges of integrating Agriculture 4.0 technologies and then proceeds to explore the myriad uses of smart technologies, such as artificial intelligence, Internet of Things, CNN-based deep learning, big data, chatbots, etc., for a variety of farm-related management issues. These include for green energy generation; for agricultural waste management; for plant disease detection and diagnosis; for security and real-time surveillance threat detection; for data-driven decision making and statistical hypothesis testing; for scheduling of irrigation, fertilization, etc.; for multi-language communication; to estimate and measure yield and field problems; and much more.

This volume helps to meet the growing world demand for efficient food production by offering smart farming solutions to the problems of climate change, resources optimization, and monitoring of crop health. Providing diverse perspectives, new research, and practical applications, this volume will prove to be an important resource for students, academicians, researchers, farmers, agritech professionals, and policymakers who are interested to unravel the transformational role of technology in agriculture.

Zielgruppe

Academic and Postgraduate

Autoren/Hrsg.

Kathole, Atul B.

Vhatkar, Kapil Netaji

Kimbahune, Vinod V.

Wadhwa, Lalit Kumar

Fachgebiete

• Naturwissenschaften Agrarwissenschaften Agrarwissenschaften Nachhaltige Landwirtschaft
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik

Inhaltsverzeichnis

Preface 1. Challenges in Achieving Compatibility While Integrating Agriculture 4.0 Technologies 2. Sustainable Farming in the Digital Ara: AI and IoT Technologies Transforming Agriculture 3. An Intelligent IoT System for Agricultural Waste Management and Green Energy Generation 4. Adaptive Hybrid Segmentation and Attention-Driven Residual ShuffleNet to Enhance the Detection of Tomato Leaf Disease 5. IoT-Powered Cotton Disease Diagnosis: Enhancing Sustainability Through Smart Sensing 6. Smart Agriculture: Early Tomato Leaf Disease Detection Using CNN-Based Deep Learning 7. Performance Analysis of Routing Table Falsification in Mobile RPL-Based Internet of Things 8. Key Privacy Challenges and Security Imperatives for Next-Generation IoT Systems 9. Integrating Data-Driven Decision Making and Statistical Hypothesis Testing in Big Data Analytics for Smart Farming 10. Identifying and Classifying Pests in the Agricultural Sector Using a Metaheuristics Deep Learning Approach 11. Turbidimeter: Design and Development of a Cost-effective Greywater Turbidity Monitoring System Using Arduino Uno to Monitor Water Quality for Irrigation and Reuse 12. Real-Time Surveillance Threat Detection System in Farms: A Review of Technological Advancements 13. Enhancing Customer Support with Contextual, History-Aware, and Multilingual Conversational AI Chatbots for Farming 14. Object Detection and Counting Using the YOLOv8 Algorithm and OpenCV in Smart Farming 15. Smart Agriculture Plant Health Monitoring Using IoT

Über Autor(innen)

Atul B. Kathole, PhD, is serving as an Professor in the Department of Computer Engineering at ADYPU, Pune, India. He earned his MTech and PhD in Computer Science & Engineering and has also completed an MBA (IT). His teaching experience spans over a decade, and his research interests include Internet of Things (IoT), VANETs, network security, deep learning, and federated learning in cloud environments. He holds several patents and has published in areas such as IoT-enabled pest identification, intrusion detection, and cloud load-balancing.

Kapil Netaji Vhatkar, PhD, is an Associate Professor at the Symbiosis Skills and Professional University, Pune, India. He holds a PhD in Computer Engineering from Veermata Jijabai Technological Institute (VJTI), Mumbai. Prior to this, he had roles in industry as a network engineer and in academia for over14 years in teaching and guiding undergraduate students. His research areas are focused on optimization algorithms, resource allocation in cloud computing, deep learning, and networking. He has co-authored a work on IoT-based agriculture, cloud resource scheduling, and virus-host prediction using deep recurrent neural networks.

Vinod V. Kimbahune, PhD, serves as Professor and Head of the Department of Computer Engineering at the Dr. D. Y. Patil Institute of Technology, Pimpri, Pune, India. His academic credentials include a PhD in Computer Engineering, and he has published extensively in areas such as IoT, artificial intelligence, and machine learning applied to networking and smart systems. As Department Head, he also oversees departmental activities including placements, laboratories, student innovation, and research and industry linkages.

Lalit Kumar Wadhwa, PhD, a seasoned academic, is Professor at Dr. D. Y. Patil Institute of Technology, Pimpri, Pune, India. He completed his bachelor’s in Electronics & Telecommunication Engineering and ME in Electronics Engineering at Government College of Engineering, Pune. He earned his PhD from the Indian Institute of Technology (ISM), Dhanbad, India. With over 20 years in academia and research, he has authored books, filed patents, and contributed to institutional accreditation and academic policy development. His focus has been on innovative teaching-learning practices, student-centric leadership, and fostering industry-academic collaboration.