Computational Techniques in Precision Agriculture

Advances and Applications
1. Auflage 2026
ISBN: 978-1-032-85977-4
Verlag: Taylor & Francis Ltd

Buch, Englisch, 328 Seiten, Format (B × H): 178 mm x 254 mm

Reihe: Computational and Intelligent Systems

This book discusses the components and foundations of precision agriculture, followed by smart farming practices, sustainable development in precision agriculture, and use of artificial intelligence techniques in the field of agriculture.

Features:

- Discusses remote sensing techniques, global positioning system, robotics, and unmanned aerial vehicles in precision agriculture.

- Covers sustainable development in precision agriculture, data management and analytics, and variable rate technology.

- Highlights the implementation of artificial intelligence techniques, and use of prediction algorithms in improving crop efficiency.

- Presents machine learning, deep learning, and computer vision applications in precision agriculture.

- Showcases case studies on cotton crops, tea pests, and crop recommendations.

The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, smart grids, environmental engineering, agricultural engineering, computer science and engineering.

Zielgruppe

Academic, Postgraduate, and Undergraduate Advanced

Autoren/Hrsg.

Batra, Bhoomika

Bhatnagar, Vaibhav

Kumar Shukla, Vinod

Fachgebiete

• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
• Mathematik | Informatik EDV | Informatik Technische Informatik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
• Naturwissenschaften Agrarwissenschaften Agrarwissenschaften Nachhaltige Landwirtschaft

Inhaltsverzeichnis

Part I: Exploring Precision Agriculture. 1. Precision Agriculture: Leveraging Technology for Efficient Farming. 2. Automated Irrigation Systems Using IoT and AI: A Smart Solution for Water Conservation. 3. Drones in Precision Agriculture: Aerial Imaging and Real-Time Data Analytics for Sustainable Farming. 4. Integrating AI and IoT in Precision Agriculture: A Pathway to Smart Farming. 5. Remote Sensing and GIS for Precision Agriculture: Enhancing Crop Monitoring and Yield Prediction. Part II: Precision Agriculture and Machine Learning. 6. Precision Agriculture Meets Machine Learning: Predicting Crop Yield Using Random Forest Regression. 7. Optimizing Crop Classification Using K-Nearest Neighbours: A Data-Driven Approach Based on Climatic and Agricultural Factors. 8. The Role of Machine Learning in Precision Agriculture: Optimizing Resource Use and Productivity. 9. Predictive Crop Yield Analysis based on Weather and Soil Data for Agriculture Optimization. 10. Precision Agriculture in India: Evaluating the Role of Environmental Factors in Crop Selection Using Machine Learning. 11. The Impact of Precision Agriculture on Agricultural Exports: A BRICS Panel Data Analysis. Part III: Precision Agriculture, Technology, Integration and Food Security. 12. Blockchain for Precision Agriculture: Enhancing Transparency and Traceability in Agri-Supply Chains. 13. Blockchain-Powered IOT for Real Time Agriculture Monitoring and Prediction. 14. Big Data Analytics in Precision Agriculture: Improving Decision-Making for Farm Management. 15. Climate-Smart Agriculture: Leveraging Precision Farming Technologies for Sustainable Food Security. 16. The Impact of Variable Rate Technology (VRT) on Soil Health and Crop Yield Optimization. Part IV: Precision Agriculture: Future Direction. 17. Advancing Agricultural Efficiency: An Exploration of Smart Predicting Crop Practices. 18. Digital Innovations in Agriculture: The Socio-Economic Impact of Smart Farming and Renewable Energy Integration. 19. Edge Computing for Real-Time Precision Agriculture Applications: Challenges and Opportunities. 20. Precision Agriculture as a Tool for Solving Major Global and Regional Problems. 21. Smart Soil Systems: Bridging Soil Classification, Precision Farming, and Digital Agriculture in India. 22. Planting the Future: Traversing the Digital Landscape of Intelligent Farming.

Über Autor(innen)

Bhoomika Batra is a founder of a research and data analytics academy called EdBhoomi Learning, and a visiting professor, at Alliance University and Wellingkar Institute of Management, Bengaluru, India. Her research interests are in the areas of agriculture, blockchain, neural networks, finance, corporate governance, and digitalization. She is an expert on data analysis and predictive analytics having over three years of experience using tools like IBM SPSS, E-views, STATA, SMART_PLS.

Vinod Kumar Shukla is an associate professor and head, in the School of Engineering Architecture Interior Design, at Amity University, Dubai, UAE. He completed his PhD in the field of Semantic Web and Ontology. He is an active member of IEEE and has published research papers in various reputed journals and conferences. He holds international patents in the broader field of information technology. His areas of research include the Internet of Things, semantic web, computer networking, and network security.

Vaibhav Bhatnagar is an assistant professor in the Department of Computer Applications, at Manipal University Jaipur, India. He has published research articles in journals indexed in Scopus and Web of Science. His areas of research include fog computing, internet of things, data analytics, and machine learning.