Big Data Analytics in Agriculture

Algorithms and Applications
Erscheinungsjahr 2027
ISBN: 978-0-323-99932-8
Verlag: William Andrew Publishing

Buch, Englisch, 350 Seiten, Format (B × H): 191 mm x 235 mm

Big Data Analytics in Agriculture focuses on the quantitative and qualitative assessment of agricultural systems using state-of-the-art technologies to deliver practical improvements in agricultural production.

Addressing the challenge of translating data into real-world applications, the book provides a comprehensive mapping of the entire data lifecycle—from data generation, storage, and curation to processing and implementation. It guides readers through the steps required to produce high-quality, reliable information that supports effective decision-making. Following a logical progression, the volume demonstrates how diverse data streams converge into decision-support systems and how they can be transformed into actionable outcomes, aligned with intelligent, efficient, technologically advanced, economically viable, and politically and culturally sustainable practices.

This book further explores the integration of information and communication technologies (ICT) and the Internet of Things (IoT) for managing rural assets and enhancing economic and environmental performance in spatially and temporally variable agricultural environments. Topics covered include big data analytics, data management and processing, and a range of algorithms and applications relevant to agriculture. Subtopics encompass artificial intelligence- and machine-learning-enabled smart and precision irrigation, disease and pest management, microclimatic forecasting, preventive fertigation and chemigation, data-driven smart farming through the Internet of Everything (IoE), and supply-chain analytics for improved farm-level operations.

Zielgruppe

Postgraduate students, PhD Research Scholars, Scientists, Academicians, Geospatial Experts, Modellers, Agricultural Scientists, Remote Sensing and Computer Science Professionals, IT Professionals, Management Firms, Computing Experts and any other field related to this.

Autoren/Hrsg.

Pandey, Manish K.

Srivastava, Prashant K.

Pradhan, Biswajeet

Fachgebiete

• Wirtschaftswissenschaften Volkswirtschaftslehre Internationale Wirtschaft Entwicklungsökonomie & Emerging Markets
• Naturwissenschaften Agrarwissenschaften Agrarwissenschaften
• Naturwissenschaften Agrarwissenschaften Ackerbaukunde, Pflanzenbau
• Wirtschaftswissenschaften Wirtschaftssektoren & Branchen Primärer Sektor Agrarökonomie, Ernährungswirtschaft
• Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware
• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung

Inhaltsverzeichnis

Section I: Introduction to Data Analytics and Big Data
1. Introduction to Traditional Data Analytics
2. Introduction to Big Data and Big Data Analytics
Section II: Big Data Management and Processing
3. Approaches for Big Data Processing: Applications and Challenges
4. Agricultural Big Data: Storage, Loading, and Application Development
5. Data Analysis Techniques for Agricultural-Based Multi Corpus: Scalability and Cost Perspectives
Section III: Big Data Analytics Algorithms
6. From Theory to Practice: The Application of Big Data and Machine Learning in Real-World Scenarios
7. Feature Engineering and Model Fitting for Efficient Big Data Analytics
8. Review on the Increasing Role of Artificial Intelligence/Machine Learning in Climate Prediction
9. Variability and Trend Analysis of Rainfall Using ARIMA and Holt-Winters Techniques
Section IV: Big Data Applications
10. Data-Driven Approaches and AI Applications in Managing Variability for Sustainable Crop Production
11. Big Data-Driven Smart Farming: A Visualization Perspective of Tracking Agricultural Productivity
12. Impact Assessment of Climate Change Through Agricultural Big Data with Emphasis on Smart Agriculture
13. Smart and Precise Irrigation: A Way Forward
14. Application of Mobile Collaborative Robot Using Deep Learning in Precision Weed Control of Large Farms: A Brief Review
15. Machine Learning Enabled Nutrient Stress Detection and Real-Time Prediction
16. Performance Evaluation of Machine Learning Algorithms for Leaf Disease Detection
17. AI-Driven Smart Agriculture System for Multi-Crop Disease Detection: A Study on Potato, Tomato and Bell Pepper
18. Rice Pest Detection Using YOLO Machine Learning
19. High Resolution Mapping of Paddy False Smut Disease Using UAV–Drone and Machine Learning Algorithms
20. Soil Moisture Estimation Through Machine Learning and Polarimetric Synthetic Aperture Radar Data Over High Altitude Agroforestry Landscapes
21. Practical Applications of Supply Chain Analytics in Agriculture
22. Harnessing Big Data for Agricultural Transformation in Developing Economies: Origins, Applications, and Impacts on Farmers
Section V: Challenges and Future Directions
23. Challenges and Future Pathways for Big Data Analytics in Agriculture from an Algorithmic and Applications Perspective

Über Autor(innen)

Pandey, Manish K.
Dr. Manish K Pandey, a Research Associate Professor at MURC, Marwadi University, Rajkot, Gujarat, India, is passionate about blending geomorphology with the latest advances in artificial intelligence, remote sensing, and geographic information systems. He completed his undergraduate and postgraduate studies in Geography with specialisation in remote sensing and GIS at the University of Allahabad and earned his Ph.D. from Banaras Hindu University in fluvial geomorphology with support from prestigious CSIR research grants. With more than a decade of research experience, Dr. Pandey investigates earth surface processes, such as flooding, landslides, and groundwater dynamics, using cutting-edge AI techniques. Skilled in cartography and GIS software like ArcGIS and QGIS, and MATLAB- and Python-based GeoAI, he has also broadened his expertise into glaciology through training with the Geological Survey of India and ISRO. Above all, Dr. Pandey is dedicated to advancing GeoAI as a powerful tool for modeling and understanding the natural world.

Srivastava, Prashant K.
Prashant K. Srivastava is working at IESD, Banaras Hindu University, as a faculty and was affiliated with Hydrological Sciences, NASA Goddard Space Flight Center, as research scientist on SMAP satellite soil mois ture retrieval algorithm development, instrumentation, and simulation for various applications. He received his PhD degree from the Department of Civil Engineering, University of Bristol, Bristol, United Kingdom. Prashant was the recipient of several awards such as NASA Fellowship, USA; University of Maryland Fellowship, USA; Commonwealth Fellowship, UK; Early Career Research Award (ECRA, DST, India), CSIR, as well as UGC JRF-NET (2005, 2006). He is leading a number of projects funded from reputed agencies in India as well as world. He was also a collaborator with NASA JPL on SMAP soil mois ture calibration and validation as well as Scatsat-1, NISAR, AVIRIS-NG missions of India. Prashant made more than 200+ publications in peer-reviewed journals and published 14 books with reputed publishing house such as Springer, Taylor and Francis, AGU-Wiley, and Elsevier, and several book chapters with good cita tion index. He presented his work in several conferences and workshops and is acting as a convener for the last few years in EGU, Hydroinformatics (HIC), and other conferences. He is also acting as Regional Editor Asia-Geocarto International (T & F), Associate Editor-Journal of Hydrology (Elsevier), GIScience and Remote Sensing (T & F), Remote Sensing Applications: Society and Environment (Elsevier), Sustainable Environment (T & F), Water Resources Management (Springer), Frontiers Remote Sensing, Associate Editor- Remote Sensing-MDPI, Associate Editor- Environment, Development and Sustainability (Springer), Environmental Processes (Springer), Bull of Env and Sci Res.

Pradhan, Biswajeet
Professor Pradhan is a globally recognized expert in geospatial analytics and artificial intelligence applications in Earth and environmental sciences. Currently a Distinguished Professor at the University of Technology Sydney (UTS), Australia, he also leads the Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS). With a PhD in GIS-based modeling, Prof. Pradhan has over two decades of experience in spatial data science, remote sensing, natural hazard modeling, and environmental monitoring. He has been listed among the world's top 2% scientists by Stanford University and received numerous international awards, including from IEEE and Elsevier. A Fellow of the Royal Geographical Society (FRGS), he also serves on editorial boards of several top-tier journals. His research integrates geospatial AI and deep learning for disaster risk reduction, land use planning, and sustainability.