Big Data Analytics in Agriculture by Pandey, Manish K.; Srivastava, Prashant K.; Kumar Mall, Rajesh;

Long description:

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.
The 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.

• Introduces readers to the evolution from conventional analytical approaches to large-scale analytical paradigms through a stepwise, sectioned framework that supports progressive skill development
• Examines practical considerations surrounding storage architectures, multi-source datasets, scalability constraints, and cost trade-offs in agricultural computing environments
• Details algorithm-centric workflows, including feature construction, model calibration, and comparative performance evaluation across diverse agricultural contexts
• Showcases a wide range of sector-specific implementations, covering crop monitoring, irrigation planning, weed control, nutrient stress identification, climate assessment, pest detection, and agri-food supply-chain operations in both advanced and resource-limited settings

Table of Contents:

Section 1: Introduction to Big Data Analytics in Agriculture
1. Introduction to Traditional Data Analytics
2. Introduction to Big Data and Big Data Analytics
Section II: Big Data Management and Processing
3. Agricultural Big Data - Storage, Loading, and Application Development
4. Data Analysis Techniques for Agricultural-based Multi corpus: Scalability and Cost Perspective
5. Approaches for Big Data Processing: Applications and Challenges
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
Section IV: Big Data Applications
8. Data-Driven Approaches and AI Applications in Managing Variability for Sustainable Crop Production
9. Big Data-Driven Smart Farming: A Visualization Perspective of tracking Agricultural Productivity
10. Smart and Precise Irrigation: A Way Forward
11. Application of Mobile Collaborative Robot using Deep Learning in Precision Weed Control of Large Farms - A Brief Review
12. Machine Learning Enabled Nutrient Stress Detection and Real time Prediction
13. Performance Evaluation of Machine Learning Algorithms for Leaf Disease Detection
14. AI-Driven Smart Agriculture System for Multi-Crop Disease Detection: A Study on Potato, Tomato and Bell Pepper
15. Soil moisture estimation through machine learning and polarimetric Synthetic Aperture Radar data over high altitude agroforestry landscapes
16. Review on the increasing role of Artificial Intelligence / Machine Learning in climate prediction
17. Impact Assessment of Climate Change Through Agricultural Big Data with Emphasis on Smart Agriculture
18. Rice Pest Detection using YOLO Machine Learning
19. Practical applications of Supply Chain Analytics in Agriculture
20. Harnessing Big Data for Agricultural Transformation in Developing Economies: Origins, Applications, and Impacts on Farmers
Section V: Challenges and prospects
21. Challenges and Future Pathways for Big Data Analytics in Agriculture from an Algorithmic and Applications Perspective