Big Data Analytics in Agriculture: Algorithms and Applications focuses on quantitative and qualitative assessment using state-of-the-art technology to provide practical improvements to agricultural production. The book provides a complete mapping—from data generation to storage to curation, processing and implementation/application—to produce high-quality reliable information for decision-making. It follows a logical pathway to demonstrate how data contributes to a converging flow of information towards a decision support system and how it can be transformed into actionable steps. The book develops ideas surrounding a strong integration of ICT and IoT to manage rural assets to deliver improved economic and environmental performance in a spatially and temporarily variable environment.
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. The efficient management of Big Data from Scalability and Cost Evaluation Perspective
4. The Approaches for the Big Data Processing: Applications and Challenges
Section III: Big Data Analytics Algorithms
5. Big Data Mining in real-time scenarios with limited resources and computational power
6. Big Data Analytics techniques comprising descriptive, predictive, prescriptive and preventive analytics with an emphasis on feature engineering and model fitting
Section IV: Big Data Applications
7. IoT foundations in Precision Agriculture and its Application.
8. Practical applications of Big Data-driven Smart farming
9. Practical applications of Smart & Precise irrigation
10. Weed or Disease Detection using AI/ML/Deep Learning techniques
11. Nutrient Stress Detection using AI/ML/Deep Learning techniques
12. Leaf Disease Detection using AI/ML/Deep Learning techniques
13. Efficient soil water management using AI/ML
14. Microclimatic Forecasting using AI/ML/Deep Learning techniques
15. AI/ML/Deep Learning techniques in precipitation forecast
16. Yield Prediction using AI/ML/Deep Learning techniques
17. Practical applications of Supply Chain Analytics in Agriculture
18. Efficient Farm Analytics using AI/ML/Deep Learning techniques
Section V: Challenges and prospects
19. Challenges and future pathway for big data analytics algorithms and applications in Agriculture
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