Big Data Analytics in Agriculture: Algorithms and Applications

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. The book follows a logical pathway to demonstrate how data contribute 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. Examines core research issues from different perspectives such as storage, handling, management, processing, and applications within an agricultural framework Offers novel research and applications along with the computational tools and techniques in development Develops a strong integration of ICT and IoT for managing rural assets to deliver improved economic and environmental performance INDICE: Section 1: Introduction to Big Data Analytics in Agriculture1. Introduction to Traditional Data Analytics2. Introduction to Big Data and Big Data AnalyticsSection II: Big Data Management and Processing3. The efficient management of Big Data from Scalability and Cost Evaluation Perspective4. The Approaches for the Big Data Processing: Applications and ChallengesSection III: Big Data Analytics Algorithms5. Big Data Mining in real-time scenarios with limited resources and computational power6. Big Data Analytics techniques comprising descriptive, predictive, prescriptive and preventive analytics with an emphasis on feature engineering and model fittingSection IV: Big Data Applications7. IoT foundations in Precision Agriculture and its Application.8. Practical applications of Big Data-driven Smart farming 9. Practical applications of Smart & Precise irrigation10. Weed or Disease Detection using AI/ML/Deep Learning techniques11. Nutrient Stress Detection using AI/ML/Deep Learning techniques12. Leaf Disease Detection using AI/ML/Deep Learning techniques13. Efficient soil water management using AI/ML14. Microclimatic Forecasting using AI/ML/Deep Learning techniques15. AI/ML/Deep Learning techniques in precipitation forecast16. Yield Prediction using AI/ML/Deep Learning techniques17. Practical applications of Supply Chain Analytics in Agriculture18. Efficient Farm Analytics using AI/ML/Deep Learning techniquesSection V: Challenges and prospects19. Challenges and future pathway for big data analytics algorithms and applications in Agriculture

• ISBN: 978-0-323-99932-8
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 350
• Fecha Publicación: 01/05/2023
• Nº Volúmenes: 1
• Idioma: Inglés