Agri-Tech Approaches for Nutrients and Irrigation Water Management

Shivam Gupta, Sushil Kumar Himanshu, Pankaj Kumar Gupta

• Editorial: CRC Press
• Año: 2024
• ISBN:
  9781040021408
• Edición: 1
• Categoría: Tecnología e ingeniería
• Categoría: Gestión medioambiental

This book includes concepts, methodologies, and techniques used in soil nutrients and irrigation water management with regional and global prospects. This book accommodates up-to-date approaches to agricultural technologies along with future directions and compiles a wide range of articles ranging from soil moisture flow, nutrient dynamics, crop water estimation techniques, approaches to improve crop water productivity and soil health, crop simulation modeling, and remote sensing/GIS applications. The book also includes chapters on climate-resilient agriculture, advances in big data and machine-learning techniques, IoT, plasma technology, seed priming, and precision farming techniques and their environmental/economic impacts.

Features:

• Discusses applications sustainable technologies for soil nutrients and irrigation water management at multi-scale.
• Covers application of remote sensing/GIS, big data and machine learning, IoT, plasma technology, seed priming, and precision farming techniques for nutrients and water management.
• Reviews concepts, methodologies, and techniques being used in soil nutrients and irrigation water management.
• Provides up-to-date information as well as future directions in the field of nutrients and agricultural water management.

This book is aimed at researchers and graduate students in agriculture, water resources, environment, and irrigation engineering.

Índice

• Cover
• Half-Title
• Title
• Copyright
• Contents
• Preface
• Editors
• Contributors
• Chapter 1 AI for Sustainable Agriculture in the Face of Climate Change
• Chapter 2 Development of a Modeling Approach for Agriculture Crop Type Classification Aiming at Large-Scale Precision Agriculture by Synergistic Utilization of Fused Sentinel-1 and Sentinel-2 Datasets with UAV Datasets
• Chapter 3 IoT and Smart Sensor Applications in Nutrient and Irrigation Water Management
• Chapter 4 Assessing Nutrient Variability Across Irrigation Management Zones Using Unsupervised Learning and Mixed Models
• Chapter 5 Comparative Study on Estimation of Sediment Yield Index Using GIS and Remote Sensing for Soil Erosion Prediction
• Chapter 6 Soil Moisture Flows Modeling for Micro-Irrigation and Nutrient Load Management
• Chapter 7 Applications of Remote Sensing and GIS Techniques in the Monitoring of Ecosystem Services
• Chapter 8 Evaluation of Machine Learning Algorithms in Soil Water Content Prediction at Multiple Depths
• Chapter 9 Artificial Intelligence Application in Database Management for SCADA Systems
• Chapter 10 Entropy-Weighted-Multi-Criteria Decision-Making (E-MCDM) Approach for Erosion Area Prioritization: Case Study of a Himalayan River Basin
• Chapter 11 Role of Urea Super Granule (USG) Applicator in Efficient Management of Nitrogen Fertilizer: Case Study of Rice Farming in Bangladesh
• Chapter 12 AI as Improved Agri-Tech Approach for Better Nutrients and Proper Irrigation Water Management: A Comparative Study
• Chapter 13 Plasma Technology: An Emerging Tool for Sustainable Agriculture
• Chapter 14 Application of Metaheuristic Optimizations for Unconfined Aquifer Parameter Estimation to Improve the Irrigation Water Management
• Chapter 15 Application of Remote Sensing, GIS, and AI Techniques in the Agricultural Sector
• Chapter 16 Flood Modeling Using the AHP Method in a GIS Environment of the Iril River Catchment, Manipur, India
• Chapter 17 Agricultural Drought Modelling Through Drought Indices in the Thoubal District, Manipur, India
• Chapter 18 Seed Priming: Potential Nutrient Management Tool for Improving Crop Productivity Under Abiotic Stress
• Chapter 19 Protected Cultivation: Microclimate-Based Agriculture Under Greenhouse
• Chapter 20 Estimation of Groundwater Fluctuation and NDVI Using Geospatial Techniques for Chandigarh City, India
• Chapter 21 Holistic Approach for Prediction of Total Nitrogen Based on Machine-Learning Techniques
• Chapter 22 Resource Conservation Technologies for Sustainable Management of Soil, Water and Energy in Modern Agriculture
• Appendix
• Index