Unlocking the Secrets of Soil
Applying AI and Sensor Technologies for Sustainable Land Use
- 1st Edition - March 1, 2025
- Imprint: Elsevier
- Editors: David C. Weindorf, Somsubhra Chakraborty, Bin Li
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 9 8 7 9 - 0
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 9 8 8 0 - 6
Unlocking the Secrets of the Soil: Applying AI and Sensor Technologies for Sustainable Land Use is a comprehensive guide to the latest advances in soil characterization. This b… Read more
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Request a sales quoteUnlocking the Secrets of the Soil: Applying AI and Sensor Technologies for Sustainable Land Use is a comprehensive guide to the latest advances in soil characterization. This book explores the role of sensors and artificial intelligence in improving soil management practices and supporting sustainable land use. Through detailed descriptions of sensor and AI-based techniques for measuring physical, chemical, and biological soil properties, readers will gain a deep understanding of the tools and technologies available for soil characterization. The book also covers the latest machine learning algorithms and image processing for analyzing soil data and making informed decisions about land use. Unlocking the Secrets of the Soil is an essential resource for researchers, practitioners, and students interested in the intersection of AI and sensor technologies for soil management and sustainability.
- Provides an integration of AI and Sensor technologies
- Highlights the importance of sustainable land use and the role that modern technologies can play in achieving this goal
- Presents an interdisciplinary approach, drawing on expertise from various fields such as agriculture, environmental science, and computer science
Soil Scientists and Environmental Scientists
- Unlocking the Secrets of Soil
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Chapter 1 Introduction
- Abstract
- Keywords
- Definition of soil characterization
- Overview of the importance of sensor-based soil characterization
- Objectives of the book
- References
- Chapter 2 Fundamentals of soil characterization
- Abstract
- Keywords
- Introduction
- Soil physical properties
- Texture
- Bulk density/particle density
- Aggregate stability/compaction
- Soil solution/infiltration/percolation
- Soil mineralogy
- Soil chemical properties
- Soil pH
- Salinity
- Soil organic matter/soil organic carbon
- Cation exchange capacity
- Soil biological properties
- Soil biology
- Microbial community composition
- Other soil organic matter dynamics and pools
- Enzyme activities
- Soil atmosphere/gaseous exchange/soil respiration
- Soil sampling and preparation
- Traditional soil characterization techniques
- Field-based methods
- Lab-based methods
- Conclusions
- References
- Chapter 3 Sensor-based soil characterization techniques
- Abstract
- Keywords
- Introduction
- Proximal soil sensors
- Optical sensors
- Electrochemical sensors
- Mechanical sensors
- Electromagnetic sensors
- Acoustic sensors
- Pneumatic sensors
- Image sensors
- Remote sensing
- Spaceborne remote sensing
- Airborne remote sensing
- Ground-based remote sensors
- Optical remote sensing
- Radar imaging
- Application of artificial intelligence in soil sensors
- Machine learning
- Robots
- Soil sensor data fusion
- Spectral data management
- References
- Chapter 4 Machine learning and AI techniques
- Abstract:
- Keywords
- Introduction to machine learning and AI
- Types of ML and AI techniques
- Data preparation for ML and AI
- Big data manipulation
- Data processing techniques
- Model selection and validation
- Data visualization and interpretation
- Types of machine learning and AI techniques
- Linear regression
- Tree-based methods
- Neural networks
- Clustering methods
- Support vector machine
- Other techniques
- Data preparation for machine learning and AI
- Handling missing data
- Dimension reduction
- Handling imbalanced dataset
- Data normalization and outlier detection
- Featuring engineering
- Data splitting
- Big data manipulation
- Big data characteristics and challenges
- Big data ecosystem
- Big data tools
- Data processing techniques in machine learning and AI
- Data collection
- Data preprocessing
- Feature extraction
- Feature selection
- Data fusion
- Model selection
- Model training
- Model evaluation
- Postprocessing and interpretation
- Deployment and continuous monitoring
- Model selection and validation
- Candidate set
- Data analysis objective
- Model selection techniques
- Model validation
- Conclusions
- Data visualization and interpretation
- Data visualization
- Data abstraction
- Visual marks and channels
- Visual expressiveness and effectiveness principles
- Data visualization and interpretation
- Visualizations for regression methods
- Visualizations for tree-based methods
- Visualizations for neural networks
- Visualizations for clustering methods
- Visualizations for support vector machines
- Visualizations for fuzzy logic systems
- Visualizations for geostatistical methods
- References
- Chapter 5 Applications of machine learning and artificial intelligence in soil science
- Abstract
- Keywords
- Introduction
- Soil classification and mapping
- Local predictions of soil classes and properties
- Soil erosion prediction
- Other applications of machine learning to soil science
- Final considerations
- References
- Chapter 6 Image acquisition and processing techniques
- Abstract
- Keywords
- Introduction
- Image acquisition
- CCD
- CMOS
- CID
- Hyperspectral imaging
- Multispectral imaging
- Methods of image acquisition
- In situ methods
- Ex situ
- Image processing
- Image preprocessing
- Image segmentation
- Feature extraction
- Image analysis
- Image data management
- Conclusions
- References
- Chapter 7 Sensing and geotechnologies for soil characterization
- Abstract
- Keywords
- Introduction
- Geotechnologies and soil characterization
- The basis of soil sensing
- Emerging platforms and connection technology
- Soil sensing at the proximal scale—Some common sensors and emerging trends
- Soil sensing at remote scales—Recent examples of machine learning applications
- Hyperspectral soil sensing
- Data fusion
- Conclusions
- References
- Chapter 8 Applications of soil sensors
- Abstract
- Keywords
- The role of soil sensing in precision agriculture: Background and case studies on predicting key soil fertility attributes
- Case study 1: Mapping key soil fertility attributes in tropical soils using mobile vis-NIR spectroscopy
- Case study 2: Electromagnetic sensors measuring ECa and MSa for digital soil mapping
- AI system for soil sensing
- Final considerations
- References
- Chapter 9 Conclusion and future directions
- Abstract
- Keywords
- Summary of key points
- The soil science of the 21st century
- From laboratory to sensors
- Leveraging sensors outputs with AI
- Future directions
- Challenges and opportunities
- References
- Index
- Edition: 1
- Published: March 1, 2025
- Imprint: Elsevier
- No. of pages: 400
- Language: English
- Paperback ISBN: 9780443298790
- eBook ISBN: 9780443298806
DW
David C. Weindorf
Dr. Weindorf’s research interests are at the nexus of soil science and environmental quality assessment. He is an internationally recognized authority in proximal sensor characterization of soils with extensive work in hydrocarbon and heavy metal polluted soils. Over a 20+ year career, he has worked in 30 countries worldwide and serves on two Elsevier editorial boards (Pedosphere and Geoderma).
He has published >200 peer reviewed research papers with >6000 citations of his work. He is Fulbright Scholar, a Fulbright Specialist, and Fellow & Presidential Award winner in the Soil Science Society of America. He has offered invited testimony before the US Congress and regularly provides expert testimony in legal matters germane to environmental quality assessment.
Affiliations and expertise
Vice President for Research and Economic Development, Georgia Southern University, Statesboro, GA, USASC
Somsubhra Chakraborty
Dr. Chakraborty's research interests lie in proximal soil sensors in combination with data mining and machine learning, with a focus on developing scalable algorithms for rapidly and non-invasively predicting soil properties. In particular, he has worked on developing methods for sensors like portable XRF, diffuse reflectance spectroscopy, smartphone-based soil sensing, Nix color sensor, digital soil mapping, as well as techniques for dealing with real-time soil characterization.
He has published over 100 research articles in various international journals and conferences, and his work has been cited over 3,700 times. He has received several awards and honors for his contributions to the field of soil science, including the Australia awards fellowship and SPESS garden scholarship (USA). In addition to his research, Chakraborty is also actively involved in teaching and mentoring students at IIT Kharagpur. He has supervised several Ph.D. and M. Tech. students and has also been involved in the development of several online courses on soil science.
Affiliations and expertise
Associate Professor of Soil Science Indian Institute of Technology (IIT) Kharagpur, IndiaBL
Bin Li
Today, Dr. Li’s research is focused on statistics and machine learning. He has published >75 peer reviewed research papers with >1,300 citations of his work.
Affiliations and expertise
Department of Experimental Statistics Louisiana State University Baton Rouge, Louisiana, USARead Unlocking the Secrets of Soil on ScienceDirect