Soil and crop sensing is a fundamental component and the first important step in precision agriculture. Unless the level of soil and crop variability is known, appropriate management decisions cannot be made and implemented. In the last few decades, various ground-based sensors have been developed to measure spatial variability in soil properties and nutrients, crop growth and yield, and pest conditions. Remote sensing as an important data collection tool has been increasingly used to map soil and crop growth variability as spatial, spectral and temporal resolutions of image data have improved significantly in recent years. While identifying spatial variability of soil and crop growth within fields is an important first step towards precision management, using that variability to formulate variable rate application plans of farming inputs such as fertilizers and pesticides is another essential step in precision agriculture.The purpose of this book is to present the historical, current and future developments of soil and crop sensing technologies with fundamentals and practical examples. The first chapter gives an overview of soil and crop sensing technologies for precision crop production. The next six chapters provide details on theories, methods, practical applications, as well as challenges and future research needs for all aspects of soil and crop sensing. The last two chapters show how soil and crop sensing technologies can be used for plant phenotyping and precision fertilization. The chapters are written by some of the world’s leading experts who have contributed significantly to the developments of precision agriculture technologies, especially in the area of soil and crop sensing. They use their knowledge, experiences, and successful stories to present informative and up-to-date information on relevant topics. Therefore, this book is an invaluable addition to the literature and can be used as a reference by scientists, engineers, practitioners, and college students for the dissemination and advancement of precision agriculture technologies for practical applications.
Author(s): Minzan Li, Chenghai Yang, Qin Zhang
Series: Agriculture Automation and Control
Publisher: Springer
Year: 2022
Language: English
Pages: 330
City: Cham
Foreword
Contents
About the Editors
Contributors
Chapter 1: Soil and Crop Sensing for Precision Crop Production: An Introduction
1.1 History of Agriculture, from Agriculture 1.0 to Agriculture 4.0
1.2 Modern Agriculture Technologies
1.2.1 Precision Agriculture
1.2.2 Digital Agriculture
1.2.3 Smart Agriculture
1.3 New Challenges in Agriculture
1.3.1 IoT in Agriculture
1.3.2 Big Data in Agriculture
1.3.3 Cloud Computing in Agriculture
1.4 Overview of Soil and Crop Sensing Technologies for Precision Crop Production
1.4.1 Information of Soil and Crop for Precision Crop Production
1.4.2 Soil and Crop Sensing Technologies for Precision Crop Production
1.5 Summary
References
Chapter 2: Sensing Technology of Soil Physical Properties
2.1 Sensing Technology of Soil Texture
2.1.1 Introduction of Soil Texture
2.1.2 Sensing Technology for Soil Texture and Particle Size
2.2 Sensing Technology of Soil Water Content
2.2.1 Measurement of Soil Water Content with Dielectric Parameters
2.2.2 Measurement of Soil Water Content with Neutron Meter
2.2.3 Measurement of Soil Water Content with Near-Infrared Spectroscopy
2.3 Sensing Technology of Soil Porosity and Bulk Density
2.3.1 Introduction of Soil Porosity and Bulk Density
2.3.2 Sensing Instruments of Soil Porosity and Bulk Density
2.4 Sensing Technology of Soil Compaction
2.4.1 Measurement of Soil Compaction with Penetrometer
2.4.2 On-the-Go Measurement of Soil Compaction
2.5 Sensing Technology of Soil Cation Exchange Capacity
2.5.1 Introduction of Soil Cation Exchange Capacity
2.5.2 Sensing Method of Soil Cation Exchange Capacity
2.5.3 Sensing Technologies of Soil Cation Exchange Capacity
References
Chapter 3: Theories and Methods for Soil Nutrient Sensing
3.1 Laboratory Measurement of Soil Nutrients
3.1.1 Detection of Soil Nitrogen Content in Laboratory
3.1.2 Detection of Soil Phosphorus Content in Laboratory
3.1.3 Detection of Soil Kalium Content in Laboratory
3.1.4 Detection of Soil Organic Matter Content in Laboratory
3.2 Spectral Technology for Soil Nutrient Sensing
3.2.1 Vis/NIR Spectral Sensing Technology for Soil Nutrients
3.2.2 Mid-infrared Spectral Sensing Technology for Soil Nutrients
3.2.3 LIBS Sensing Technology for Soil Nutrients
3.2.4 Multispectral and Hyperspectral Imaging Sensing Technology for Soil Nutrients
3.3 Instruments of Soil Nutrient Detection
3.3.1 Portable Instruments of Soil Nutrient Detection
3.3.2 Airborne Equipment of Soil Nutrient Detection
3.3.3 Satellite-Based Equipment for Soil Nutrient Detection
3.3.4 Sensors in Internet of Things of Soil Nutrient Detection
3.4 Summary
References
Chapter 4: Application of Soil Sensing in Precision Agriculture
4.1 Tractor-Mounted Soil Analysis System Based on Vis-NIR Spectroscopy
4.1.1 Soil Sensing Instruments Based on Vis-NIR Spectroscopy
4.1.2 Tractor-Mounted Soil Analysis Systems
4.1.3 Soil Analysis System (SAS) Series
4.1.4 Analysis of Calibration Model for Multiple Soil Properties
4.2 Application of Tractor-Mounted Soil Analysis System in Precision Agriculture
4.2.1 Site-Specific Soil Mapping and Interpretation of Agricultural Fields
4.2.2 Decision-Making for Crop Precision Farming
4.3 Measurement and Application of Soil EC in Precision Agriculture
4.3.1 Soil EC Measurement: Theory and Method
4.3.2 On-the-Go Measurement System of Soil EC
4.3.3 Application of Soil EC in Precision Agriculture
References
Chapter 5: Theories and Methods for Spectroscopy-Based Crop Nutrient Sensing
5.1 Spectral Characteristics and Vegetation Indices of Crop Nutrients
5.1.1 Canopy Spectral Characteristics of Crop Nutrients
5.1.2 Vegetation Indices of Crop Nutrients
5.2 Estimation of Leaf Nitrogen Accumulation in Wheat Based on Hyperspectral Sensing
5.2.1 Analysis of Canopy Spectral Characteristics
5.2.2 Spectral Monitoring Models for Crop Nutrients
5.3 Real-Time Diagnosis of Crop Growth
5.3.1 Diagnosis of Crop Nutrient Status Based on Nutrient Balance Principle
5.3.2 Diagnosis of Crop Nutrient Status Based on Nitrogen Index Method
5.3.3 Diagnosis of Crop Nutrient Status Based on Indicator Difference Method
5.3.3.1 Dynamic Changes of Leaf Area Index
5.3.3.2 Dynamic Changes of Spectral Index
5.4 Ground-Based, UAV-Borne, and Satellite Remote Sensing for Crop Nutrient Management
5.4.1 Ground-Based Monitoring Systems for Crop Nutrient Management
5.4.2 UAV-Borne Monitoring System for Crop Nutrient Management
5.4.3 Satellite-Based Remote Sensing Systems for Crop Nutrient Management
References
Chapter 6: Remote Sensing Technologies for Crop Disease and Pest Detection
6.1 Introduction
6.2 Remote Sensing Platforms and Systems for Disease and Pest Detection
6.2.1 Satellite Sensors for Disease and Pest Detection
6.2.2 Manned Aircraft-Based Imaging Systems for Disease and Pest Detection
6.2.2.1 Multispectral Imaging Systems Based on Industrial Cameras
6.2.2.2 Multispectral Imaging Systems Based on Consumer-Grade Cameras
6.2.2.3 Hyperspectral Cameras
6.2.3 Unmanned Aircraft-Based Imaging Systems for Disease and Pest Detection
6.3 Practical Methodologies for Crop Disease Detection and Management
6.3.1 Image Selection and Acquisition for Crop Disease Detection
6.3.2 Image Processing and Prescription Map Creation for Crop Disease Management
6.3.3 Site-Specific Fungicide Application for Crop Disease Management
6.3.4 Performance and Efficacy Evaluation for Crop Disease Management
6.4 Challenges and Future Development
References
Chapter 7: Plant Phenotyping
7.1 Introduction
7.2 Sensing Instruments for Plant Phenotyping
7.2.1 Overview
7.2.2 Sensing Instrumentation for Plant Canopy
7.2.2.1 Plant Height Measurement
Plant Height Measurement Based on Stereo Vision Systems
Plant Height Measurement Based on Lidar Sensors
Plant Height Measurement Based on Ultrasonic Sensors
Plant Height Measurement Based on Range Cameras
Comparison and Analysis of Plant Height Measurements
7.2.2.2 Leaf Parameter Measurements
Leaf Parameter Measurement Using Color Digital Cameras and Stereo Vision System
Leaf Parameter Measurement Using Range Cameras
Leaf Parameter Measurement Using Spectral Sensors and Cameras
Leaf Parameter Measurement Using Lidar/Laser Sensors
Comparison and Analysis of Leaf Parameter Measurements
7.2.2.3 Chlorophyll Measurements
Chlorophyll Measurement Based on Spectral Sensors
Chlorophyll Measurement Based on Spectral Cameras
Chlorophyll Measurement Based on Fluorescence Sensors
Chlorophyll Measurement Based on Lidar/Laser Sensors
Comparison and Analysis of Chlorophyll Measurements
7.2.2.4 Water Stress Measurements
Water Stress Measurement Based on Thermometers
Water Stress Measurement Based on Thermography
Water Stress Measurement Based on Spectral Sensors and Cameras
Comparison and Analysis of Water Stress Measurements
7.2.3 Sensing Instruments for Biomass
7.2.3.1 Biomass Measurement Using the Nonspectral Method
7.2.3.2 Biomass Measurement Using the Spectral Method
7.2.3.3 Combined Method for Biomass Measurement
7.2.3.4 Comparison and Analysis of Biomass Measurements
7.2.4 Sensing Instrument for Plant Roots
7.2.4.1 Plant Root Measurement Based on Color Digital Cameras
7.2.4.2 Plant Root Measurement Based on X-Ray CT
7.2.4.3 Plant Root Measurement Using Other Sensing Techniques
7.2.4.4 Comparison and Analysis of Plant Root Measurements
7.3 Platforms for Plant Phenotyping
7.3.1 Ground-Based Platforms
7.3.1.1 In-Field Sensor Networks
7.3.1.2 Ground Mobile Platforms
7.3.2 Aerial Platforms
7.3.2.1 Unmanned Aerial Vehicles (UAVs)
7.3.2.2 Blimps
7.3.2.3 Manned Aerial Vehicles
7.3.2.4 Satellites
7.3.3 Indoor Platforms
7.3.3.1 Tissue-Level Platforms
7.3.3.2 Organ-/Single Plant-Level Platforms
7.3.3.3 Group-Level Platforms
7.4 Data Analytics for Plant Phenotyping
7.4.1 Data Preprocessing
7.4.1.1 Overview
7.4.1.2 Image Preprocessing
7.4.1.3 Point Cloud Preprocessing
7.4.2 Traditional Statistical Analysis
7.4.3 Machine Learning and Deep Learning
7.4.3.1 Machine Learning
7.4.3.2 Deep Learning
7.5 Summary
References
Chapter 8: Crop Sensing in Precision Agriculture
8.1 Introduction
8.2 Spectroscopy-Based Sensing Instruments for Crop Monitoring
8.2.1 Foundation of Spectral Sensing and Vegetation Indices in Crop Sensing
8.2.2 Spectral Sensing in Crop Monitoring
8.2.2.1 Hyper-Spectrometers for Crop Sensing
8.2.2.2 Portable Sensors Used in Crop Monitoring
Portable Sensors for Leaf Measurement
Portable Sensors for Canopy Measurement
8.2.3 Development of Spectroscopy-Based Systems for Crop Detection
8.2.3.1 Development of Hyperspectral Sensors for Crop Monitoring
8.2.3.2 WSN-Based Sensors for Crop Monitoring
8.2.3.3 An Integrated Sensor Based on Spectroscopy and Imagery
8.3 Image Sensing for Crop Detection
8.3.1 Foundation of Crop Imaging and Feature Extraction
8.3.2 Imaging Technologies Used in Crop Detection
8.3.3 Development of Imaging Systems for Crop Detection
8.3.3.1 A Two-CCD-Based Imaging System for Crop Measurement
8.3.3.2 A Portable Binocular Sensor for Crop Monitoring
8.3.3.3 A Portable Multispectral Sensor for Crop Measurement
8.4 Remote Sensing Platforms for Crop Monitoring
8.4.1 Remote Sensing Instruments Used in Crop Monitoring
8.4.2 Application of Multispectral Remote Sensing
8.4.3 Application of Hyperspectral Remote Sensing
8.5 Precision Crop Management Based on Sensing Instruments
8.5.1 Applications of Spectroscopy-Based Crop Sensors
8.5.1.1 Classification of Weeds and Damage Caused by Disease and Pests
8.5.1.2 Monitoring of Nutrient Content and Biomass Status
8.5.2 Applications of Imaging-Based Crop Sensors
8.5.2.1 Application of Ground-Based Imaging Instruments
Classification of Crops and Weeds
Identification of Specialty Fruits
Measurement of Crop Growth Status
8.5.2.2 Application of UAV-Based Imaging Instruments
Crop Classification
Crop Detection
8.5.3 Variable-Rate Fertilizer Management Based on Crop Sensors
8.5.3.1 Variable-Rate Fertilizer Mapping Based on Imaging Instruments
8.5.3.2 Variable-Rate Fertilizer Control Based on Crop Sensing
References
Chapter 9: Perspectives of Soil and Crop Sensing in Smart Agriculture
9.1 Opportunities and Challenges of Sensing Technology for Crops and Soil
9.1.1 Limitations of Sensing Technology for Crops and Soil at Present
9.1.2 Indicators Perceived in Smart Agriculture
9.1.3 From Field to Satellite Observation
9.2 Inspiration of Advanced Technology for Crop and Soil Sensing
9.2.1 Electrochemical Sensors
9.2.2 Optical Sensors
9.2.3 Other Advanced Sensors
9.2.4 Lessons from Other Platforms and Missions
9.3 Prospects for Crop Sensor Technologies in Smart Agriculture
9.3.1 Crop Phenomics Sensor Technologies
9.3.2 Sensing for Crop Nutritional Status
9.3.3 Sensing for Crop Pests
9.3.4 Remote Sensing for Crops
9.4 Prospects for Soil Sensing Technology in Smart Agriculture
9.4.1 Soil Nutrient Sensors
9.4.2 Soil Heavy Metal Sensors
9.4.3 Soil Physical Parameter Sensors
9.4.4 Prospective of Soil Sensing by Remote Sensing
References
Index
