Preface
Acknowledgements
Contents
About the Editors
Contributors
Chapter 1: Introduction and Basic Sensing Concepts
1.1 Background
1.2 Purpose, Aims, Structure and Audience of this Book
1.3 Sensing Approaches
1.3.1 Remote Sensing Systems
1.3.2 Proximal Sensing Systems
1.4 Basic Sensing Concepts for Precision Agriculture
1.4.1 When Sensors Are Used in Precision Agriculture
1.4.2 How Sensors Are Used in Precision Agriculture
1.4.3 Sensing Resolutions
1.4.4 Global Navigation Satellite Systems
1.4.5 Concepts Related to Metrology
References
Chapter 2: Satellite Remote Sensing for Precision Agriculture
2.1 Introduction
2.2 Multispectral Satellites
2.2.1 Landsat 7 and 8
2.2.2 Ikonos 2
2.2.3 QuickBird
2.2.4 RapidEye
2.2.5 GeoEye-1
2.2.6 WorldView-2 and 3
2.2.7 Pleiades 1A and 1B
2.2.8 SPOT-6 and 7
2.2.9 ResourceSat 2
2.2.10 KompSat 3
2.2.11 Planet Lab Flock CubeSats
2.2.12 Sentinel-2A and 2B
2.3 Hyperspectral Satellites
2.3.1 Earth Observing (EO) 1 Hyperion
2.3.2 Proba 1 CHRIS
2.4 Sun-Induced Fluorescence Satellites
2.4.1 EnviSat Schiamachy
2.4.2 MetOp B Gome 2
2.4.3 GoSat TANSO FTS
2.4.4 Sentinel-5P TROPOMI
2.4.5 TanSat
2.5 Synthetic Aperture Radar Satellites
2.5.1 Japanese Earth Resources Satellite 1
2.5.2 Advanced Land Observing Satellites 1 and 2
2.5.3 Soil Moisture and Ocean Salinity
2.5.4 Soil Moisture Active Passive
2.5.5 European Remote Sensing 1 and 2
2.5.6 RadarSat 1 and 2
2.5.7 EnviSat
2.5.8 Sentinel-1A and -1B
2.5.9 Cosmo SkyMed
2.5.10 Terra SAR-X and TanDEM-X
2.6 Satellite-Based Digital Surface Model Products
2.6.1 Shuttle Radar Topography Mission
2.6.2 Advanced Spaceborne Thermal Emission and Reflection Radiometer
2.6.3 Panchromatic Remote-Sensing Instrument for Stereo Mapping
2.6.4 TanDEM-X
2.7 Conclusions for the Chapter
References
Chapter 3: Sensing Crop Geometry and Structure
3.1 Introduction
3.2 Photogrammetric Techniques
3.2.1 Stereo Vision
3.2.2 Structure from Motion and Multi-View Stereo
3.3 Ultrasonic Sensors
3.4 Optical Sensors
3.4.1 Photoelectric Sensors
3.4.2 LiDAR Sensors
3.5 Depth Cameras
3.5.1 Structured-Light Sensors
3.5.2 Time-of-Flight Cameras
3.5.3 Active Stereo Vision
3.6 Conclusions for the chapter
References
Chapter 4: Soil Sensing
4.1 Introduction
4.1.1 Proximal Soil Sensing Technologies
4.1.2 Topography
4.1.3 Geophysical Sensors
4.1.4 Spectral Sensors
4.1.5 Mechanistic Sensors
4.1.6 Electrochemical Sensors
4.1.7 Soil Sensor Data Interpretation
4.1.7.1 Sampling Design
4.1.7.2 Sensor Calibration and Validation
4.1.7.3 Sensor Data Fusion
4.1.8 Digital Soil Mapping
4.1.9 Management Zone Delineation
4.2 Introduction of Case Studies
4.2.1 Case Study 4.1. Comparison of Mapping Soil Properties in Canada Using a Multi-Sensor Platform and 1 ha Grid Sampling
4.2.1.1 Introduction
4.2.2 Materials and Methods
4.2.2.1 The PSS Field Survey
4.2.2.2 Soil Sampling
4.2.2.3 Quantitative Methods for Soil Mapping
4.2.2.4 Map Assessment
4.2.3 Results and Discussion
4.2.3.1 Description of Soil Properties and Soil Sensor Responses
4.2.3.2 Multiple Linear Regression Analysis
4.2.3.3 Quality of Soil Mapping
4.2.4 General Discussion
4.2.5 Conclusions
4.3 Case Study 4.2. Including Management as a Predictor Can Improve Subfield-Scale Digital Maps of Soil Chemical Properties
4.3.1 Introduction
4.3.2 Materials and Methods
4.3.3 Results and Discussion
4.3.4 Conclusions
4.4 Case Study 4.3. Validation of a NIR-Based Laboratory Soil Analysis Procedure for Less Expensive Farm-scale Soil Mapping
4.4.1 Introduction
4.4.2 Method
4.4.3 Results and Discussion
4.4.4 Conclusions
4.5 Case Study 4.4. Soil-Based Precision Fertilisation Using Near-Infrared spectroscopy for Optimising Cocoa Production
4.5.1 Introduction
4.5.2 Materials and Method
4.5.2.1 Soil Sampling and Analysis
4.5.2.2 Spectral Data Acquisition and Pre-Processing
4.5.2.3 Chemometric Modelling
4.5.2.4 Fertilizer Recommendation Using Soil Diagnosis Software
4.5.3 Results and Discussion
4.5.3.1 Descriptive Statistics
4.5.3.2 Prediction of Soil Properties
4.5.3.3 An Economically Viable Option for Measuring Soil Properties
4.5.3.4 Soil Fertilizer Recommendation
4.5.4 Conclusion
4.6 Conclusions for the Chapter
References
Chapter 5: Sensing with Wireless Sensor Networks
5.1 Concepts of Sensing with Wireless Sensor Networks
5.1.1 Introduction
5.1.2 Principles of Wireless Sensor Networks in Agriculture
5.1.2.1 Wireless Communication Standards and Protocols
5.1.2.2 Network Topologies in WSNs
5.2 Applications of WSNs in Agriculture
5.3 WSNs for Application of Inputs
5.3.1 Irrigation Applications
5.3.2 Fertilizer Applications
5.3.3 Pest Management Applications
5.4 WSNs for Crop Monitoring
5.5 The WSNs for Meteorology
5.6 Conclusions
References
Chapter 6: Sensing for Health, Vigour and Disease Detection in Row and Grain Crops
6.1 Introduction
6.2 Sensors for Decision Support Systems (DSS)
6.3 Crop Health and Vigour Related to Insect Damage and Disease
6.4 Nanosensors
6.5 Sensors for Crop Nutrient Management and Improvement of Crop Health and Vigour from Nutrient Deficiency
6.6 Indices Used to Identify Nutrient Deficiencies
6.7 Use of UAV Remote Sensing Platforms
6.8 Plant Health Assessment Through Soil Sensing
6.9 Conclusions for the Chapter
References
Chapter 7: On-Combine Sensing Techniques in Arable Crops
7.1 Introduction
7.2 On-Combine Sensing Systems
7.2.1 Sensing Grain Yield
7.2.2 Near-Infrared Spectroscopy of Grain Quality
7.2.3 Optical Sensing of Crop Density, Cover or Height
7.3 Applications of On-Combine Sensor Information
7.3.1 Precision Nitrogen Management
7.3.2 Grain Segregation by Protein on the Combine
7.3.3 Post-Harvest Evaluation of Crop Stress
7.3.4 Detecting and Mapping Weeds
7.3.5 Conclusions for the Chapter
References
Chapter 8: Sensing in Precision Horticulture
8.1 The Situation in Horticulture
8.2 Biotic and Abiotic Plant Stresses
8.2.1 Background
8.2.2 Remote Sensing for Stress Detection
8.2.3 Visible and NIR Bands
8.2.4 Mid-Infrared Bands
8.2.5 Fluorescence Spectroscopy
8.2.6 Laser-Induced Breakdown Spectroscopy
8.2.7 Thermal Bands
8.2.8 Ranging Sensors
8.3 Proximal fruit Sensing
8.3.1 Background
8.3.2 Maturity
8.3.3 Visible and NIR Bands
8.4 Data Analyses
8.5 Yield Estimation
8.5.1 Background
8.5.2 Weather Data
8.5.3 Remote Sensing
8.6 Harvesting
8.7 Conclusions for the Chapter
References
Chapter 9: Sensing from Unmanned Aerial Vehicles
9.1 Introduction
9.2 Platforms and Sensors
9.3 UAV Data Collection and Data Processing Process
9.3.1 Data Collection
9.3.2 Image Processing
9.3.3 UAV Example Imagery
9.4 Sample Applications of UAVs in Agriculture
9.5 Pros and Cons of UAVs in Agriculture
9.5.1 Changing Technologies and Software
9.5.2 Imaging Large Fields
9.6 Case Study: Application of UAV Images in Variable Side-Dress Fertilization of Winter Cereals
9.7 Conclusions for the Chapter
References
Chapter 10: Sensing for Weed Detection
10.1 Introduction
10.2 Types of Weed Detection Sensors
10.3 Sensor Platforms
10.4 Weed Detection and Management Systems
10.5 Image Analysis – From Sensing to Weed Detection
10.6 Vegetation Indices
10.7 Vegetation Cover Fraction
10.8 Object-Based Methods
10.9 Machine Learning, Convolutional Neural Networks and Deep Learning
10.10 Weed Detection Accuracy
10.11 Case Study 1: From UAV Mission Planning to Patch Spraying of Cirsium arvense with Commercial Sprayers
10.11.1 Introduction
10.11.2 Methods
10.11.2.1 Platform and Sensor
10.11.2.2 Flight Planning and Image Acquisition
10.11.2.3 Image Processing: Stitching and Orthorectification
10.11.2.4 Weed Detection
10.11.2.5 Patch Spraying
10.11.3 Discussion and Conclusions
10.12 Case Study 2: Use of Machine Learning to Identify Grass for Site-Specific Weed Management in Winter Cereals During Patch Spraying of Four Fields
10.12.1 Introduction
10.12.2 Methods
10.12.2.1 Sensing
10.12.3 Image Processing
10.12.3.1 Mapping
10.12.3.2 Decisions
10.12.3.3 Prescription Maps and Spraying
10.12.4 Discussion and Conclusions
10.13 Conclusions for the Chapter
References
Chapter 11: Applications of Sensing to Precision Irrigation
11.1 Introduction
11.2 Case Study 11.1. Variable-Rate Irrigation with Centre Pivot in South-Eastern USA
11.2.1 Introduction
11.2.2 Prescription Maps
11.2.3 A Dynamic VRI System
11.2.3.1 Real-Time Soil Moisture Sensing Network
11.2.3.2 Web-Based User Interface and Decision Support System
11.2.3.3 On-Farm Testing of the UGA Dynamic VRI Control System
11.2.3.4 Yield and Water-Use Efficiency
11.2.4 Conclusions
11.3 Case Study 11.2. Variable-Rate irrigation with Drip irrigation in Israel
11.3.1 Introduction
11.3.2 Thermal Imaging for Mapping Variation in Water Status
11.3.3 Thermal-Based Water Status Maps for Irrigation Management
11.3.3.1 Irrigation Management Experiment in Cotton
11.3.4 Results
11.3.4.1 Addressing the Natural Variation in Water Status in a Commercial Cotton Field
11.3.5 Conclusions
11.4 Case Study 11.3. Automatic Irrigation of Orchards Using Soil Moisture Sensors (IRRIX Model)
11.4.1 Introduction
11.4.2 Semi Commercial Testing of an Automatic Irrigation System
11.4.2.1 Selection of the Testing Zone
11.4.2.2 Automatic Irrigation System
11.4.3 Conclusions
11.5 Conclusions for the Chapter
References
Chapter 12: Applications of Optical Sensing of Crop Health and Vigour
12.1 Introduction
12.2 Case Study 12.1. Health and Vigour for Table Grapes in Greece
12.2.1 Introduction
12.2.2 Materials and Methods
12.2.3 Results and Discussion
12.2.4 Conclusions
12.3 Case Study 12.2. Airborne Multispectral images as a Tool to Characterize the Spatial Variation of vine Water Status: Application to a Non-irrigated Mediterranean Vineyard
12.3.1 Introduction
12.3.2 Materials and Methods
12.3.2.1 Description of the Vineyard
12.3.2.2 Multispectral Image
12.3.2.3 Spatial Variation and vineyard Block Selection
12.3.2.4 Sampling Scheme
12.3.2.5 Vine Measurements
12.3.3 Results
12.3.4 Conclusions
12.4 Case Study 12.3. Proximal Sensing for Cotton Management
12.4.1 Introduction
12.4.2 Material and Methods
12.4.3 Results and Discussion
12.4.3.1 General Observations
12.4.3.2 Example Field
12.4.4 Conclusions
12.5 Case Study 12.4. Integration of UAV Imagery into Potato Crop Modelling Services
12.5.1 Introduction
12.5.2 The Crop Model
12.5.3 Adapting the Model Using UAV Imagery
12.5.4 Examples of Improvement in Model Performance
12.5.5 Conclusions
12.6 Conclusions for the Chapter
References
Chapter 13: Applications of Sensing for Disease Detection
13.1 Introduction
13.2 Case Study 13.1. Detecting Cotton Root Rot disease for Precision Fungicide Application
13.2.1 Introduction
13.2.2 Studies, Methods, and Results
13.2.2.1 Satellite Remote Sensing
13.2.2.2 Manned Aircraft Remote Sensing
13.2.2.3 UAV Remote Sensing
Regional Classification
Plant-by-Plant Classification
13.2.3 Conclusions
13.3 Case Study 13.2. Detection of Laurel Wilt Disease in Avocado: A Case Study for Avocado Production in Florida
13.3.1 Introduction
13.3.2 Materials and Methods
13.3.2.1 Spectral Requirements: Spectral Data Analysis
Spectral Data Collection
Spectral Data Analysis
13.3.2.2 Image Specifications: Image Data Analysis
13.3.3 Results
13.3.3.1 Spectral Analysis-Leaf Level
13.3.3.2 Image Processing-Canopy Level
Multispectral Band Alignment and Image Radiometric Calibration
Image Data Analysis
13.3.4 Conclusions
13.4 Case Study 13.3. The Use of Hyperspectral Imaging for Esca Detection in a Vineyard
13.4.1 Introduction
13.4.2 Materials and Methods
13.4.2.1 Plant Material
13.4.2.2 Hyperspectral Imaging
Hyperspectral Image Acquisition
Image Processing
13.4.2.3 Multivariate Data Analysis
Spectral Pre-processing
Leaf Pixel Classification
13.4.3 Results and Discussion
13.4.4 Conclusions
13.5 Conclusions for the Chapter
References
Chapter 14: Conclusions: Future Directions in Sensing for Precision Agriculture
14.1 Introduction
14.2 Spatial Resolution
14.3 Temporal Resolution
14.4 Spectral Resolution and Range of Sensor Approaches
14.5 Accuracy and Calibration of Sensors
14.6 Computing Issues
14.7 Issues Influencing the Adoption of Sensing Approaches in PA: Economic and Training Issues
14.8 The Way Forward
Index