Intro
Preface
Where are We Coming From?
Organization of the Book
Summary and Concluding Thoughts
Contents
Contributors
About the Editors
Chapter 1: Agricultural and Field Robotics: An Introduction
1.1 Background
1.2 Fundamental Technologies for Agricultural and Field Robotics
1.2.1 Sensing and Situation Awareness
1.2.2 Intelligent Decision-Making
1.3 Challenges and Opportunities
1.3.1 Economics: A Critical Dimension
1.4 Concluding Thoughts
References
Part I: Sensing and Machine Vision

Chapter 2: Sensors I: Color Imaging and Basics of Image Processing
2.1 Introduction
2.2 Basics of Color Imaging
2.2.1 Color Representation
2.2.2 Color Space Conversion
2.2.3 Color Comparison
2.3 Image Acquisition
2.4 Basic Image Processing Operations
2.4.1 Image Enhancement
2.4.1.1 Histogram
2.4.1.2 Morphological Operations
2.4.1.3 Low-Pass Filtering
2.4.2 Segmentation
2.4.2.1 Pixel-Wise Techniques
2.4.2.2 Region-Based Segmentation
2.4.3 Features of Objects of Interest
2.4.4 Hough Transform
2.5 Pattern Matching
2.6 Things to Consider

2.7 Summary and Concluding Thoughts
References
Chapter 3: Sensors II: 3D Sensing Techniques and Systems
3.1 Introduction
3.2 3D Measurement Principles
3.2.1 3D from 2D Images
3.2.2 3D with Time-of-Flight of Light
3.2.3 Structured Light
3.3 Stereo-Vision System
3.3.1 Introduction
3.3.2 Depth Estimation Using Stereo-Vision Camera
3.3.3 Camera Calibration
3.3.4 Image Correspondence
3.3.5 Epipolar Geometry
3.3.6 Tools for Stereo-Vision-Based Distance Measurement
3.4 Other 3D Measurement Systems
3.4.1 Visual Servoing
3.4.2 Laser and LIDAR

3.4.3 3D Camera
3.4.4 Global Navigation Satellite Systems (GNSS)
3.4.5 Interferometric Synthetic Aperture RADAR (InSAR)
3.4.6 Ultrasonic and Infrared Techniques
3.5 Case Studies
3.5.1 Crop-Load Estimation in Orchards
3.5.2 Robotic Fruit Harvesting
3.5.3 Robotic Fruit Tree Pruning
3.5.4 Automated Red Raspberry Bundling
3.6 Summary and Concluding Thoughts
References
Chapter 4: Sensors III: Spectral Sensing and Data Analysis
4.1 Introduction
4.2 Spectroradiometers
4.2.1 Working Principle
4.2.2 Spectroradiometer Types
4.2.3 Spectral Data Analysis

4.2.3.1 Preprocessing
4.2.3.2 Spectral Feature Extraction
4.2.3.3 Spectral Data Classification/Prediction Models
4.2.4 Application Case Studies
4.2.4.1 Case Study 1. Biotic and Abiotic Stress Detection in Grapevines
4.2.4.2 Case Study 2. Citrus Disease Detection
4.2.4.3 Case Study 3. Apple Bitter Pit Disorder Detection
4.3 Spectral Imaging
4.3.1 Multispectral Imaging
4.3.1.1 Imaging Platforms
4.3.1.2 Multispectral Imagery Processing
4.3.1.3 Multispectral Imaging Applications
4.3.2 Hyperspectral Imaging
4.3.2.1 Data Analysis Methods