This book on automation in tree fruit production consists of 11 chapters covering the following topics: (1) concept of tree fruit production automation; (2) economics of perennial crops production automation; (3) sensing for stress detection and high-throughput phenotyping in precision horticulture; (4) light interception and canopy sensing for tree fruit canopy management; (5) precision orchard systems; (6) variable rate irrigation on centre pivots; (7) precision technologies for pest and disease management; (8) precision nutrient management; (9) precise crop load management; (10) mechanical harvest and in-field handling of tree fruit crops; and (11) opportunity of robotics in precision horticulture.

• Automation in Tree Fruit Production: Principles and Practice
• Copyright
• Contents
• Contributors
• 1 Tree Fruit Production Automation
  • 1.1 Introduction
  • 1.2 Precision and Automated Production for Tree Fruit
  • 1.3 Special Issues for Precision and Automated Production of Tree Fruit
  • 1.4 Integrated Solutions to Orchard Mechanization and Automation
  • References
• 2 The Economics of Perennial Crops’ Production Automation
  • 2.1 Background
  • 2.2 Economic Analysis of Mechanization of Tree Fruits
    • 2.2.1 Adoption of mechanization technologies
      • 2.2.1.1 Net present value (NPV)
      • 2.2.1.2 Introducing risk and uncertainty to technology adoption models
    • 2.2.2 Diffusion of new technologies: heterogeneity and patterns for adoption
  • 2.3 Empirical Findings
  • 2.4 Summary and Conclusion
  • Acknowledgment
  • References
• 3 Sensing for Stress Detection and High-throughput Phenotyping in Precision Horticulture
  • 3.1 Overview
  • 3.2 Sensor Technologies
    • 3.2.1 RGB camera
    • 3.2.2 Multispectral and hyperspectral sensors/cameras
    • 3.2.3 Fluorescent sensors
    • 3.2.4 Time of flight sensors
    • 3.2.5 Thermal camera
  • 3.3 Sensing for Stress Detection
    • 3.3.1 Case study I: Water stress detection in grapevine
    • 3.3.2 Case study II: Identification of fruit damage in apples
    • 3.3.3 Case study III: Disease detection in citrus
  • 3.4 Sensing for High-throughput Phenotyping
    • 3.4.1 Case study I: Crop architecture evaluation
    • 3.4.2 Case study II: Disease rating
    • 3.4.3 Case study III: Water stress response in apples
  • 3.5 Summary and Future Directions
  • References
• 4 Light Interception and Canopy Sensing for Tree Fruit Canopy Management
  • 4.1 Introduction
  • 4.2 Principles and Technologies
    • 4.2.1 Solar radiation and tree productivity
    • 4.2.2 Sensing canopy light interception
    • 4.2.3 Modeling canopy light interception
  • 4.3 Applications
    • 4.3.1 Canopy management
    • 4.3.2 Yield estimation
  • 4.4 Case Studies
    • 4.4.1 Case 1: Systems to continuously measure light interception of orchard crops
      • 4.4.1.1 System description
      • 4.4.1.2 Spatial resolution control
    • 4.4.2 Case 2: Mapping PAR interception
      • 4.4.2.1 Geometrical transformation of the shadow to represent PAR interception
      • 4.4.2.2 Y-trellis canopy architecture
      • 4.4.2.3 UFO canopy architecture
    • 4.4.3 Case 3: Modeling canopy PAR interception for estimating potential yield
  • 4.5 Challenges and Opportunities
  • 4.6 Summary
  • References
• 5 Precision Orchard Systems
  • 5.1 Introduction
  • 5.2 Canopy Architecture and Training Systems
  • 5.3 Rootstocks for Vigor Control
  • 5.4 Light Interception and Productivity
  • 5.5 Variability in Fruit Quality
  • 5.6 Orchard Systems for Harvest Mechanization
    • 5.6.1 Case study: Upright Fruiting Offshoots system for sweet cherry
  • 5.7 Future Precision Orchard Systems
  • References
• 6 Variable Rate Irrigation on Center Pivots
  • 6.1 Introduction
  • 6.2 Variable Speed Irrigation versus Variable Zone Irrigation
    • 6.2.1 Variable speed irrigation
    • 6.2.2 Variable zone irrigation
  • 6.3 Variable Rate Irrigation in Response to Variable Soils
    • 6.3.1 Variable rate irrigation in response to variations in soil water-holding capacity
      • 6.3.1.1 Managing for sandy soils
      • 6.3.1.2 Managing for silty soils
    • 6.3.2 Variable rate irrigation in response to runoff in some areas
  • 6.4 Situations Where VRI Can Conserve Water and Improve Profitability
    • 6.4.1 Non-cropped areas
    • 6.4.2 Areas of the field getting water from other sources
    • 6.4.3 Different crops in the same field
    • 6.4.4 Overwatering the inside span
    • 6.4.5 Variations in crop water use (ET)
    • 6.4.6 Use of pivot as a variable rate sprayer
    • 6.4.7 Control for uniform dry down
    • 6.4.8 Leaving room in the soil to capture rainfall
  • 6.5 Creating and Modifying VRI Prescriptions
  • 6.6 What Other Researchers Have Found
  • 6.7 Summary
  • References
• 7 Precision Technologies for Pest and Disease Management
  • 7.1 Introduction
  • 7.2 Pest Monitoring Technologies
    • 7.2.1 Conventional pest monitoring techniques
    • 7.2.2 Emerging pest monitoring technologies
    • 7.2.3 Advances in microclimatic measurements
      • 7.2.3.1 Open field microclimate measurement
      • 7.2.3.2 In-field microclimate measurement
      • 7.2.3.3 Climate data-driven decision systems
  • 7.3 Disease Monitoring Technologies
    • 7.3.1 Contact-type sensors
    • 7.3.2 Non-contact-type sensors
    • 7.3.3 Sensing technology adoption challenges
  • 7.4 Agricultural Application Technologies
    • 7.4.1 Traditional orchard sprayers
    • 7.4.2 Variable rate technology: concept and implementation
    • 7.4.3 Future application technologies
    • 7.4.4 Standardization issues
  • 7.5 Summary
  • References
• 8 Precision Nutrient Management
  • 8.1 Introduction
  • 8.2 Fertigation Methods
    • 8.2.1 Fertilizer injection
    • 8.2.2 Irrigation scheduling
    • 8.2.3 Fertilizer sources
    • 8.2.4 Nutrient and moisture monitoring
  • 8.3 Nutrient Requirements
    • 8.3.1 Nitrogen
    • 8.3.2 Phosphorus
    • 8.3.3 Potassium
    • 8.3.4 Other Nutrients
  • 8.4 Fertigation Challenges
    • 8.4.1 Crop load
    • 8.4.2 Soil acidification
    • 8.4.3 Nutrient balance
  • 8.5 Future Fertigation Management Developments
  • 8.6 Summary
  • References
• 9 Precise Crop Load Management
  • 9.1 Introduction
  • 9.2 Manual Approaches for Crop Load Management
    • 9.2.1 Pruning
    • 9.2.2 Chemical thinning
    • 9.2.3 Hand thinning
      • 9.2.3.1 Cluster trimming and berry thinning
      • 9.2.3.2 Blossom/fruit thinning
  • 9.3 Case Study of Mechanical Crop Load Management
    • 9.3.1 Continuously pruning technology for grapevine
    • 9.3.2 Practice of mechanical thinning in peach and sweet cherry
      • 9.3.2.1 Blossom thinning with string thinning machine and drum shaker in peach
      • 9.3.2.2 Hand-held targeted thinning machine for sweet cherry
    • 9.3.3 Electrostatic pollination in fruit tree crops
  • 9.4 Challenges and Opportunities for Automation in Crop Load Management
  • 9.5 Summary
  • References
• 10 Mechanical Harvest and In-field Handling of Tree Fruit Crops
  • 10.1 Introduction
  • 10.2 Crop Architecture
    • 10.2.1 Central leader architecture
    • 10.2.2 Spindle architecture
    • 10.2.3 Multi-leader canopies
      • 10.2.3.1 Open vase architecture
      • 10.2.3.2 Kym green bush (KGB)
      • 10.2.3.3 Biaxial
      • 10.2.3.4 Upright fruiting offshoot (UFO)
    • 10.2.4 Conventional versus fruiting wall canopies
    • 10.2.5 Formal versus random canopies
    • 10.2.6 Angled versus vertical canopies
  • 10.3 Overview of Mechanical Harvesting
    • 10.3.1 Historical background of mechanical harvesting and handling
      • 10.3.1.1 Mass harvesting systems
      • 10.3.1.2 Robotic harvesting
    • 10.3.2 Theories and principles
      • 10.3.2.1 Fruit quality
      • 10.3.2.2 Yield benefits
      • 10.3.2.3 Machine/orchard compatibility
      • 10.3.2.4 Handling and storage
      • 10.3.2.5 Cost
    • 10.3.3 Latest developments
  • 10.4 Mechanical Harvesting Systems and Components
    • 10.4.1 Mass harvesting system components
      • 10.4.1.1 Shaking parameters
      • 10.4.1.2 Canopy shaking mechanisms
      • 10.4.1.3 Trunk shaking mechanisms
      • 10.4.1.4 Branch shaking mechanisms
      • 10.4.1.5 Catching mechanisms
    • 10.4.2 Robotic harvesting systems components
      • 10.4.2.1 Sensing and machine vision
      • 10.4.2.2 End-effector
      • 10.4.2.3 Manipulation
      • 10.4.2.4 Path planning and inverse kinematics
    • 10.4.3 Materials and methods for fruit collection and handling
      • 10.4.3.1 Foam materials
      • 10.4.3.2 Non-Newtonian fluid
      • 10.4.3.3 Air suspension
      • 10.4.3.4 Fruit transportation and bin filling
    • 10.4.4 Performance evaluation of harvesting systems
      • 10.4.4.1 Fruit removal efficiency
      • 10.4.4.2 Fruit collection efficiency
      • 10.4.4.3 Fruit damage percentage
      • 10.4.4.4 Fruit removal condition
      • 10.4.4.5 Throughput and cycle time
    • 10.4.5 Comparison of fruit harvesting techniques
  • 10.5 Case Study: Shake-and-catch Cherry Harvesting
    • 10.5.1 Introduction
    • 10.5.2 Optimizing shaking parameters for cherry harvesting
      • 10.5.2.1 Shaking frequency
      • 10.5.2.2 Shaking location
      • 10.5.2.3 Shaking duration
    • 10.5.3 Optimizing catching parameters for cherry harvesting
    • 10.5.4 Cherry harvesting systems
      • 10.5.4.1 Hand-held tools for sweet cherry harvesting
      • 10.5.4.2 Sweet cherry harvesting machines
    • 10.5.5 Future direction for cherry harvesting
      • 10.5.5.1 Biological aspects
      • 10.5.5.2 Automating shake-and-catch harvesting
  • 10.6 Robotic Apple Harvesting System
    • 10.6.1 Working environment and design specifications
    • 10.6.2 Developing components for the robotic apple harvester
      • 10.6.2.1 Machine vision system
      • 10.6.2.2 Study of human hand picking
      • 10.6.2.3 Manipulator and end-effector design
      • 10.6.2.4 Path planning
    • 10.6.3 Laboratory experiments with the robotic harvester
    • 10.6.4 Field evaluation of the robotic apple harvester
    • 10.6.5 Overall discussion, potentials and challenges
      • 10.6.5.1 Vision
      • 10.6.5.2 Manipulation
      • 10.6.5.3 Overall system
  • 10.7 Status, Challenges and Opportunities for Fruit Harvesting
    • 10.7.1 Model-based design
    • 10.7.2 Multipurpose robotic systems
    • 10.7.3 Human–machine collaboration
  • 10.8 Summary
  • References
• 11 Opportunity of Robotics in Precision Horticulture
  • 11.1 Introduction
  • 11.2 Autonomous Robotic Vehicle Guidance
    • 11.2.1 Case Study: Autonomous navigation in citrus groves
    • 11.2.2 Kalman filter design
    • 11.2.3 State transition model
    • 11.2.4 Measurement model
    • 11.2.5 Filter gain
    • 11.2.6 Reliability factor of primary guidance sensors in the Kalman filter
    • 11.2.7 Fuzzy logic sensor supervisor
  • 11.3 Novel Technologies for Robotic Crop Status Monitoring
    • 11.3.1 Laser used in precision sprayer
    • 11.3.2 Laser used in yield estimation
    • 11.3.3 Laser/lidar used in tree canopy volume
    • 11.3.4 Machine vision used in yield estimation
    • 11.3.5 Machine vision used in detecting citrus greening on leaves
  • 11.4 Cultural Practices Mechanization and Automation
    • 11.4.1 Hedging and pruning automation in orchard production
    • 11.4.2 Fruit thinning by hand, string mechanisms and electromechanical methods
    • 11.4.3 Robotic pruning/thinning
    • 11.4.4 Precision spraying applications
    • 11.4.5 Yield monitoring
  • 11.5 Robotic Tree Fruit Harvesting Background
    • 11.5.1 Horticultural aspects of robotic harvesting
    • 11.5.2 Plant population and spacing
    • 11.5.3 Plant shape and size
    • 11.5.4 Tree genetics for optimal harvesting
  • 11.6 Design Aspects of Robotic Harvesting
    • 11.6.1 Physical properties and fruit removal
    • 11.6.2 Machine vision and sensing technologies
    • 11.6.3 Robotic manipulation and control
  • 11.7 Case Study: Robotic citrus harvester system development
    • 11.7.1 Test bed robotic manipulator
    • 11.7.2 Vision sensory system
    • 11.7.3 Harvesting end-effector
    • 11.7.4 System architecture
    • 11.7.5 Fruit detection and harvesting trials
  • 11.8 Continuing Development and Enhancement Opportunities
    • 11.8.1 Fruit detection systems
    • 11.8.2 End-effector development
  • 11.9 Novel Approaches in Visual Servo Control Development
    • 11.9.1 Image-based visual servo control
      • 11.9.1.1 Euclidean reconstruction
      • 11.9.1.2 Control objective
      • 11.9.1.3 Controller development
      • 11.9.1.4 Rotation controller
      • 11.9.1.5 Translation controller
      • 11.9.1.6 Experimental validation
    • 11.9.2 Robust visual servo control
      • 11.9.2.1 Control objective
      • 11.9.2.2 Rotation controller
      • 11.9.2.3 Translation controller
      • 11.9.2.4 Experimental validation
    • 11.9.3 Adaptive visual servo control
      • 11.9.3.1 Fruit motion modeling
      • 11.9.3.2 Controller development
      • 11.9.3.3 Simulation results
  • 11.10 Conclusions
  • References
• Index