Welcome!

Cloud Expo Authors: Roger Strukhoff, Barbara Porter, Shelly Palmer, Yeshim Deniz, Pat Romanski

News Feed Item

Agricultural Robots: Market Shares, Strategies, and Forecasts, Worldwide, 2014 to 2020

NEW YORK, Feb. 10, 2014 /PRNewswire/ -- Reportlinker.com announces that a new market research report is available in its catalogue:

Agricultural Robots: Market Shares, Strategies, and Forecasts, Worldwide, 2014 to 2020
http://www.reportlinker.com/p02003735/Agricultural-Robots-Market-Shares-Strategies-and-Forecasts-Worldwide-2014-to-2020.html#utm_source=prnewswire&utm_medium=pr&utm_campaign=Agriculture

WinterGreen Research announces that it has published a new study Agricultural Robots Market Shares, Strategy, and Forecasts, Worldwide, 2014 to 2020. The 2014 study has 430 pages, 236 tables and figures. Worldwide markets are poised to achieve significant growth as the agricultural robots are used in every aspect of farming, milking, food production, and animal control to implement automated process for the industry.
Weed control is able to achieve crop-yield increases. Robot technology is deploying machines for weed control, promising to improve crop yields. Robots make the crops safer by eliminating or virtually eliminating herbicides. Downstream processing system solutions and robots achieve automation of process. Robots meet stringent hygiene and safety regulations, work tirelessly 24 hours a day, and relieve human workers of physically arduous tasks. Robots contribute to the freshness, variety and quality of food. Projects are ongoing.
High value crops are a target of agricultural robotic development. What could be tastier than a strawberry, perfectly formed, and perfectly ripened? New agricultural robots are able to improve the delivery of consistent quality food, and to implement efficiency in managing food production.

Strawberries are a high profit crop. A new generation of machines has just been born. Strawberry Harvesters with the world's most advanced technology to give maximum performance to a farm. Harvesting robots can optimize the productivity of the farming business. Growers can get the best results in a berry farm using automated process. Automated picking collection systems improve labor productivity, give speed and agility to harvest operations.
The robotic platforms are capable of site-specific spraying. This is targeted spraying only on foliage and selected targets. It can be used for selective harvesting of fruit. The robots detect the fruit, sense its ripeness, then move to grasp and softly detach only ripe fruit.
Agricultural robots address automation of process for agribusiness. The challenge being addressed is to guide farmers towards a new economic model. The aim is to meet demands of a global market. Harvesting is one benefit. Crop-yield increases come from weed control. Robot technology is deploying its machines for weed control, promising to improve crop yields. Robots make the crops safer by eliminating or virtually eliminating herbicides.
Machinery manufacturers and downstream processing industries look for system solutions and robots to achieve automation of process. Robots meet stringent hygiene and safety regulations, work tirelessly 24 hours a day, and relieve human workers of physically arduous tasks. Robots contribute to the freshness, variety and quality of food.

According to Susan Eustis, principal author of the market research study, "Agricultural robotic projects are ongoing. The key to industrial farm robots is keeping costs down. Adapting existing commercial vehicles instead of building new ones is the best way to build viable agricultural robots."
Agricultural robot market size at $817 million in 2013 are anticipated to reach $16.3 billion by 2020, a hefty growth for a nascent market. Agricultural robots are but part of an overall trend toward more automated process for every type of human endeavor. Robots are being used more widely than expected in a variety of sectors, and the trend is likely to continue with robotics becoming as ubiquitous as computer technology over the next 15 years.

1. Market Agricultural Robot Description and Market Dynamics

1.1 Agricultural Markets
1.1.1 Automation Potential In The Agricultural Industry
1.1.2 Robots Find A Place in the Agriculture Industry
1.1.3 Agricultural Robots Make Production More Efficient
1.1.4 Use Of Industrial Robots for Agriculture
1.1.5 Agricultural Robotics and Automation
1.2 RAS Agricultural Robotics and Automation (AgRA) Technical Committee
1.3 Farm Bots Pick, Plant and Drive
1.3.1 Relying On Illegal Immigrants Can Be A Legal Liability
1.4 Nursery & Greenhouse Sector

1.4.1 Harvest Automation Labor Process Automation
1.4.2 The Growing Season Is Also The Shipping Season
1.5 Improving Nursery Efficiency
1.5.1 Small Mobile Robot for Plants and Shrubs
1.6 Agricultural Producers Seek To Improve Operations
1.6.1 Increasing Cows Days Of Grazing
1.7 cRops (Clever Robots for Crops) Robots To Harvest High Value Crops
1.8 European Union Seventh Framework Program
1.9 Strawberries
1.9.1 Strawberries in the US
1.10 Transformational Agricultural Robots

2 Agricultural Robots Market Share and Market Forecasts

2.1 Agricultural Robot Market Driving Forces
2.1.1 Agricultural Robot Target Markets
2.1.2 Robotic Agriculture Trends
2.2 Agricultural Robot Market Shares
2.2.1 Lely Group Revenue
2.2.2 Use Of Standard Industrial Robots In Agriculture
2.2.3 Kuka
2.2.4 Fanuc
2.2.5 Agrobot High Value Crop Robotic Automation
2.2.6 John Deere Autonomous Tractors
2.2.7 Harvest Automation
2.2.8 Vision Robotics
2.3 Agricultural Robot Market Forecasts
2.3.1 Agricultural Robot Market Segments

2.3.2 Agricultural Robotics Key Economic Enabler
2.3.3 High Value Fruit Crops: Strawberries
2.3.4 Nursery And Garden Products
2.3.5 Ornamental Plant Markets
2.3.6 Golf courses Robotic Mowing
2.3.7 Crop Dusting With Remote-Controlled Helicopters
2.3.8 Distributed Robotics Garden
2.3.9 Cultibotics
2.3.10 Agricultural Robot Vision Pruning Systems
2.4 Agricultural Robot Pricing
2.4.1 Harvest Automation
2.4.2 Shibuya Seiko Co. Strawberry Picking Robot
2.4.3 Wall-Ye V.I.N. Robot Functions
2.4.4 iRobot Automated Lawn Mowing
2.5 Agricultural Robots TCO / ROI
2.5.1 Cost Structures and Roles of Agricultural Robots
2.6 Agricultural Robot Regional Analysis
2.6.1 Production of Agricultural Robotics in China
2.6.2 Chinese Agricultural Machinery
2.6.3 Agricultural Robots in Africa

3 Agricultural Robots Product Description

3.1 John Deere Autonomous Tractor
3.1.1 John Deere Crop Spraying
3.2 Kuka
3.2.1 Kuka Robots in the Agricultural Industry
3.2.2 Kuka Robots in the Food Processing Industry
3.2.3 Kuka Automation in Agriculture
3.3 FANUC
3.3.1 Fanuc Vegetable Sorting Robot
3.3.2 FANUC Robodrill DiA5 Series
3.4 ABB Robots
3.4.1 ABB Symphony Plus
3.5 Yaskawa
3.5.1 Yaskawa Industrial AC Drives 1/8 thru 1750 Horsepower
3.5.2 Yaskawa Specialty Pump Drives 3/4 thru 500 Horsepower
3.5.3 Yaskawa Servo Systems and Motion Controllers
3.5.4 Motoman Robot Handling and Palletizing Bags of Livestock Feed
3.5.5 Motoman Agriculture Robotics Palletizing Bags Solution
3.5.6 Motoman Robotics Agricultural Robot Palletizing
Bags Fixtures / Tooling Details
3.5.7 Motoman Agricultural Grain Bin Dryer Fan Wheels
3.5.8 Motoman Robotics Fixtures/Tooling Details
3.5.9 Motoman Agricultural Irrigation Pipe
3.5.10 Motoman Robotics Fixtures/Tooling Details
3.5.11 Motoman Agricultural Equipment
3.5.12 Motoman Robotics Fixtures/Tooling Details
3.5.13 Motoman Round Baler Pickup Frames for Agricultural Equipment
3.5.14 Motoman Robotics Fixtures/Tooling Details
3.5.15 Motoman Skid Steer Loader Mount Plates
3.5.16 Motoman Bags of Livestock Feed

3.5.17 Motoman Robotics Fixtures/Tooling Details
3.6 Harvest Automation
3.6.1 Harvest Automation Technology
3.6.2 Harvest Automation Behavior-Based Robotics
3.7 Robotic Harvesting
3.7.1 Robotic Harvesting Strawberry Harvester
3.8 Agrobot SW 6010
3.8.1 Agrobot AGB: Harvesting High Level System
3.8.2 Agrobot AG Vision
3.9 Blue River Technology
3.9.1 Blue River Precision Lettuce Thinning - 40/42" Beds
3.9.2 Blue River Precision Lettuce Thinning - 80/84" Beds
3.9.3 Lettuce Bot, Blue River Technology
3.10 cRops (Clever Robot for Crops)
3.10.1 cRops European Project, Made Up Of Universities And Labs
3.11 Jaybridge Robotics Agriculture
3.11.1 Jaybridge Robotics Kinze Partnering, Autonomous
Vehicle Row Crop Harvesting
3.11.2 Jaybridge Software Expertise
3.12 Nano Ganesh
3.13 Aqua Spy
3.14 8 Villages
3.15 IBM / Bari Fishing Market App

3.16 M Farm
3.17 Sustainable Harvest
3.18 Tractor Harvesting
3.19 Spensa Technology Pest Control
3.20 The Pebble Watch
3.21 Louisiana State University AgBot
3.21.1 AgBot Uses Autonomous, Advanced GPS System
3.21.2 Agbot Small Robots Versatility
3.21.3 Delivery Robot
3.22 Harvard Robobee
3.22.1 Harvard Robobee Practical Applications
3.22.2 Harvard Robobee Vision and Aims
3.22.3 Harvard Robobee Body, Brain, and Colony
3.22.4 Harvard Robobee Body
3.22.5 Harvard Robobee Flexible Insect Wings And Flight
Stability In Turbulent Airflow
3.22.6 Harvard Robobee Sensor Networks
3.22.7 Harvard Robobee Colony
3.22.8 Harvard Robobee Sensor Network Development
3.23 iRobot's Automatic Lawn Mower
3.24 MIT Autonomous Gardener Equipment Mounted On The
Base of a Roomba
3.25 Carnegie Mellon University's National Robotics Engineering Center
3.25.1 Carnegie Mellon. Self-Guided Farm Equipment
3.26 Cesar the LettuceBot

3.27 Universidad Politécnica de Madrid Rosphere
3.27.1 Rosphere Spherical Shaped Robot
3.28 Shibuya Seiko Co.
3.28.1 Shibuya Seiko Co. Strawberry Picking Robot
3.28.2 Shibuya Seiko Robot Can Pick Strawberry Fields
3.29 University of California, Davis Robots For Harvesting Strawberries
3.30 Wall-Ye V.I.N. Robot
3.30.1 Wall-Ye V.I.N. Robot Functions
3.30.2 Wall-Ye V.I.N. Robot Security System
3.30.3 Wall-Ye V.I.N. Robot Prunes 600 Vines Per Day
3.31 Vision Robotics
3.31.1 Vision Robotics Automated Tractors
3.32 Nogchui Autonomous Tractor
3.32.1 Professor Nogchui Agricultural Tractor Robot Uses
Navigation Sensor Called AGI-3 GPS Compass Made by TOPCON
3.32.2 Professor Nogchui Agricultural Tractor Robot Mapping System
3.32.3 Nogchui Autonomous Tractor Robot Management Systems
3.33 Microsoft Agricultural Robot Software
3.34 Australian Centre for Field Robotics Herder Robot
3.34.1 Robotic Rover Herds Cows
3.35 Chinese Agricultural Robots
3.36 Oracle Robot
3.37 3D Robotics
3.38 Lely Automatic Milking Robots
3.38.1 Lely Astronaut Milking Robots
3.38.2 Lely Concept and Management
3.38.3 Lely Correct Feed Management
3.38.4 Lely Milk Robots At Large Dairy Farms

3.38.5 Lely Free Cow Traffic
3.39 Kyoto University Tomato Harvesting Robot
3.40 Yamaha Crop Dusting Drones
3.41 RHEA Robot Fleets for Accuracy
3.41.1 RHEA Synchronoized Weeding
3.41.2 Synchronized Spraying
3.42 Precise Path Robotics

4. Agricultural Robots Technology

4.1 Harvest Automation Proprietary Sensor Technology
4.1.1 Harvest Automation Robot System Architecture
4.1.2 Harvest Automation Technology
4.1.3 Behavior-Based Robotics
4.1.4 Proprietary Sensor Technology
4.1.5 System Design & Architecture
4.2 Welding Robots
4.3 Material Handling Robots:
4.4 Plasma Cutting Robots:
4.5 Agricultural Robotics and Automation Scope:
4.5.1 IEEE Standards Initiatives
4.5.2 Delft Robotics Institute
4.6 Robotics and Automation
4.7 An Electronic System Improves Different Agriculture Processes

5 Agricultural Robots Company Description

5.1 ABB Robotics
5.1.1 ABB Revenue
5.1.2 ABB Strategy
5.1.3 ABB Global Leader In Power And Automation Technologies
5.1.4 ABB and IO Deliver Direct Current-Powered Data Center Module
5.1.5 ABB / Validus DC Systems DC Power Infrastructure Equipment
5.1.6 ABB Technology
5.1.7 ABB Global Lab Power
5.1.8 ABB Global Lab Automation
5.2 Agile Planet
5.3 AgRA: RAS Agricultural Robotics and Automation (AgRA
5.4 Agrobot
5.4.1 Agrobot Innovation and Technology for Agribusiness
5.5 Astronaut
5.6 Australian Centre for Field Robotics
5.7 Blue River Technology
5.7.1 Blue River / Khosla Ventures
5.8 CNH Industrial / Fiat / Case IH
5.8.1 Case IH Customers Work Directly With Design Engineers
5.9 cRops
5.10 Fanuc
5.10.1 FANUC Corporation
5.10.2 Fanuc Revenue
5.11 Georgia Tech Agricultural Robots
5.12 Google
5.12.1 Google / Boston Dynamics
5.12.2 Boston Dynamics LS3 - Legged Squad Support Systems

5.12.3 Boston Dynamics CHEETAH - Fastest Legged Robot
5.12.4 Boston Dynamics Atlas - The Agile Anthropomorphic Robot
5.12.5 Boston Dynamics BigDog
5.12.6 Boston Dynamics LittleDog - The Legged Locomotion
Learning Robot
5.12.7 Google Robotic Division
5.12.8 Google Self-Driving Car
5.12.9 Google Cars Address Vast Majority Of Vehicle
Accidents Due To Human Error
5.12.10 Google Business
5.12.11 Google Corporate Highlights
5.12.12 Google Search
5.12.13 Google Revenue
5.12.14 Google Second Quarter 2013 Results
5.12.15 Google Revenues by Segment and Geography
5.12.16 Google / Motorola Headcount
5.12.17 Google / Motorola
5.13 Harvard Robobee
5.13.1 Harvard Robobee Funding
5.13.2 Harvard Robobee Main Area Of Research
5.13.3 Harvard Robobee OptRAD is used as an Optimizing
Reaction-Advection-Diffusion system.
5.13.4 Harvard Robobee The Team
5.14 Harvest Automation
5.14.1 Harvest Automation Ornamental Horticulture
5.14.2 Harvest Automation M Series C Financing
5.14.3 Harvest Robotic Solutions For The Agricultural Market
5.14.4 Harvest Automation Robots
5.15 IBM
5.15.1 IBM Strategy
5.15.2 IBM Business Partners
5.15.3 IBM Messaging Extension for Web Application Pattern
5.15.4 IBM MobileFirst
5.15.5 IBM Business Analytics and Optimization Strategy
5.15.6 IBM Growth Market Initiatives
5.15.7 IBM Business Analytics and Optimization

5.15.8 IBM Strategy
5.15.9 IBM Smarter Planet
5.15.10 IBM Cloud Computing
5.15.11 IBM Business Model
5.15.12 IBM Business Revenue Segments And Capabilities
5.16 iRobot
5.16.1 iRobot Home Robots:
5.16.2 iRobot Defense and Security: Protecting Those in Harm's Way
5.16.3 iRobot Role In The Robot Industry
5.16.4 iRobot SPARK (Starter Programs for the Advancement of
Robotics Knowledge)
5.16.5 iRobot Revenue
5.16.6 iRobot Acquires Evolution Robotics, Inc.
5.16.7 iRobot / Evolution Robotics
5.17 Jaybridge Robotics
5.17.1 Jaybridge Robotics Software Solutions
5.17.2 Jaybridge Systems Integration for Autonomous Vehicles
5.17.3 Jaybridge Robotics Rigorous Quality Processes
5.17.4 Jaybridge Robotics Professional, Experienced Team
5.17.5 Jaybridge Robotics Seamless Working Relationship with
Client Teams
5.18 Kuka
5.18.1 Kuka Revenue
5.18.2 Kuka Competition
5.18.3 Kuka Innovative Technology
5.18.4 Kuka Well Positioned With A Broad Product Portfolio In
Markets With Attractive Growth Prospects
5.18.5 Kuka Strategy

5.18.6 Kuka Corporate Policy
5.19 KumoTek
5.19.1 KumoTek Robotics Software Specialists
5.20 Kyoto University
5.21 Lely
5.21.1 Lely Group Business Concepts
5.21.2 Lely Group Revenue
5.22 Millennial Net
5.22.1 Millennial Net Wireless Sensor Network:
5.22.2 Millennial Net 1000-Node MeshScape GO Wireless
Sensor Network (WSN) Agricultural Sensors
5.22.3 Millennial Net's MeshScape GO WSN Technology
5.23 National Agriculture and Food Research Organization
5.23.1 NARO, a Japanese Incorporated Administrative Agency
5.23.2 National Agriculture and Food Research Organization
(NARO) third mid-term plan (from 2011 to 2015)
5.23.3 National Agriculture and Food Research Organization
Stable Food Supply
5.23.4 National Agriculture and Food Research Organization
Development For Global-Scale Issues And Climate Change

5.23.5 National Agriculture and Food Research Organization
Development To Create Demand For New Food Products
5.23.6 National Agriculture and Food Research Organization
Development For Utilizing Local Agricultural Resources
5.23.7 Japanese National Agriculture and Food Research Organization
5.24 Ossian Agro Automation / Nano Ganesh
5.25 Precise Path Robotics
5.26 Robotic Harvesting
5.27 Sicily Tractor Harvesting
5.28 Shibuya Seiki
5.28.1 Shibuya Kogyo Pharmaceutical Application Examples
5.28.2 Shibuya Kogyo Robotic System For Handling Soft Infusion Bags
5.28.3 Shibuya Kogyo Robotic Cell Culture System "CellPRO"
5.28.4 Shibuya Kogyo Robotic System For Leaflet & Spoon Placement
5.28.5 Shibuya Kogyo Robotic Collating System
5.28.6 Shibuya Kogyo Automated Aseptic Environmental
Monitoring System
5.29 Universidad Politécnica de Madrid
5.30 University of California, Davis
5.31 Wall-Ye V.I.N. Robot
5.32 Yamaha
5.33 Yaskawa
5.33.1 Yaskawa Revenue
5.33.2 Yaskawa Business
5.33.3 YASKAWA Electric Motion Control
5.33.4 YASKAWA Electric Robotics
5.33.5 YASKAWA Electric System Engineering
5.33.6 YASKAWA Electric Information Technology
5.33.7 Yaskawa / Motoman

5.34 Agricultural Robotic Research Labs
5.34.1 Outdated links
5.34.2 Agricultural Robotic Companies
5.34.3 IEEE Agricultural Technical Committee
5.34.4 Agricultural Robotic Conferences
5.34.5 Agricultural Robotic Publications
5.34.6 Selected VC Funding In Robotics

List of Tables and Figures

Figure ES-1
Agrobot Strawberry Picker
Table ES-2
Agricultural Robot Market Driving Forces
Table ES-3
Agricultural Robot Target Markets
Table ES-4
Robotic Agricultural Trends
Table ES-5
Agriculture Robotic Activities
Table ES-6
Market Forces for Agricultural Modernization
Table ES-7
Robotics – State of the Art Advantages
Table ES-8
Agricultural Robot Challenges
Figure ES-9
Agricultural Robot Market Shares, Dollars, Worldwide, 2013
Figure ES-10
Agricultural Robot Market Forecasts Dollars, Worldwide,
2014-2020
Table 1-1
Aspects of Agricultural Sector Modernization
Figure 1-2

Agricultural Robotics Positioned To Meet The Increasing Demands For
Food And Bioenergy
Source: John Deere.
Figure 1-3
Autonomous Orchard Vehicle
Figure 1-4
Automated Picker Machine
Table 1-5
Nursery Robot Benefits
Figure 1-6
Cows Grazing
Figure 1-7
European Union Seventh Framework Program cRops
(Clever Robots for Crops) Focus On Harvesting High Value Crops
Figure 1-8
Transformational Agricultural Robots

Figure 2-1
Agrobot Strawberry Picker
Table 2-2
Agricultural Robot Market Driving Forces
Table 2-3
Agricultural Robot Target Markets
Table 2-4
Robotic Agricultural Trends
Table 2-5
Agriculture Robotic Activities
Table 2-6
Market Forces for Agricultural Modernization
Table 2-7
Robotics – State of the Art Advantages
Table 2-8
Agricultural Robot Challenges
Figure 2-9
Agricultural Robot Market Shares, Dollars, Worldwide, 2013
Table 2-10
Agricultural Robot Market Shares, Dollars, Worldwide, 2013

Figure 2-11
Agrobot Strawberry Picker
Figure 2-12
John Deere Autonomous Tractors
Figure 2-13
Agricultural Robot Market Forecasts Dollars, Worldwide,
2014-2020
Table 2-14
Agricultural Robot Market Forecast, Shipments, Dollars,
Worldwide, 2014-2020
Table 2-15
Agricultural Robot Market Industry Segments, Cow Milking and
Barn Systems, Strawberries and High Value Crops, Wheat, Rice,
Corn Harvesting, Grape Pruning and Harvesting, Nursery Management,
Golf Course and Lawn Mowing, Drone Crop Dusting Segments,
Dollars, Worldwide, 2014-2020
Table 2-16
Agricultural Robot Market Industry Segments, Cow Milking and
Barn Systems, Strawberries and High Value Crops, Wheat, Rice,
Corn Harvesting, Grape Pruning and Harvesting, Nursery Management,
Golf Course and Lawn Mowing, Drone Crop Dusting Segments,
Percent , Worldwide, 2014-2020
Figure 2-17
Multiple Small Intelligent Machines Replace Large Manned Tractors
Table 2-18
Agricultural Robots for Ornamental Plant Handling Benefits
Figure 2-19
UC Davis Using Yahama Helicopter Drones For Crop Dusting

Figure 2-20
Yahama Crop Duster
Figure 2-21
Distributed Robotics Garden
Figure 2-22
Modernized Agriculture Telegarden, As Installed At Ars Electronica
Table 2-23
Voluntary Cow Traffic Benefits
Table 2-24
Cow Traffic System Cubicles ROI Metrics
Table 2-25
Lely Example of Herd Size and Robots / Farm Worker
Table 2-26
Roles of Agricultural Robots
Figure 2-27
Cost Structures and Roles of Agricultural Robots
Figure 2-28
Agricultural Robotic Regional Market Segments, 2013
Table 2-29
Agricultural Robot Regional Market Segments, 2013
Figure 3-1

John Deere Autonomous Tractors
Figure 3-2
John Deere Autonomous Tractor Flexible Uses
Figure 3-3
John Deere Crop Spraying
Figure 3-4
Kuka Agricultural Robots
Figure 3-5
Kuka Material Handling Robots
Figure 3-6
Kuka Industry Standard Robots Used in Agriculture
Figure 3-7
Kuka Welding Robots in the Agricultural Industry
Figure 3-8
Kuka Robots in the Agricultural Industry
Figure 3-9
Kuka Robots in the Food Processing Industry
Figure 3-10
Kuka Agricultural Robots
Figure 3-11
Kuka Plasma Cutting Robot

Figure 3-12
Fanuc M-3iA Robots Sorting Boxes
Figure 3-13
FANUC Robodrill DiA5 Series
Figure 3-14
FANUC Welding Robots
Figure 3-15
FANUC Material Handling Robots
Figure 3-16
FANUC Plasma Cutting Robot
Figure 3-17
ABB Welding Robots
Figure 3-18
ABB Material Handling Robots
Figure 3-19
Yaskawa Plasma Cutting Robot
Figure 3-20
Yaskawa Robots Used in Agriculture
Figure 3-21
Yaskawa Industrial AC Drives 1/8 thru 1750 Horsepower

Figure 3-22
Yaskawa Specialty Pump Drives 3/4 thru 500 Horsepower
Figure 3-23
Motoman Robot Handling and Palletizing Bags of Livestock Feed
Table 3-24
Motoman Robot Handling and Palletizing Bags of Livestock
Feed Project Challenges
Table 3-25
Motoman Agriculture Robotics Palletizing Bags Solution
Table 3-26
Motoman Agricultural Grain Bin Dryer Fan Wheels Project Challenges
Table 3- 27
Motoman Agricultural Grain Bin Dryer Fan Wheels Robotics Solution
Figure 3-28
Motoman Agricultural Irrigation Pipe
Table 3-29
Motoman Agricultural Irrigation Pipe Project Challenges
Table 3-30
Motoman Agricultural Irrigation Pipe
Robotics Solution
Figure 3-31

Motoman Agricultural Equipment
Table 3-32
Motoman Agricultural Equipment Project Challenges
Table 3-33
Motoman Agricultural Equipment Robotics Solution
Figure 3-34
Motoman Round Baler Pickup Frames for Agricultural Equipment
Table 3-35
Motoman Round Baler Pickup Frames for Agricultural Equipment Project Challenges
Table 3-36
Motoman Round Baler Pickup Frames for Agricultural
Equipment Robotics Solution
Figure 3-37
Motoman Skid Steer Loader Mount Plates
Table 3-38
Motoman Skid Steer Loader Mount Plates Project Challenges
Table 3-39
Motoman Skid Steer Loader Mount Plates Robotics Solution
Figure 3-40
Motoman Bags of Livestock Feed
Table 3-41
Motoman Bags of Livestock Feed Project Challenges

Table 3-42
Motoman Bags of Livestock Feed Robotics Solution
Figure 3-43
Harvest Automation Shrub Robot
Figure 3-44
Harvest Automation Shrub Robot In Garden
Figure 3-45
Harvest Automation Robot Provides Marketplace Sustainability
Table 3-46
Harvest Automation Shrub Robot Features:
Table 3-47
Harvest Automation Shrub Robot Functions:
Figure 3-48
Robotic Harvesting of Strawberries
Figure 3-49
Agrobot SW 6010
Figure 3-50
Agrobot AGB: Harvesting High Level System
Figure 3-51
Agrobot AG Vision
Figure 3-60

Blue River Technology Agricultural Robot
Figure 3-61
Blue River Precision Lettuce Thinning Agricultural Robot
Table 3-62
Blue River Technology Agricultural Robot Functions
Figure 3-63
Blue River Precision Lettuce Thinning - 80/84" beds
Table 3-64
cRops Robotic Platform Functions
Table 3-65
cRops Robot System European Project Supporters
Figure 3-66
cRops Robot System
Figure 3-67
cRops Robot Target System
Figure 3-68
Jaybridge Robotics Driverless Tractor
Figure 3-69
IBM / Bari Fishing Market App
Figure 3-70

IBM / Bari Real Time Fishing Market App
Figure 3-71
IBM / Bari Fishing Market Need Matching App
Figure 3-72
Small Tractor Used For Manual Artichokes Harvesting
Figure 3-73
LSU AgBot
Table 3-74
Harvard Robobee Robot Applications
Table 3-75
Nature-Inspired Robotic Research Aims
Figure 3-76
Robobee Boby, Brain, Colony
Figure 3-77
Harvard Robobee Propulsive Efficiency
Figure 3-78
Robobee Boby, Brain, Colony
Figure 3-79
Harvard Robobee Studies of Stability And Control In Unsteady,

Structured Wakes
Table 3-80
Harvard Robobee Sensor Networks
Figure 3-81
Harvard Robobee Computationally-Efficient Control System
Table 3-82
Harvard Robobee Sensor Network Design Challenges
Table 3-83
Harvard Robobee Challenges In Development Of A Sensor Network
Table 3-84
Harvard Robobee Sensor Network Context Challenges
Table 3-85
Harvard Robobee Sensor Network Elements
Table 3-86
Harvard Robobee Sensor Network Limitations
Table 3-87
Harvard Robobee Software Language Limitations
Table 3-88
Harvard Robobee Software Language Current Efforts
Figure 3-89
Robomow RL850 Automatic Lawn Mower
Figure 3-90
MIT smart gardener robot
Figure 3-91
Carnegie Mellon Self-Guided Farm Equipment
Figure 3-92
Carnegie Mellon Self-Guided Equipment Running on Farm
Figure -3-93
Cesar the LettuceBot


Figure 3-94
Benefits of Lettuce Harvesting Robot
Figure 3-95
Rosphere
Figure 3-96
Rosphere Induction Of Forward/Backward And Turning Movements
Figure 3-97
University of California, Davis Robot For Harvesting Strawberries
Table 3-98
Wall-Ye V.I.N. Robot Functions
Table 3-99
Wall-Ye V.I.N. Robot Technology
Table 3-100
Wall-Ye V.I.N. Robot Features
Figure 3-101
Vision Robotics Snippy Robotic Vine Pruner
Figure 3-102
Nogchui Autonomous Tractor Grading
Figure 3-103
Nogchui Autonomous Tractor Working Field
Figure 3-104
Professor Nogchui Autonomous Tractor Navigation Map Information
Figure 3-105
Microsoft Agricultural Robot Software
Figure 3-106
Herder Robotic Rover
Figure 3-107
Chinese Farmbot Tractor Image
Figure 3-108

3D Robotics
Figure 3-109
3D Robotics Drone Spray Application
Figure 3-110
3D Robotics Uses Pesticides And Fungicides Only When Needed
Figure 3-111
3D Robotics Data For Marketing
Figure 3-112
3D Robotics Aerial Views of Crops
Figure 3-113
3D Robotics Aerial Views Multicopter To Fly Over Vineyards
Figure 3-114
Lely Automatic Milking
Figure 3-115
Astronaut Milking Robot
Figure 3-116
Lely Milking System Farm
Figure 3-117
Lely Cattle Feeding System Farm
Figure 3-118
Lely Automated Process for Managing Milking and Farm
Figure 3-119

Lely Correct Cattle Feeding Management
Figure 3-120
Lely Automated Process Cattle Feeding Management
Figure 3-121
Lely Multi-Barn Cattle Feeding Management
Figure 3-122
Lely Cattle Milking Management
Figure 3-123
Kyoto University Tomato Harvesting Robot
Figure 3-124
Kyoto University Fruit Harvesting Robots In Greenhouse
Figure 3-125
Kyoto University Tomato Cluster Harvesting Robot
Figure 3-126
Kyoto University Strawberry Harvesting Robot In Plant Factory
Figure 3-127
RHEA Robot Fleets for Seeding
Figure 3-128
RHEA Robot Fleet Mapping for Seeding
Figure 3-129
Robot Fleet Deterministic Route Planning for Seeding
Figure 3-130
Orthogonal Inter Row Mechanical Weeding for Organic Farming
Table 3-131

HGCA Laser Weeding
Figure 3-132
RHEA Laser Weeding
Figure 3-133
RHEA Horibot Cutter and Sprayer
Figure 3-134
RHEA Broad leafed Weed Sensing And Spraying
Table 3-135
RHEA Broad Leafed Weed Sensing And Spraying
Figure 3-136
RHEA Multiple Small Intelligent Machines Replace Large Manned Tractors
Figure 3-137
RHEA Cooperative Fleet Of Robots
Figure 3-138
RHEA Hexacopter (Aerial Mobile Unit)
Table 4-1
Harvest Automation Proprietary Sensor Technology Functions
Table 4-2
Harvest Automation Robot System Architecture
Table 4-3
Proprietary Sensor Technology
Table 4-4
System Design & Architecture
Table 4-5
Tight Scientific Collaboration Between Different Disciplines
Figure 4-6
IEEE Agricultural Robots
Figure 4-7
IEEE Orchard Robots
Figure 4-8
IEEE Automated Agricultural Robot

Table 5-1
ABB Product Launches
Table 5-2
ABB Global Lab Target Technologies
Table 5-3
ABB's Global Lab Automation Target Solutions
Table 5-4
ABB Active Current Research Areas
Figure 5-5
Agrobot Strawberry Picker
Figure 5-6
Agrobot Strawberry Picker
Figure 5-7
Agrobot Robot for Agriculture
Table 5-8
Agrobot Innovation and Technology for Agribusiness
Figure 5-9
Agrobot Innovation and Technology for Agribusiness
Table 5-10
Agrobot SW6010 Support
Table 5-11
cRops technology Functions
Table 5-12
cRops Intelligent Tools
Table 5-13
cRops Target Markets
Table 5-14
cRops Robotic Platform Customized Automated Processes
Figure 5-15

Fanuc Revenue
Figure 5-16
Fanuc Revenue
Figure 5-17
Boston Dynamic LS3
Figure 5-18
Boston Dynamic CHEETAH
Figure 5-19
Boston Dynamic Atlas
Figure 5-20
Boston Dynamic BigDog
Figure 5-21
Boston Dynamics LittleDog -
Table 5-22
Google Autonomous Vehicles Technology
Table 5-23
Harvard Robobee Project Characteristics
Figure 5-24
Harvard Robobee Kilobot Robot Group
Table 5-25
Harvest Automation Robot Navigation
Table 5-26
Harvest Automation Robot Sensor Network Functions
Table 5-27
IBM Systems Target Industries
Table 5-28
Jaybridge Robotics Software Solutions
Table 5-29
Jaybridge Robotics Software Functions
Figure 5-30
Kuka Positioning with Smart Tools
Figure 5-31
Lely's Astronaut A4 Milking Robot
Table 5-32
Millennial Net's MeshScape System Functions
Table 5-33

MeshScape GO Deployment Components:
Table 5-34
National Agriculture and Food Research Organization (NARO) Plan Goals
Figure 5-35
Precise Path Robotics
Figure 5-36
Sicily Small Tractor Used For Manual Artichoke Harvesting
Figure 5-37
Shibuya Kogyo Robotic System For Leaflet & Spoon Placement
Figure 5-38
Shibuya Kogyo Robotic Collating System
Figure 5-39
Shibuya Kogyo Automated Aseptic Environmental Monitoring System
Table 5-40
Universidad Politécnica de Madrid Projects
Figure 5-41
UC Davis Using Yahama Helicopter Drones For Crop Dusting
Figure 5-42
Yamaha Crop Dusting Initiatives
Figure 5-43
YASKAWA Electric Group Businesses

To order this report: Agricultural Robots: Market Shares, Strategies, and Forecasts, Worldwide, 2014 to 2020
http://www.reportlinker.com/p02003735/Agricultural-Robots-Market-Shares-Strategies-and-Forecasts-Worldwide-2014-to-2020.html#utm_source=prnewswire&utm_medium=pr&utm_campaign=Agriculture

__________________________
Contact Clare: [email protected]
US: (339)-368-6001
Intl: +1 339-368-6001

SOURCE Reportlinker

More Stories By PR Newswire

Copyright © 2007 PR Newswire. All rights reserved. Republication or redistribution of PRNewswire content is expressly prohibited without the prior written consent of PRNewswire. PRNewswire shall not be liable for any errors or delays in the content, or for any actions taken in reliance thereon.

Cloud Expo Breaking News
As more applications and services move "to the cloud" (public or on-premise) cloud environments are increasingly adopting and building out traditional enterprise features. This in turn is enabling and encouraging cloud adoption from enterprise users. In many ways the definition is blurring as features like continuous operation, geo-distribution or on-demand capacity become the norm. NuoDB is involved in both building enterprise software and using enterprise cloud capabilities. In his session at 15th Cloud Expo, Seth Proctor, CTO at NuoDB, Inc., will discuss the experiences from building, deploying and using enterprise services and suggest some ways to approach moving enterprise applications into a cloud model.
Until recently, many organizations required specialized departments to perform mapping and geospatial analysis, and they used Esri on-premise solutions for that work. In his session at 15th Cloud Expo, Dave Peters, author of the Esri Press book Building a GIS, System Architecture Design Strategies for Managers, will discuss how Esri has successfully included the cloud as a fully integrated SaaS expansion of the ArcGIS mapping platform. Organizations that have incorporated Esri cloud-based applications and content within their business models are reaping huge benefits by directly leveraging cloud-based mapping and analysis capabilities within their existing enterprise investments. The ArcGIS mapping platform includes cloud-based content management and information resources to more widely, efficiently, and affordably deliver real-time actionable information and analysis capabilities to your organization.
In his session at 15th Cloud Expo, Mark Hinkle, Senior Director, Open Source Solutions at Citrix Systems Inc., will provide overview of the open source software that can be used to deploy and manage a cloud computing environment. He will include information on storage, networking(e.g., OpenDaylight) and compute virtualization (Xen, KVM, LXC) and the orchestration(Apache CloudStack, OpenStack) of the three to build their own cloud services. Speaker Bio: Mark Hinkle is the Senior Director, Open Source Solutions, at Citrix Systems Inc. He joined Citrix as a result of their July 2011 acquisition of Cloud.com where he was their Vice President of Community. He is currently responsible for Citrix open source efforts around the open source cloud computing platform, Apache CloudStack and the Xen Hypervisor. Previously he was the VP of Community at Zenoss Inc., a producer of the open source application, server, and network management software, where he grew the Zenoss Core project to over 10...
Almost everyone sees the potential of Internet of Things but how can businesses truly unlock that potential. The key will be in the ability to discover business insight in the midst of an ocean of Big Data generated from billions of embedded devices via Systems of Discover. Businesses will also need to ensure that they can sustain that insight by leveraging the cloud for global reach, scale and elasticity. In his session at Internet of @ThingsExpo, Mac Devine, Distinguished Engineer at IBM, will discuss bringing these three elements together via Systems of Discover.
Cloud and Big Data present unique dilemmas: embracing the benefits of these new technologies while maintaining the security of your organization’s assets. When an outside party owns, controls and manages your infrastructure and computational resources, how can you be assured that sensitive data remains private and secure? How do you best protect data in mixed use cloud and big data infrastructure sets? Can you still satisfy the full range of reporting, compliance and regulatory requirements? In his session at 15th Cloud Expo, Derek Tumulak, Vice President of Product Management at Vormetric, will discuss how to address data security in cloud and Big Data environments so that your organization isn’t next week’s data breach headline.
The cloud is everywhere and growing, and with it SaaS has become an accepted means for software delivery. SaaS is more than just a technology, it is a thriving business model estimated to be worth around $53 billion dollars by 2015, according to IDC. The question is – how do you build and scale a profitable SaaS business model? In his session at 15th Cloud Expo, Jason Cumberland, Vice President, SaaS Solutions at Dimension Data, will give the audience an understanding of common mistakes businesses make when transitioning to SaaS; how to avoid them; and how to build a profitable and scalable SaaS business.
SYS-CON Events announced today that Gridstore™, the leader in software-defined storage (SDS) purpose-built for Windows Servers and Hyper-V, will exhibit at SYS-CON's 15th International Cloud Expo®, which will take place on November 4–6, 2014, at the Santa Clara Convention Center in Santa Clara, CA. Gridstore™ is the leader in software-defined storage purpose built for virtualization that is designed to accelerate applications in virtualized environments. Using its patented Server-Side Virtual Controller™ Technology (SVCT) to eliminate the I/O blender effect and accelerate applications Gridstore delivers vmOptimized™ Storage that self-optimizes to each application or VM across both virtual and physical environments. Leveraging a grid architecture, Gridstore delivers the first end-to-end storage QoS to ensure the most important App or VM performance is never compromised. The storage grid, that uses Gridstore’s performance optimized nodes or capacity optimized nodes, starts with as few a...
SYS-CON Events announced today that Solgenia, the global market leader in Cloud Collaboration and Cloud Infrastructure software solutions, will exhibit at SYS-CON's 15th International Cloud Expo®, which will take place on November 4–6, 2014, at the Santa Clara Convention Center in Santa Clara, CA. Solgenia is the global market leader in Cloud Collaboration and Cloud Infrastructure software solutions. Designed to “Bridge the Gap” between personal and professional social, mobile and cloud user experiences, our solutions help large and medium-sized organizations dramatically improve productivity, reduce collaboration costs, and increase the overall enterprise value by bringing collaboration and infrastructure solutions to the cloud.
Cloud computing started a technology revolution; now DevOps is driving that revolution forward. By enabling new approaches to service delivery, cloud and DevOps together are delivering even greater speed, agility, and efficiency. No wonder leading innovators are adopting DevOps and cloud together! In his session at DevOps Summit, Andi Mann, Vice President of Strategic Solutions at CA Technologies, will explore the synergies in these two approaches, with practical tips, techniques, research data, war stories, case studies, and recommendations.
Enterprises require the performance, agility and on-demand access of the public cloud, and the management, security and compatibility of the private cloud. The solution? In his session at 15th Cloud Expo, Simone Brunozzi, VP and Chief Technologist(global role) for VMware, will explore how to unlock the power of the hybrid cloud and the steps to get there. He'll discuss the challenges that conventional approaches to both public and private cloud computing, and outline the tough decisions that must be made to accelerate the journey to the hybrid cloud. As part of the transition, an Infrastructure-as-a-Service model will enable enterprise IT to build services beyond their data center while owning what gets moved, when to move it, and for how long. IT can then move forward on what matters most to the organization that it supports – availability, agility and efficiency.
Every healthy ecosystem is diverse. This is especially true in cloud ecosystems, where portability and interoperability are more important than old enterprise models of proprietary ownership. In his session at 15th Cloud Expo, Mark Baker, Server Product Manager at Canonical/Ubuntu, will discuss how single vendors used to take the lead in creating and delivering technology, but in a cloud economy, where users want tools of their preference, when and where they need them, it makes no sense.
The 15th International Cloud Expo has just expanded its conference program, to bring together Cloud Computing, APM, APIs, Security, Big Data, Internet of Things, DevOps and WebRTC at one location. Cloud Expo is the single show where delegates and technology vendors can meet to experience and discuss the entire world of the cloud. With cloud computing driving a higher percentage of enterprise IT budgets every year, it becomes increasingly important to learn about the latest technology developments and solutions.
SYS-CON Events announced today that Bsquare Corporation, a leading enabler of smart connected systems, has been named “Bronze Sponsor” of SYS-CON's Internet of @ThingsExpo, which will take place on November 4–6, 2014, at the Santa Clara Convention Center in Santa Clara, CA. Bsquare is a global leader of embedded software solutions. We enable smart connected systems at the device level and beyond that millions use every day and provide actionable data solutions for the growing Internet of Things (IoT) market. We empower our world-class customers with our products, services and solutions to achieve innovation and success.
SYS-CON Events announced today that NuoDB, Inc., the leader in webscale distributed database technology, has been named “Bronze Sponsor” of SYS-CON's 15th International Cloud Expo®, which will take place on November 4–6, 2014, at the Santa Clara Convention Center in Santa Clara, CA. NuoDB was launched in 2010 by industry-renowned database architect Jim Starkey and accomplished software CEO Barry Morris to deliver a webscale distributed database management system that is specifically designed for the cloud and the modern datacenter.
SYS-CON Events announced today that Cloudian, Inc., the leading provider of hybrid cloud storage solutions, has been named “Bronze Sponsor” of SYS-CON's 15th International Cloud Expo®, which will take place on November 4–6, 2014, at the Santa Clara Convention Center in Santa Clara, CA. Cloudian is a Foster City, Calif.-based software company specializing in cloud storage. Cloudian HyperStore® is an S3-compatible cloud object storage platform that enables service providers and enterprises to build reliable, affordable and scalable hybrid cloud storage solutions. Cloudian actively partners with leading cloud computing environments including Amazon Web Services, Citrix Cloud Platform, Apache CloudStack, OpenStack and the vast ecosystem of S3 compatible tools and applications. Cloudian's customers include Vodafone, Nextel, NTT, Nifty, and LunaCloud. The company has additional offices in China and Japan.