Welcome!

Cloud Expo Authors: Liz McMillan, Elizabeth White, Cloud Ventures, Pat Romanski, Michelle Drolet

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: clare@reportlinker.com
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
More and more enterprises today are doing business by opening up their data and applications through APIs. Though forward-thinking and strategic, exposing APIs also increases the surface area for potential attack by hackers. To benefit from APIs while staying secure, enterprises and security architects need to continue to develop a deep understanding about API security and how it differs from traditional web application security or mobile application security. In his session at 14th Cloud Expo, Sachin Agarwal, VP of Product Marketing and Strategy at SOA Software, will walk you through the various aspects of how an API could be potentially exploited. He will discuss the necessary best practices to secure your data and enterprise applications while continue continuing to support your business’s digital initiatives.
The revolution that happened in the server universe over the past 15 years has resulted in an eco-system that is more open, more democratically innovative and produced better results in technically challenging dimensions like scale. The underpinnings of the revolution were common hardware, standards based APIs (ex. POSIX) and a strict adherence to layering and isolation between applications, daemons and kernel drivers/modules which allowed multiple types of development happen in parallel without hindering others. Put simply, today's server model is built on a consistent x86 platform with few surprises in its core components. A kernel abstracts away the platform, so that applications and daemons are decoupled from the hardware. In contrast, networking equipment is still stuck in the mainframe era. Today, networking equipment is a single appliance, including hardware, OS, applications and user interface come as a monolithic entity from a single vendor. Switching between different vendor'...
You use an agile process; your goal is to make your organization more agile. What about your data infrastructure? The truth is, today’s databases are anything but agile – they are effectively static repositories that are cumbersome to work with, difficult to change, and cannot keep pace with application demands. Performance suffers as a result, and it takes far longer than it should to deliver on new features and capabilities needed to make your organization competitive. As your application and business needs change, data repositories and structures get outmoded rapidly, resulting in increased work for application developers and slow performance for end users. Further, as data sizes grow into the Big Data realm, this problem is exacerbated and becomes even more difficult to address. A seemingly simple schema change can take hours (or more) to perform, and as requirements evolve the disconnect between existing data structures and actual needs diverge.
Cloud backup and recovery services are critical to safeguarding an organization’s data and ensuring business continuity when technical failures and outages occur. With so many choices, how do you find the right provider for your specific needs? In his session at 14th Cloud Expo, Daniel Jacobson, Technology Manager at BUMI, will outline the key factors including backup configurations, proactive monitoring, data restoration, disaster recovery drills, security, compliance and data center resources. Aside from the technical considerations, the secret sauce in identifying the best vendor is the level of focus, expertise and specialization of their engineering team and support group, and how they monitor your day-to-day backups, provide recommendations, and guide you through restores when necessary.
Cloud scalability and performance should be at the heart of every successful Internet venture. The infrastructure needs to be resilient, flexible, and fast – it’s best not to get caught thinking about architecture until the middle of an emergency, when it's too late. In his interactive, no-holds-barred session at 14th Cloud Expo, Phil Jackson, Development Community Advocate for SoftLayer, will dive into how to design and build-out the right cloud infrastructure.
SYS-CON Events announced today that SherWeb, a long-time leading provider of cloud services and Microsoft's 2013 World Hosting Partner of the Year, will exhibit at SYS-CON's 14th International Cloud Expo®, which will take place on June 10–12, 2014, at the Javits Center in New York City, New York. A worldwide hosted services leader ranking in the prestigious North American Deloitte Technology Fast 500TM, and Microsoft's 2013 World Hosting Partner of the Year, SherWeb provides competitive cloud solutions to businesses and partners around the world. Founded in 1998, SherWeb is a privately owned company headquartered in Quebec, Canada. Its service portfolio includes Microsoft Exchange, SharePoint, Lync, Dynamics CRM and more.
The world of cloud and application development is not just for the hardened developer these days. In their session at 14th Cloud Expo, Phil Jackson, Development Community Advocate for SoftLayer, and Harold Hannon, Sr. Software Architect at SoftLayer, will pull back the curtain of the architecture of a fun demo application purpose-built for the cloud. They will focus on demonstrating how they leveraged compute, storage, messaging, and other cloud elements hosted at SoftLayer to lower the effort and difficulty of putting together a useful application. This will be an active demonstration and review of simple command-line tools and resources, so don’t be afraid if you are not a seasoned developer.
SYS-CON Events announced today that BUMI, a premium managed service provider specializing in data backup and recovery, will exhibit at SYS-CON's 14th International Cloud Expo®, which will take place on June 10–12, 2014, at the Javits Center in New York City, New York. Manhattan-based BUMI (Backup My Info!) is a premium managed service provider specializing in data backup and recovery. Founded in 2002, the company’s Here, There and Everywhere data backup and recovery solutions are utilized by more than 500 businesses. BUMI clients include professional service organizations such as banking, financial, insurance, accounting, hedge funds and law firms. The company is known for its relentless passion for customer service and support, and has won numerous awards, including Customer Service Provider of the Year and 10 Best Companies to Work For.
Chief Security Officers (CSO), CIOs and IT Directors are all concerned with providing a secure environment from which their business can innovate and customers can safely consume without the fear of Distributed Denial of Service attacks. To be successful in today's hyper-connected world, the enterprise needs to leverage the capabilities of the web and be ready to innovate without fear of DDoS attacks, concerns about application security and other threats. Organizations face great risk from increasingly frequent and sophisticated attempts to render web properties unavailable, and steal intellectual property or personally identifiable information. Layered security best practices extend security beyond the data center, delivering DDoS protection and maintaining site performance in the face of fast-changing threats.
From data center to cloud to the network. In his session at 3rd SDDC Expo, Raul Martynek, CEO of Net Access, will identify the challenges facing both data center providers and enterprise IT as they relate to cross-platform automation. He will then provide insight into designing, building, securing and managing the technology as an integrated service offering. Topics covered include: High-density data center design Network (and SDN) integration and automation Cloud (and hosting) infrastructure considerations Monitoring and security Management approaches Self-service and automation
In his session at 14th Cloud Expo, David Holmes, Vice President at OutSystems, will demonstrate the immense power that lives at the intersection of mobile apps and cloud application platforms. Attendees will participate in a live demonstration – an enterprise mobile app will be built and changed before their eyes – on their own devices. David Holmes brings over 20 years of high-tech marketing leadership to OutSystems. Prior to joining OutSystems, he was VP of Global Marketing for Damballa, a leading provider of network security solutions. Previously, he was SVP of Global Marketing for Jacada where his branding and positioning expertise helped drive the company from start-up days to a $55 million initial public offering on Nasdaq.
Performance is the intersection of power, agility, control, and choice. If you value performance, and more specifically consistent performance, you need to look beyond simple virtualized compute. Many factors need to be considered to create a truly performant environment. In his General Session at 14th Cloud Expo, Marc Jones, Vice President of Product Innovation for SoftLayer, will explain how to take advantage of a multitude of compute options and platform features to make cloud the cornerstone of your online presence.
Are you interested in accelerating innovation, simplifying deployments, reducing complexity, and lowering development costs? The cloud is changing the face of application development and deployment, with enterprise-grade infrastructure and platform services making it possible for you to build and rapidly scale enterprise applications. In his session at 14th Cloud Expo, Gene Eun, Sr. Director, Oracle Cloud at Oracle, will discuss the latest solutions and strategies for application developers and enterprise IT organizations to leverage Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) to build and deploy modern business applications in the cloud.
Hybrid cloud refers to the federation of a public and private cloud environment for the purpose of extending the elastic and flexibility of compute, storage and network capabilities, in an on-demand, pay-as-you go basis. The hybrid approach allows a business to take advantage of the scalability and cost-effectiveness that a public cloud computing environment offers without exposing mission-critical applications and data to third-party vulnerabilities. Hybrid cloud environments involve complex management challenges. First, organizations struggle to maintain control over the resources that lie outside of their managed IT scope. They also need greater infrastructure visibility to help reduce maintenance costs and ensure that their company data and resources are properly handled and secured.
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. At NuoDB we're involved in both building enterprise software and using enterprise cloud capabilities. In his session at 14th Cloud Expo, Seth Proctor, CTO of NuoDB, Inc., will cover experiences from building, deploying and using enterprise services and suggest some ways to approach moving enterprise applications into a cloud model.