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市場調查報告書

農業用機器人和無人機 2018-2038年:技術、市場、企業

Agricultural Robots and Drones 2018-2038: Technologies, Markets, and Players

出版商 IDTechEx Ltd. 商品編碼 368201
出版日期 內容資訊 英文 213 Pages
商品交期: 最快1-2個工作天內
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農業用機器人和無人機 2018-2038年:技術、市場、企業 Agricultural Robots and Drones 2018-2038: Technologies, Markets, and Players
出版日期: 2018年01月30日 內容資訊: 英文 213 Pages
簡介

本報告聚焦農業用機器人及無人機,分析機器人市場與技術開發如何影響農業商業面,如何實現超精密農業,以及解決課題時的有效性等。

第1章 摘要整理

第2章 大型拖拉機的自動運行

  • 全球拖拉機銷售數量
  • 作物生產額及每個地區的平均農場規模
  • 主要農業設備企業概要
  • 拖拉機導引和大型拖拉機自動駕駛技術
  • 大型拖拉機自動駕駛
  • 自動駕駛拖拉機的10年預測
  • 具主從式或地面引導功能的大型自動拖拉機
  • 完全自動無人大型拖拉機
  • 無人自動大型拖拉機的技術進步
  • 拖拉機引導、自動駕駛及完全自動拖拉機/聯合收穫機的10年預測

第3章 自主機器人農業平台

  • 自主型小型農業用機器人
  • 自主型農業機器人平台
  • 自主型機器人數據偵查10年預測

第4章 機器人除草

  • 從載人散佈至自動超精密除草
  • 作物保護化學藥品銷售:全球主要供應商
  • 全球和中國主要除草劑供應商銷售
  • 全球除草劑消費數據
  • 全球草甘膦 (除草劑) 消費及市場、其他

第5章 機器人蔬菜間拔和收穫

  • 自動萵苣間拔機器人
  • 蘆筍收穫應自動化的理由
  • 自動蘆筍收穫
  • 機器人/自動蘆筍收穫
  • 機器人的萵苣間拔、除草服務潛在市場規模
  • 機器人的萵苣間拔、蔬菜收穫10年市場預測:按技術、領域

第6章 機器人新鮮水果採收

  • 農作物、非新鮮水果的採收大部分為機械化
  • 新鮮水果採收大部分仍是手動
  • 以機械協助新鮮水果採收的部分在過去50年內無顯著進化
  • 新興的利用機器人協助新鮮水果採收技術
  • 機器人果樹園數據偵查和產量估計、其他

第7章 葡萄修剪機器人

  • 自動機器人葡萄園偵查和修剪機器

第8章 溫室和苗床

  • 溫室和苗床的自動機器人

第9章 機器人播種機

  • 精密播種的技術變動率
  • 利用機器人的種植

第10章 機器人酪農

  • 全球酪農場規模趨勢及平均
  • 全球乳牛數量和分佈:按領域
  • 全球及各國潛力市場
  • 機器人擠奶機概要
  • 自動機器人飼料推送、其他

第11章 空中數據蒐集

  • 衛星 vs. 飛行機 vs. 無人機mapping與偵查
  • 農業中使用空中影像的優點
  • 日本稻田中無人機的有害生物管理
  • 地區播種的無人機、直昇機
  • 市場的無人農業無人機、其他

第12章 抓持技術

  • 新鮮水果採收的吸引型末端操作技術
  • 新鮮水果採收的單純有效機器人末端操作器
  • 新鮮水果出貨作業的軟機器人型末端操作器
  • 新鮮水果採收的機器人末端操作器技術
  • 農業用機器人的靈巧機器手
  • 靈巧機器手實例

第13章 導航技術 (RTK、LIDAR、LASER及其他)

  • RTK系統:操作、性能及價值鏈
  • LIDAR:基本的操作原理
  • 市售及開發中的LIDAR概要
  • 市售及開發中的各LIDAR性能比較
  • 農業用機器人的各LIDAR適宜性評估、其他

第14章 市場預測、業務情勢、企業定位及企業簡介

  • 農業用機器人、無人機10年預測:按類型和/或技術
  • 農業用機器人、無人機10年預測:按類型和/或功能
  • 自動型、行動農業機器人、無人機10年預測:按類型和/或功能
  • 拖拉機導引、自動駕駛及完全自動拖拉機/聯合收穫機10年預測
  • 自動機器人數據偵查10年預測
  • 機器人除草10年預測:按技術類型、其他

本網頁內容可能與最新版本有所差異。詳細情況請與我們聯繫。

目錄

This report is focused on agricultural robots and drones. It analyses how robotic market and technology developments will change the business of agriculture, enabling ultra-precision and/or autonomous farming and helping address key global challenges.

It develops a detailed roadmap of how robotic technology will enter into different aspects of agriculture, how it will change the way farming is done and transform it's value chain, how it becomes the future of agrochemicals business and how it will modify the way we design agricultural machinery.

In particular, this report provides:

Market forecasts: In our report we provide granular twenty-year (2018-2038) market forecasts for 16 market categories. We built a twenty-year model because our technology roadmap suggests that these changes will take place over long timescales. Our market forecasts are often segmented by territory. All our assumptions and data points are clearly explained.

More specifically, we cover the following 16 categories: static milking robots, mobile dairy farm robotics, autonomous agricultural small robots (data scouts, weeding and multi-platform), autonomous tractors (simple guidance, autosteer, fully unmanned autonomy), robotic implements (simple and highly intelligent), robotic strawberry harvesting, robotic fresh fruit picking, and agricultural drones (data scouts, data services/analytics, multi-functional drones, unmanned spraying helicopters).

Technology assessment: We provide a detailed technology assessment covering all the key robotic/drone projects, prototypes and commercial products relevant to the agricultural sector. Furthermore, we offer an overview and assessment of key technological components such as vision sensors, LIDARs, novel end-effectors, and hyper/multi-spectral sensors. Our technology roadmaps also outline how different equipment is increasingly becoming vision-enabled, intelligent and unmanned/autonomous.

This report also analyses the key enabling hardware and software technologies underpinning new robotics. For the hardware part, we consider long-term price and performance trends in transistors, memory, energy storage, electric motors, GPS, cameras, and MEMS technology. For the software side, we consider the latest achievements in deep learning applications in various fields.

Application assessment: A detailed application assessment covering dairy farms, fresh fruit harvesting, organic farming, crop protection, data mapping, seeding, nurseries, and so on. For each application/sector, a detailed overview of the existing industry is given, the needs for and the challenges facing the robotic technology are analysed, the addressable market size is estimated by territory, and granular ten-year market projections are given.

Company profiles: More than 20 interview-based full company profiles with detailed SWOT analysis, 45 company profiles without SWOT analysis, and the works of more than 80 companies/research groups listed and summarized.

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20-year market forecasts for all aspects of agricultural robots and drones. Note that in our previously-published forecasts we had primarily disclosed the forecast at the level of the value of the automation suite and not the entire vehicle automation level. Further note that we have now transitioned our forecast model to a 20-year timescale. We believe this is sensible since many changes will take place on long time scales. We have also defined some of our forecast categories to better reflect the market changes. Further note that the market could reach higher levels (close to $45Bn by 2038) in a highly accelerated technology progression and market adoption scenario. This scenario is not included in the report. Source IDTechEx

Will tractors evolve towards full unmanned autonomy?

Tractor guidance and autosteer are well-established technologies. In the short to medium terms, both will continue their growth thanks to improvements and cost reductions in RTK GPS technology. Indeed, we estimate that around 700k tractors equipped with autosteer or tractor guidance will be sold in 2028. We also assess that tractor guidance sales, in unit numbers and revenue, will peak around 2027-2028 before a gradual decline commences. This is because the price differential between autosteer and tractor guidance will narrow, causing autosteer to attract more of the demand. Note that our model accounts for the declining cost of navigational autonomy (e.g., level 4 for autosteer).

Unmanned autonomous tractors have also been technologically demonstrated with large-scale market introduction largely delayed not by technical issues but by regulation, high sensor costs and the lack of farmers' trust. This will start to slowly change from 2024 onwards. However the sales will only slowly grow. We estimate that around 40k unmanned fully-autonomous (level 5) tractors will be sold in 2038. The uptake will remain slow as users will only slowly become convinced that transitioning from level 4 to level 5 autonomy is value for money. This process will be helped by the rapidly falling price of the automation suite.

Overall, our model suggests that tractors with some degree of autonomy will become a $27Bn market at the vehicle level (our model also forecasts the added value that navigational autonomy provides).

The rise of fleets of small agricultural robots

Autonomous mobile robots are causing a paradigm shift in the way we envisage commercial and industrial vehicles. In traditional thinking bigger is often better. This is because bigger vehicles are faster and are thus more productive. This thinking holds true so long as each vehicle requires a human driver. The rise of autonomous mobility is however upending this long-established notion: fleets of small slow robots will replace or complement large fast manned vehicles.

These robots appear like strange creatures at first: they are small, slow, and lightweight. They therefore are less productive on a per unit basis than traditional vehicles. The key to success however lies in fleet operation. This is because the absence of a driver per vehicle enables remote fleet operation. Our model suggests that there is a very achievable operator-to-fleet-size ratio at which such agrobots become commercially attractive in the medium term.

We are currently at the beginning of the beginning. Indeed, most examples of such robots are only in the prototype or early stage commercial trial phase. These robots however are now being trailed in larger numbers by major companies, whilst smaller companies are making very modest sales. The inflection point, our models suggest, will arrive in 2024 onwards. At this point, sales will rapidly grow. These small agrobot fleets themselves will also grow in capability, evolving from data acquisition to weeding to offering multiple functionalities. Overall, we anticipate a market as large as $900M and $2.5Bn by 2028 and 2038, respectively. This will become a significant business but even it will remain a small subset of the overall agricultural vehicle industry.

Implements will become increasingly intelligent

Implements predominantly perform a purely mechanical functional today. There are some notable exceptions, particularly in organic farming. Here, implements are equipped with simple row-following vision technology, enabling them to actively and precisely follow rows.

This is however changing as robotic implements become highly intelligent. Indeed, early versions essentially integrated multiple computers onto the implement. These are today used for advanced vision technology enabled by machine learning (e.g. deep learning). Here, the intelligent implements learn to distinguish between crops and weeds as the implement is pulled along the field, enabling them to take site-specific weeding action.

We anticipate that such implements will become increasingly common in the future. They are currently still in their early generations where the software is still learning, and the hardware is custom built and ruggedized by small firms. Recent activities including acquisitions by major firms suggest that this is changing.

Robotics finally succeed in fresh fruit harvesting?

Despite non-fresh fruit harvesting being largely mechanized, fresh fruit picking has remained mostly out of the reach of machines or robots. Picking is currently done using manual labour with machines at most playing the part of an aid that speeds up the manual work.

A limited number of fresh strawberry harvesters are already being commercially trialled and some are transitioning into commercial mode. Some versions require the farm layout to be changed and the strawberry to be trained to help the vision system identify a commercially-acceptable percentage of strawberries. Others are developing a more universal solution compatible with all varieties of strawberry farms.

Progress in fruit picking in orchards has been slower. This is because it is still a technically challenging task: the vision system needs to detect fruits inside a complex canopy whilst robotic arms need to rapidly, economically and gently pick the fruit.

This is however beginning to change, albeit slowly. Novel end effectors including those based on soft robotics that passively adapt to the fruit's shape, improved grasping algorithms underpinned by learning processes, low-cost good-enough robotic arms working in parallel, and better vision systems are all helping push this technology towards commercial viability.

We forecast that commercial sales- either as equipment sales or service provision- will slowly commence from 2024 and that an inflection point will arrive around 2028. Our model suggests a market value for $500M per year for fresh fruit picking in orchards.

Drones bring in increased data analytics into farming

Agriculture will be a major market for drones, reaching over $420m in 2028. Agriculture is emerging as one of the main addressable markets as the drone industry pivots away from consumer drones that have become heavily commoditized in recent years.

Drones in the first instances bring aerial data acquisition technology to even small farm operators by lowering the cost of deployment compared to traditional methods like satellites. This market will grow as more farmers become familiar with drone technology and costs become lower. The market will also change as it evolves: drones will take on more functionalities such as spraying and data analytic services that help farmers make data-driven decisions will grow in value.

Note that the use of unmanned aerial technology is not just limited to drones. Indeed, unmanned remote-controlled helicopters have already been spraying rice fields in Japan since early 1990s. This is a maturing technology/sector with overall sales in Japan having plateaued. This market may however benefit from a new injection of life as suppliers diversify into new territories

Robotics in dairy farms is a multibillion dollar market already

Thousands of robotic milking parlours have already been installed worldwide, creating a $1.6bn industry. This industry will continue its grow as productivity is established. Mobile robots are also already penetrating dairy farms, helping automate tasks such as feed pushing or manure cleaning. In general, this is a major robotic market about to which little attention is paid.

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Table of Contents

1. EXECUTIVE SUMMARY

  • 1.1. What is this report about?
  • 1.2. Growing population and growing demand for food
  • 1.3. Major crop yields are plateauing
  • 1.4. Employment in agriculture
  • 1.5. Global evolution of employment in agriculture
  • 1.6. Aging farmer population
  • 1.7. Trends in minimum wages globally
  • 1.8. Towards ultra precision agriculture via the variable rate technology route
  • 1.9. Ultra Precision farming will cause upheaval in the farming value chain
  • 1.10. Agricultural robotics and ultra precision agriculture will cause upheaval in agriculture's value chain
  • 1.11. Agriculture is one the last major industries to digitize: a look a investment in data analytics/management firms in agricultural and dairy farming
  • 1.12. The battle of business models between RaaS and equipment sales
  • 1.13. Transition towards to swarms of small, slow, cheap and unmanned robots
  • 1.14. Market and technology readiness by agricultural activity
  • 1.15. Technology progression towards driverless autonomous large-sized tractors
  • 1.16. Technology progression towards autonomous, ultra precision de-weeding
  • 1.17. Technology and progress progression roadmap for robotic fresh fruit harvesting
  • 1.18. 20-year market forecasts (2018 to 2038) for agricultural robots and drones segmented by 16 technologies
  • 1.19. Summary of market forecasts
  • 1.20. Tractors evolving towards full autonomy: 2018-2038 market forecasts in unit numbers segmented by level of navigational autonomy
  • 1.21. Tractors evolving towards full autonomy: 2018-2038 market forecasts in market value segmented by level of navigational autonomy
  • 1.22. Tractors evolving towards full autonomy: 2018-2038 market forecasts segmented by level of navigational autonomy (value of automation only)
  • 1.23. The rise of fleets of small autonomous robots: 2018-2038 market forecasts in unit numbers segmented by level of robot functionality
  • 1.24. The rise of fleets of small autonomous robots: 2018-2038 market forecasts in market value segmented by level of robot functionality
  • 1.25. Robotic tractor-pulled implements become increasingly intelligent and multi-functional: 2018-2038 market forecasts
  • 1.26. Robotic fresh fruit harvesting will overcome challenges but only in the long run: 2018-2038 market forecasts for robotic fresh fruit harvesting
  • 1.27. Agricultural drones become multi-purpose and data services capture more value: 2018-2038 market forecasts
  • 1.28. Robotic milking are already a major market: 2018-2038 market forecasts
  • 1.29. Mobile robots and drones dominate the agricultural robotic market: 2018-2038 market forecasts segmented by mobility vs stationary robots

2. AUTONOMOUS MOBILITY FOR LARGE TRACTORS

  • 2.1. Number of tractors sold globally
  • 2.2. Value of crop production and average farm sizes per region
  • 2.3. Revenues of top agricultural equipment companies
  • 2.4. Overview of top agricultural equipment companies
  • 2.5. Tractor Guidance and Autosteer Technology for Large Tractors
  • 2.6. Auto steer for large tractors
  • 2.7. Ten-year forecasts for autosteer tractors
  • 2.8. Master-slave or follow-me large autonomous tractors
  • 2.9. Fully autonomous driverless large tractors
  • 2.10. Fully autonomous unmanned tractors
  • 2.11. Technology progression towards driverless autonomous large-sized tractors
  • 2.12. Handsfree Hectar: fully autonomous human-free barley farming
  • 2.13. Tractors evolving towards full autonomy: 2018-2038 market forecasts in unit numbers segmented by level of navigational autonomy
  • 2.14. Tractors evolving towards full autonomy: 2018-2038 market forecasts in market value segmented by level of navigational autonomy
  • 2.15. Tractors evolving towards full autonomy: 2018-2038 market forecasts segmented by level of navigational autonomy (value of automation only)

3. AUTONOMOUS ROBOTIC AGRICULTURAL PLATFORMS

  • 3.1. Autonomous small-sized agricultural robots
  • 3.2. FENDT (AGCO) launches swarms of autonomous agrobots
  • 3.3. Autonomous agricultural robotic platforms

4. AUTONOMOUS ROBOTIC WEED KILLING

  • 4.1. From manned, broadcast towards autonomous, ultra precision de-weeding
  • 4.2. Crop protection chemical sales per top suppliers globally
  • 4.3. Sales of top global and Chinese herbicide suppliers
  • 4.4. Global herbicide consumption data
  • 4.5. Glyphosate consumption and market globally
  • 4.6. Regulations will impact the market for robotic weed killers?
  • 4.7. Penetration of herbicides in different field crops
  • 4.8. Growing challenge of herbicide-resistant weeds
  • 4.9. Autonomous weed killing robots
  • 4.10. Autonomous robotic weed killers
  • 4.11. Organic farming
  • 4.12. Robotic mechanical weeding for organic farming
  • 4.13. Technology progression towards autonomous, ultra precision de-weeding
  • 4.14. The rise of fleets of small autonomous robots: 2018-2038 market forecasts in unit numbers segmented by level of robot functionality
  • 4.15. The rise of fleets of small autonomous robots: 2018-2038 market forecasts in market value segmented by level of robot functionality

5. ROBOTIC IMPLEMENTS: WEEDING, VEGETABLE THINNING, AND HARVESTING

  • 5.1. Autonomous lettuce thinning robots
  • 5.2. Why asparagus harvesting should be automated
  • 5.3. Automatic asparagus harvesting
  • 5.4. Robotic/Automatic asparagus harvesting
  • 5.5. Addressable market size for robotic lettuce thinning and weeding service provision
  • 5.6. Robotic tractor-pulled implements become increasingly intelligent and multi-functional: 2018-2038 market forecasts

6. ROBOTIC FRESH FRUIT PICKING

  • 6.1. Field crop and non-fresh fruit harvesting is largely mechanized
  • 6.2. Fresh fruit picking remains largely manual
  • 6.3. Machining aiding humans in fresh fruit harvesting have not evolved in the past 50 years
  • 6.4. Emerging robotic fresh fruit harvest assist technologies
  • 6.5. Robot orchard data scouts and yield estimators
  • 6.6. Emerging robotic fresh fruit harvest assist technologies
  • 6.7. Robotic fresh apple harvesting
  • 6.8. Fresh fruit harvesting robots
  • 6.9. Technology and progress progression roadmap for robotic fresh fruit harvesting
  • 6.10. Addressable market size for robotic fresh apple-picking service provision
  • 6.11. Robotic fresh fruit harvesting will overcome challenges but only in the long run: 2018:2038 market forecasts for robotic fresh fruit harvesting
  • 6.12. Robotic fresh strawberry harvesting
  • 6.13. Evolution of fresh strawberry harvesting robots
  • 6.14. Fully autonomous strawberry picking robots with soft grippers
  • 6.15. Addressable market size for robotic fresh strawberry-picking service provision
  • 6.16. Ten-year market forecasts for robotic fresh strawberry harvesting by territory

7. VINE PRUNING ROBOTS

  • 7.1. Autonomous robotic vineyard scouts and pruners

8. GREENHOUSES AND NURSERIES

  • 8.1. Autonomous robotics for greenhouses and nurseries

9. ROBOTIC SEEDERS

  • 9.1. Variable rate technology for precision seed planting
  • 9.2. Robotic seed planting

10. ROBOTIC DAIRY FARMING

  • 10.1. Global trends and averages for dairy farm sizes
  • 10.2. Global number and distribution of dairy cows by territory
  • 10.3. Robotic milking parlours
  • 10.4. Overview of robotic milking parlours
  • 10.5. Autonomous robotic feed pushers
  • 10.6. Alternatives to autonomous robotic feed pushers
  • 10.7. Autonomous robotic shepherds
  • 10.8. Autonomous manure cleaning robots
  • 10.9. Ten-year market forecasts for robotic milking systems by country
  • 10.10. Robotic milking are already a major market: 2018-2038 market forecasts

11. AERIAL DATA COLLECTION AND DRONES

  • 11.1. Drones: dominant designs begin to emerge
  • 11.2. Drones: moving past the hype?
  • 11.3. Drones: company formation slows down
  • 11.4. Drones: global geographical spread of companies
  • 11.5. Drones: platforms commoditize?
  • 11.6. Drones: market forecasts
  • 11.7. Drones: application pipeline
  • 11.8. Satellite vs plane vs drone mapping and scouting
  • 11.9. Benefits of using aerial imaging in farming
  • 11.10. Unmanned drones in rice field pest control in Japan
  • 11.11. Unmanned drones and helicopters for field spraying
  • 11.12. Unmanned agriculture drones on the market
  • 11.13. Comparing different agricultural drones on the market
  • 11.14. Regulation barriers coming down?
  • 11.15. Agricultural drones: the emerging value chain
  • 11.16. Core company information on key agricultural drone companies
  • 11.17. Software opportunities: Vertical focused actionable analytics
  • 11.18. Drones: increasing autonomy
  • 11.19. Ten-year market forecasts for agricultural drones

12. ENABLING TECHNOLOGIES: GRIPPER TECHNOLOGY

  • 12.1. Suction-based end effector technologies for fresh fruit harvesting
  • 12.2. Simple and effective robotic end effectors for fruit harvesting
  • 12.3. Soft robotics based end effector technologies for fresh fruit handling
  • 12.4. Pneumatic soft actuator: extensible layer + fiber
  • 12.5. Soft actuator: self-contained McKibbern-type muscle
  • 12.6. Shape Deposition Manufacturing (SDM) Compliant Joint
  • 12.7. Fabrication processes for soft robotic actuators
  • 12.8. Robotic end effector technologies for fresh fruit harvesting
  • 12.9. Dexterous robotic hands for agricultural robotics
  • 12.10. Examples of dexterous robotic hands

13. ENABLING TECHNOLOGIES: NAVIGATIONAL TECHNOLOGIES (RTK, LIDAR, LASERS AND OTHERS)

  • 13.1. RTK systems: operation, performance and value chain
  • 13.2. Lidar- basic operation principles
  • 13.3. Review of LIDARs on the market or in development
  • 13.4. Performance comparison of different LIDARs on the market or in development
  • 13.5. Assessing suitability of different LIDAR for agricultural robotic applications
  • 13.6. Hyperspectral image sensors
  • 13.7. Hyperspectral imaging and precision agriculture
  • 13.8. Hyperspectral imaging in other applications
  • 13.9. Hyperspectral imaging sensors on the market
  • 13.10. Common multi-spectral sensors used with agricultural drones
  • 13.11. GeoVantage
  • 13.12. Why is new robotics becoming possible now? A hardware point of view
  • 13.13. Why is new robotics possible now?
  • 13.14. Transistors (computing): price evolution
  • 13.15. Transistors (computing): performance evolution
  • 13.16. Memory (RAM, hard driver and flash): price evolution in $/Mbit
  • 13.17. Memory: performance evolution in Gbit/ sq inch
  • 13.18. Sensors (Camera): price evolution
  • 13.19. Sensors (MEMS): price evolution
  • 13.20. Sensors (GPS): price and market adoption (in unit numbers) evolution of GPS sensors
  • 13.21. Is Lidar on a similar path as other robotic sensor technologies?
  • 13.22. Li ion battery: performance evolution in Wh/Kg and Wh/L
  • 13.23. Energy storage technologies: price evolution in $/kWh by sector
  • 13.24. Electric motors: evolution of size of a given output since 1910
  • 13.25. Artificial intelligence: waves of development
  • 13.26. Terminologies explained: AI, machine learning, artificial neural networks, deep neural networks
  • 13.27. Rising interesting in deep learning
  • 13.28. Algorithm training process in a single layer
  • 13.29. Towards deep learning by deepening the neutral network
  • 13.30. The main varieties of deep learning approaches explained
  • 13.31. Evolution of deep learning
  • 13.32. The rise of the big data quantified: fuel for deep learning applications
  • 13.33. Examples of milestones in deep learning AI: word recognition supresses human level
  • 13.34. Deepening the neutral network to increase accuracy rate
  • 13.35. GPUs: an enabling component for deep learning?
  • 13.36. Examples of milestones in deep learning AI: translation approaching human level performance
  • 13.37. Examples of milestones in deep learning AI: leap in progress in robotic grasping
  • 13.38. What is 'good enough' accuracy in deep learning?
  • 13.39. RoS and RoS-I: major open source movement slashing development costs and enticing OEMs to finally engage
  • 13.40. Robotic Operating System (RoS): Examples of cutting edge projects

14. COMPANY INTERVIEWS AND PROFILES

  • 14.1. Interview based company profiles
    • 14.1.1. Agrobot
    • 14.1.2. Blue River Technology
    • 14.1.3. DeepField Robotics
    • 14.1.4. F. Poulsen Engineering ApS
    • 14.1.5. Fresh Fruit Robotics
    • 14.1.6. Harvest CROO Robotics
    • 14.1.7. Ibex Automation
    • 14.1.8. miRobot
    • 14.1.9. Naio Technologies
    • 14.1.10. Nippon Signal
    • 14.1.11. Parrot
    • 14.1.12. Precision Hawk
    • 14.1.13. Quanergy
    • 14.1.14. Robotic Solutions
    • 14.1.15. Shadow Solutions
    • 14.1.16. Soft Robotics Inc
    • 14.1.17. Stream Technologies
    • 14.1.18. SwarmFarm Robotics
    • 14.1.19. Tillet and Hague
    • 14.1.20. Velodyne LIDAR
  • 14.2. Company Profiles
    • 14.2.1. 3D Robotics
    • 14.2.2. AGCO
    • 14.2.3. AgEagle
    • 14.2.4. AgJunction Inc
    • 14.2.5. Agribotix
    • 14.2.6. Airinov
    • 14.2.7. Autonomous Tractor Cooperation
    • 14.2.8. Beijing UniStrong Science and Technology (BUST)
    • 14.2.9. Case IH
    • 14.2.10. Dogtooth Technologies
    • 14.2.11. Empire Robotics
    • 14.2.12. Farmbot
    • 14.2.13. Festo Gamaya
    • 14.2.14. GrabIT
    • 14.2.15. Harvest Automation
    • 14.2.16. Headwall
    • 14.2.17. HerdDog
    • 14.2.18. HETO
    • 14.2.19. HiPhen
    • 14.2.20. Hortau
    • 14.2.21. John Deere
    • 14.2.22. Kinzes Autonomous Harvest System
    • 14.2.23. Kubota Corp
    • 14.2.24. L'Avion Jaune
    • 14.2.25. LeddarTech
    • 14.2.26. Lely
    • 14.2.27. LemnaTec
    • 14.2.28. Magnificant
    • 14.2.29. Mavrx
    • 14.2.30. McRobotic
    • 14.2.31. MicaSense
    • 14.2.32. Motorleaf
    • 14.2.33. NavCom
    • 14.2.34. Near Earth Autonomy
    • 14.2.35. Novariant
    • 14.2.36. Orbital Insight
    • 14.2.37. Pix4D
    • 14.2.38. Prospera
    • 14.2.39. Qubit Systems
    • 14.2.40. Robotics Plus
    • 14.2.41. Robotnik
    • 14.2.42. Scanse
    • 14.2.43. senseFly
    • 14.2.44. Sentra
    • 14.2.45. SkySquirrel
    • 14.2.46. SpelR
    • 14.2.47. Trimble
    • 14.2.48. UAV-IQ Precision Agriculture
    • 14.2.49. Urban Crops
    • 14.2.50. URSULA Agriculture
    • 14.2.51. VineRangers
    • 14.2.52. Yanmar
    • 14.2.53. Yara

ENABLING TECHNOLOGIES: LONG-TERM PRICE AND PERFORMANCE TRENDS IN KEY HARDWARE COMPONENTS ( TRANSISTORS, MEMORY, CAMERA, MEMS, GPS, BATTERIES, ELECTRIC MOTORS, ETC)

ENABLING TECHNOLOGY: SOFTWARE, DEEP LEARNING AND BIG DATA

TABLES AND FIGURES

  • Evolution of agricultural machinery from manual hoes through to robots
  • Population growth between 1950 and 2050 segmented by development stage
  • Income growth of developed and developing countries between 2005 and 2050
  • Expansion in global arable land between 1961 to 2050 in million ha
  • Grain yield improvements by territory for wheat, maize and rice between 1950 to 2012
  • Share of labour force working in agriculture between 1300 to 2000 for England, Netherlands, Italy France and Poland
  • Output per unit of labour in agriculture between 1961 to 2001 by country
  • Global map of agricultural employment for 1980s, 1990s, 2000s, and 2010s
  • Average age of principal farm operator in the USA between 192 to 2120
  • Average age of different farmer groups in Australia
  • Correlation between minimum wage and GPD per person at PPP
  • Minimum wage level in $/hr by country
  • Real hourly wage for non-supervisory hired farm works in the US between 1990 and 2012
  • Technology roadmap showing progression from constant rate technology, to variable rate technology and now ultra-precision technology
  • Existing and emerging value chain of agriculture showing how robotic technologies shift value away from traditional players
  • Assessing the pros and cons of RaaS vs. equipment sale model
  • Evolution of agriculture machinery from heavy, fast, large to light, slow and small
  • Soil compaction depth as a function of year caused by increased vehicle weight
  • Table showing that new robots need to be 24 times cheaper than traditional tractor models
  • Market and technology readiness chart placing different agricultural robotic technology on levels ranging from proof-of-concept to fully maturity
  • Market and technology readiness chart placing different agricultural robotic companies on levels ranging from proof-of-concept to fully maturity
  • Technology roadmap showings technology progression from manned tractor to tractor guidance to manned autosteer to master-slave and to fully autonomous tractors
  • Technology roadmap showing progress from manned aerial vehicles towards fully autonomous ultra-precision weeding
  • Technology roadmap showings the progression of robotic technology in fresh fruit harvesting
  • Ten-year market forecasts segmented by 14 agricultural robotics categories
  • Number of tractors sold globally between 2010 and 2014 by country
  • Number of tractors sold in the USA and Canada by horse power level between 2006 and 2015
  • Total value of crop production in $bn between 2009 and 2016 fir EU, USA, Brazil, CIS, China and India
  • Table showing the number and average size of farms in USA, EU, Brazil, CIS, China and India
  • Revenues in $bn of leading tractor suppliers including Yanmar, Deutz Fahr, Mihandra, AGCO, John Deere, Kubota Tractor Corp., CNN Industrial and so on
  • 5- or 10-year annual sales for Kubota, John Deere, AGCO, Mihandra, CNH Industrial, Deutz Fahr and so on
  • RTK GPS-enabled auto-steer technology
  • Number of GNSS receivers in used agriculture between 2006 and 2023 segmented by tractor guidance, automatic steering, VRT and asset management
  • Market value (in $m) for GNSS receivers used in agriculture between 2006 and 2023 segmented by tractor guidance, automatic steering, VRT and asset management
  • Unit price ($/unit) of GNSS receivers used in agriculture between 2006 and 2023 segmented by tractor guidance, automatic steering, VRT and asset management
  • Master-slave autonomous tractors by Yanmar, Fendt, Case IT, John Deere and Kinze Autonomy
  • Fully autonomous tractors by Yanmar, Kubota Corp., and Autonomous Tractor Corp.
  • Technology roadmap showings technology progression from manned tractor to tractor guidance to manned autosteer to master-slave and to fully autonomous tractors
  • Ten-year market forcasts for tractor guidance, autosteer and fully autonomous tractors/combines
  • Agbot II by QUT
  • Kongskilde Vibro Crop Robotti by by Kongskilde Industries A/S and Conpleks Innovation.
  • Astrix autonomous agricultural robot by Adigo
  • Horibit autonomous agricultural robot by Aarhus University
  • Ladybird autonomous agricultural robot by Australian Centre of Field Studies
  • Autonomous tractors by the The Robot Fleers for Highly Effective Agriculture and Forestry Management project
  • ATRV-2
  • Autonomous agricultural robot KU Leuven and FMTC
  • Autonomous agricultural robot by Rowbot for cornfields
  • Ten-year market forecasts for autonomous robotic data scouts
  • Technology evolution from manual hoeing to large-scale broadcast spraying to unmanned drone spraying to manned weeding with high precision and finally to autonomous weeding with ultra-high precision
  • Crop protection revenues for top ten global agrochemical suppliers including Monsanto, Sumitomo Chemical, Agricultural Solutions Ltd, DuPont, Bayer, Syngenta, BASF, DOW, Nufran
  • Crop protection revenues for top 20 Chinease suppliers including Zheijang Wynca Chemical Industrial Group, Zhejiam Jinfanda BioChemical, Nutrichem, Sichuan Leshan Fuhua Tonga Agrochemical and so on
  • 2014 and 2015 herbicide sales for Monsanto, Sumitomo Chemical, Agricultural Solutions Ltd, DuPont, Bayer, Syngenta, BASF, DOW, Nufran
  • Revenue map of Top ten Chinese producers of glyphosate
  • Historical data on global herbicide consumption in tonnes between 2004 and 2014 segmented by country
  • Glyphosate global consumption in agricultural and non-agricultural activities between 1994 and 2014 in Kg
  • Market size for glyphosate in $bn between 2004 and 20014
  • Historical growth in adoption of GE-HE seeds for major field crops such as soybeans, cotton, and corn
  • Increase in the number of herbicide-resistant weed species between 1950 and today
  • Total area in acres covered with herbicide-resistant weeds in the US between 1998 and 2014
  • Geographical spread of herbicide-resistant weeds in the US by state
  • Autonomous robotic weeder
  • Development of organic land in million ha
  • Distribution of organic land between different uses
  • Robotic weeding implements for organic farming
  • Ten-year market forecast for robotic weeding by technology type
  • Autonomous asparagus harvesting robots
  • Autonomous lettuce thinning robots
  • Ten-year market forecasts for robotic lettuce thinning and vegetable harvesting by technology and territory
  • Non-fresh fruit harvesting machines
  • Machines aiding manual fresh fruit harvesting
  • Robotic bin follower
  • Robotic orchard data scouts
  • Emerging robotic fresh fruit harvest assist technologies
  • Robotic fresh apple harvesting
  • Robotic fresh citrus harvesting
  • Fresh fruit harvesting robots
  • Addressable market size for robotic fresh apple-picking service provision
  • Ten-year market forecasts for robotic fresh citrus/apple harvesting by territory
  • Robotic fresh strawberry harvesting
  • Addressable market size for robotic fresh strawberry-picking service provision
  • Ten-year market forecasts for robotic fresh strawberry harvesting by territory
  • Autonomous robotic vineyard scouts and pruners
  • Autonomous robotics for greenhouses and nurseries
  • Schematic showing the concept of VRT for seed planting
  • Robotic seed planting
  • Map of average dairy farm sizes worldwide
  • Average size and number of dairy farms in the US between 1970 and 2007
  • Global number and distribution of dairy cows by country
  • Addressable market for robotic milking machines by country
  • Addressable market for robotic feed pushers by country
  • Lely's robotic milking machine
  • Robotic milking machines
  • Autonomous robotic feed pushers
  • Robotic manure cleaning
  • Alternatives to autonomous robotic feed pushers
  • Autonomous robotic shepherds
  • Ten-year market forecasts for robotic milking systems by country
  • Ten-year market forecasts for automatic feed pusher and other mobile robotics in dairy farming
  • Table comparing the resolution, image acquisition cost, image processing cost and minimum order size for satellite imaging
  • Annual sales of unmanned spraying helicopters in Japan
  • Area of rice paddies in Japan sprayed by unmanned helicopters between in Ha
  • Unmanned drones and helicopters for field spraying
  • Unmanned agriculture drones on the market
  • Table comparing different agricultural drones on the market on the basis of price, type, autonomy, cruise speed, flight time and so on
  • Agricultural drones: the emerging value chain
  • Core company information on key agricultural drone companies
  • Ten-year market forecasts for agricultural drones
  • Suction-based end effectors by Vision Robotics
  • Suction-based end effectors by Abundant Robotics
  • Other novel end-effectors in development
  • Soft robotic grippers by Soft Robotics, Festo, Empire Robotic, Pneubotics
  • Dexterous robotic by Shadow Robotics, Schunk, Allegro, Willow Garage and so on
  • Value chain of RTK GPS Technology from signal service provides to receiver manufacturers to device vendors to tractor companies
  • Performance levels of DGPS, OmniStar XP/HP and RTK technologies
  • Basic operational mechanism of LIDAR
  • LIDAR examples
  • Table comparing the performance of different LIDARs on the market or in development
  • Table assessing suitability of different LIDAR for agricultural robotic applications
  • Hyperspectral imaging and precision agriculture
  • Hyperspectral imaging sensors on the market
  • Common multi-spectral sensors used with agricultural drones
  • Ten-year market forecasts for all agricultural robots and drones segmented by type and/or function
  • Ten-year market forecasts for agricultural robots and drones segmented by type and/or function
  • Ten-year market forecasts for autonomous and mobile agricultural robots and drones segmented by type and/or function
  • Ten-year market forecasts for tractor guidance, autosteer and fully autonomous tractors/combines
  • Ten-year market forecasts for autonomous robotic data scouts
  • Ten-year market forecast for robotic weeding by technology type
  • Ten-year market forecasts for robotic lettuce thinning and vegetable harvesting by technology and territory
  • Ten-year market forecasts for robotic fresh citrus/apple harvesting by territory
  • Ten-year market forecasts for robotic fresh strawberry harvesting by territory
  • Ten-year market forecasts for robotic milking systems by country
  • Ten-year market forecasts for automatic feed pusher and other mobile robotics in dairy farming
  • Ten-year market forecasts for agricultural drones
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