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.
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.
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.
Key Topics Covered:
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)
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
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