Mercado de robótica agrícola 2022-2032: IDTechEx

The global market for agricultural robotics will reach $7.88 billion by 2032

Mercado de robótica agrícola 2022-2032

Evaluación tecnológica y de mercado de la industria de la robótica agrícola, incluidos robots de desherbado, robots de siembra, tractores autónomos, portadores de implementos robóticos, cosecha robótica, drones agrícolas y robots de ordeño

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As agricultural labour becomes increasingly costly and scarce, something exacerbated by the COVID-19 crisis, attention is increasingly turning towards robotics as a key component of agricultural production.
This report from IDTechEx provides a technical and commercial analysis of the growing market for agricultural robotics, considering both the key application areas and enabling technologies underpinning the industry. The report also provides ten-year application-based and regional market forecasts for the future of the agricultural robotics industry.
Agricultural robotics are increasingly attracting interest as a potential solution to the sustainability and labour issues facing global agriculture. In recent years, agricultural labour has steadily become costlier and scarcer, particularly following the border closures and worker travel restrictions in the wake of the COVID-19 pandemic, further squeezing farmers' margins and threatening food security across the world.
Automation could help mitigate this. Over the last decade, advances in robotics technology and artificial intelligence (AI) have made the use of farming robots an increasingly viable option. Across the world, a range of start-ups and established companies are working to develop robotic solutions for a number of agricultural tasks, including weeding, seeding, and harvesting.
Agricultural Robotics 2022-2032, a report from IDTechEx, provides a comprehensive overview of agricultural robotics, focusing on the key application areas of agricultural robotics, the enabling technologies that are underpinning the growth of the industry, and the market factors that will shape the future of the field. The report also provides a ten-year market forecast for the future of the agricultural robotics industry, broken down by regional market share and by application area, predicting that the global agricultural robotics market will be worth $6.7 billion by 2032.
The IDTechEx report divides the global agricultural robotics industry into eight key application areas: weeding robots, seeding robots, autonomous tractors, autonomous implement carriers and platform robots, robotic harvesting, agricultural drones, milking robots, and other applications of agricultural robots. Key enabling technologies considered include RTK-GPS, LiDAR, artificial intelligence (AI), hyperspectral imaging, end effector technology, and precision spraying technology.
Key questions answered in this report
  • What are the sustainability and labour issues facing global agriculture?
  • What is agricultural robotics?
  • What are the key application areas of agricultural robotics?
  • What are the main technological hurdles facing the agricultural robotics industry?
  • Who are the main players in the field?
  • What are the key business model considerations in developing agricultural robots?
  • How will the global agricultural robotics market evolve over the next decade?
Agricultural robotics: a growing industry enabled by emerging technologies
The day-to-day operation of a farm involves a range of repetitive, time-consuming, and dangerous tasks that could be well suited to automation using robotics. Automation is already widespread in some of these tasks. Robotic milking, for example, is already a billion-dollar industry with a significant percentage of farms in Europe using a form of robotic milking. Agricultural drones are also beginning to find widespread application in imaging and spraying, although regulations continue to limit their usage across much of the world and autonomy of tasks remains somewhat limited. Nevertheless, the market for agricultural drones is expected to show strong growth over much of the next decade.
Other applications are still emerging. Field robots for tasks such as weeding and seeding are entering the early stages of commercialisation. Compared with milking robots, which are stationary robots that generally operate indoors, developing autonomous field robots presents several technical challenges that have historically limited progress. Agricultural environments often feature unpredictable terrain, unknown obstacles, and a range of weather conditions that can impair autonomous navigation and operation and limit reliability. Additionally, agricultural regions are often in highly rural areas, where connectivity and access to repair and maintenance services can be limited.
Despite this, progress is being made and advances in artificial intelligence (AI), computer vision, and positioning technologies have brought field robots closer than ever to commercialisation. Start-ups such as Naïo Technologies, ecoRobotix, and TerraClear have begun commercialising robots for a diverse range of agricultural tasks, while major equipment providers such as John Deere, AGCO, and Kubota have developed autonomous tractor concepts. The Fendt MARS project provided a glimpse into the future of farm robots, using a swarm of small, autonomous robots to carry out tasks usually performed by manned tractors, with the company using the results of this project to develop its Xaver line of agricultural robots. Looking further into the future, companies such as Octinion, Harvest CROO, and FFRobotics are developing robots for harvesting fresh fruit, something that currently involves costly and difficult-to-source labour but is very difficult to replace using robots, requiring a careful balance of computer vision, accurate positioning, and soft-grip technology.
The rise of agricultural robotics is leading to new value chains emerging.
The growth of the agricultural robotics industry has also led to debate around the best business models, particularly around robotics-as-a-service (RaaS) versus traditional equipment sale. In a robotics-as-a-service model, robots are hired by farms, alongside trained operators, vs. traditional machine/equipment sales. This can help de-risk the operation for farmers, avoiding the need to meet high upfront costs or develop expertise in the technology before deployment. However, it also requires a team of trained operators, which can prevent developers from operating in new geographies and limit scalability. There are also questions around the issue of data ownership and whether data belongs to farmers, data collectors, technology providers, or landowners. Regulations around this have not yet caught up with the pace of technology development and this is a key uncertainty over the future of the agricultural robotics industry.
Developments over the next few years are set to play a pivotal role in the progress of the agricultural robotics industry. Agricultural Robotics 2022-2032, a report from IDTechEx, explores all of these issues, analysing both the technological and market factors that will shape the future of the emerging industry around farming robots, providing ten-year market forecasts broken down by region and application area.
Report Metrics Details
CAGR The global market for agricultural robotics will reach $7.88 billion by 2032. This represents a CAGR is 13.09% compared with 2022.
Forecast Period 2022-2032
Forecast Units Millions of US dollars
Segments Covered Weeding robots, seeding robots, autonomous tractors, robotic implement carriers and platform robots, robotic harvesting, agricultural drones, milking robots
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Table of Contents
1.1.1.What are agricultural robots?
1.1.2.Current uses of agricultural robots
1.1.3.Potential uses of agricultural robots
1.1.4.Agriculture has historically been slow to digitise
1.1.5.This is beginning to change: companies developing digital and robotic solutions for agriculture
1.1.6.The state of agricultural robotics
1.1.7.Agricultural robotics: drivers and restraints
1.1.8.The trend towards precision agriculture
1.1.9.Applications of agricultural robotics
1.1.10.Application areas by technology readiness
1.1.11.Technology progression towards autonomous, ultra precision de-weeding
1.1.12.Variable rate technology for precision seed planting
1.1.13.Technology progression towards driverless autonomous large-sized tractors
1.1.14.Small autonomous robots vs. tractors
1.1.15.Drones are becoming increasingly autonomous
1.1.16.Where do drones fit in on a farm?
1.1.17.Robotic milking: a blueprint for the wider agricultural robotics industry?
1.1.18.Which crop sectors will see agricultural robots first?
1.1.19.Agricultural robotics and precision agriculture could lead to a new value chain emerging
1.1.20.Development in agricultural robotics remains slow
1.1.21.Agricultural robotics, market forecast by robot category
1.1.22.Agricultural robotics, market forecast by region
2.1.Challenges facing 21st century agriculture: productivity and labour issues century agriculture is facing major challenges
2.1.2.Employment in agriculture is declining
2.1.3.As wealth increases, employment in agriculture decreases but agricultural productivity increases
2.1.4.Agricultural labour shortages
2.1.5.Agricultural labour costs are rising
2.1.6.Falling agricultural prices are tightening margins
2.1.7.Is agricultural automation part of the solution?
2.2.Challenges facing 21st century agriculture: agrochemicals
2.2.1.The environmental impact of fertilizers
2.2.2.Global pesticide use
2.2.3.Trends in global pesticide use
2.2.4.Regulations around pesticides are getting harsher
2.2.5.The environmental impact of pesticides
2.2.6.Agrochemicals are getting more expensive to develop
2.2.7.Roundup lawsuits: a potential blow for herbicides
2.2.8.Pesticide resistance
2.2.9.Is a precision agriculture approach part of the solution?
2.2.10.The trend towards precision agriculture
2.3.Agricultural robotics
2.3.1.What are agricultural robots?
2.3.2.Current uses of agricultural robots
2.3.3.Potential uses of agricultural robots
2.3.4.Agriculture has historically been slow to digitise
2.3.5.This is beginning to change: companies developing digital and robotic solutions for agriculture
2.3.6.Robotics: replacing or complementing human labour?
2.3.7.The state of agricultural robotics
2.3.8.The impact of COVID-19 on agriculture
2.3.9.Developing agricultural robots: more challenging than other industries?
2.3.10.Agricultural robotics: drivers and restraints
2.3.11.Levels of autonomy
2.3.12.Is full autonomy possible?
2.3.13.Autonomous sensor technologies
2.3.14.Satellite positioning
2.3.15.Electric vs non-electric agricultural robots
2.3.16.How large is the average farm?
3.1.1.Applications of agricultural robotics
3.1.2.Application areas by technology readiness
3.2.Weed and pest control
3.2.1.Most commercial field robots are used for weeding
3.2.2.From manned, broadcast spraying towards autonomous precision weeding
3.2.3.Technology progression towards autonomous, ultra precision de-weeding
3.2.4.Oz by Naïo Technologies
3.2.5.Dino by Naïo Technologies
3.2.6.Autonomous weeding robots by Vitirover
3.2.7.Anatis by Carré
3.2.8.Challenges in robotic weeding
3.2.9.A comparison of different weeding methods
3.2.10."Smart weeding" vs. traditional weeding
3.2.11.GEN-2 by Ekobot
3.2.12.Weed Whacker by Odd.Bot
3.2.13.Titan FT-35 by Roush and FarmWise
3.2.14.Robot One by Pixelfarming Robotics
3.2.15.Precision spraying
3.2.16."Green-on-green" vs. "green-on-brown"
3.2.17.John Deere's acquisition of Blue River Technology
3.2.18.Blue River Technology (John Deere): "See and Spray"
3.2.19.Avo by ecoRobotix
3.2.20.Arbus 4000 JAV by Jacto
3.2.21.AX-1 by Kilter
3.2.22.Novel methods for weed removal
3.2.23.Dick by Small Robot Company
3.2.24.Robotic pest control: beyond weeds
3.2.25.Bug Vacuum by Agrobot
3.3.Robotic seeding
3.3.1.Automating seeding
3.3.2.Variable rate technology for precision seed planting
3.3.3.FD20 by FarmDroid
3.3.4.Genesis by FarmBot
3.4.Fully autonomous tractors
3.4.1.Small robots or big tractors?
3.4.2.Technology progression towards driverless autonomous large-sized tractors
3.4.3.Tractor guidance and autosteer technology for large tractors
3.4.4.Tractor autosteer - a first step towards autonomy
3.4.5.Semi-autonomous "follow-me" tractors
3.4.6.EOX-175 by H2Trac
3.4.7.Fully autonomous driverless tractors
3.4.8.Autonomous tractor concepts developed by the major tractor companies
3.4.9.When will fully autonomous tractors be ready?
3.4.10.Monarch Tractor
3.4.11.eTrac by Farmertronics
3.4.12.AgBot by AgXeed
3.4.13.Full automation of existing tractors
3.5.Autonomous implement carriers and platform robots
3.5.1.Small autonomous robots vs. tractors
3.5.2.Land Care Robot by Directed Machines
3.5.3.RoamIO by Korechi
3.5.4.SwarmBot 5 by SwarmFarm Robotics
3.5.5.Custom or standard implements?
3.5.6.Dot by Raven Industries
3.5.7.Robotti 150D by Agrointelli
3.5.8.Over-the-row vineyard robots
3.5.9.Bakus by VitiBot
3.5.10.Ted by Naïo Technologies
3.5.11.Trektor by SITIA
3.5.12.A comparison of over-the-row vineyard robots
3.5.13.Agricultural sprayer by Hubei Sense Intelligence Technology Co.
3.5.14.A vision of the future? The Fendt MARS project
3.6.Robotic fresh fruit and vegetable harvesting
3.6.1.Row crop and non-fresh fruit harvesting is largely mechanised
3.6.2.Fresh fruit picking remains largely manual
3.6.3.Strawberries and apples: the most popular targets
3.6.4.Robotic harvesting: apples
3.6.5.FFRobot apple harvester by FFRobotics
3.6.6.Robotic harvesting: strawberries
3.6.7.A comparison of strawberry harvesting robot developers
3.6.8.Strawberry picking robots in advanced development
3.6.9.Harvester B7 by Harvest CROO Robotics
3.6.10.Rubion by Octinion
3.6.11.Robotic harvesting: asparagus
3.6.12.Robotic asparagus harvesting projects
3.6.13.Sparter by Cerescon
3.6.14.Robotic harvesting in development for other crops
3.6.15.Challenges in developing fruit picking robots
3.7.Agricultural drones
3.7.1.Drones: application pipeline
3.7.2.Agricultural drones
3.7.3.Commercially available agricultural drones
3.7.4.Agricultural drones: key considerations
3.7.5.Aerial imaging in farming
3.7.6.Drones vs. satellites vs. aeroplanes
3.7.7.Where does drone spraying have regulatory approval?
3.7.8.Commercially available spraying drones
3.7.9.Drones are becoming increasingly autonomous
3.7.10.Agricultural drones: company landscape
3.7.11.Potential software opportunities in agricultural drones
3.7.12.Where do drones fit in on a farm?
3.7.13.Fruit picking drones by Tevel Aerobotics Technologies
3.7.14.CropHopper by HayBeeSee
3.8.Milking robots and other robotic dairy farming
3.8.1.Global trends and averages for dairy farm sizes
3.8.2.Global number and distribution of dairy cows by territory
3.8.3.Robotic (automatic) milking
3.8.4.Robotic milking is becoming increasingly widespread
3.8.5.Robotic milking: a blueprint for the wider agricultural robotics industry?
3.8.6.Robotic milking: advantages and disadvantages
3.8.7.Robotic milking: key players
3.8.8.Robotic feed pushers
3.9.Other applications
3.9.1.PothaFacile by Pietro Rivi
3.9.2.Tom by Small Robot Company
3.9.3.Rock Picker by TerraClear
4.1.Positioning technologies: RTK-GPS, LiDAR, and others
4.1.1.Navigation for autonomous agricultural robots
4.1.2.Navigation in agricultural environments
4.1.3.The challenge of safe positioning
4.1.4.Position accuracy vs. position integrity
4.1.5.Achieving safe positioning
4.1.6.Fixposition AG
4.1.8.GPS as a tool for navigation
4.1.9.RTK systems: operation, performance and value chain
4.1.10.RTK systems for use in agriculture: value chain
4.1.11.Challenges of RTK-GPS
4.1.13.LiDAR, Radar, camera & ultrasonic sensors: comparison
4.1.14.Time of flight (TOF) LiDAR: Spatial Data Analysis
4.1.15.Performance comparison of different LiDARs on the market or in development
4.1.16.Assessing the suitability of different LiDAR for agricultural robotic applications
4.2.Hyperspectral imaging
4.2.1.Introduction to hyperspectral imaging
4.2.2.Multiple methods to acquire a hyperspectral data-cube
4.2.3.Line-scan hyperspectral camera design
4.2.4.Snapshot hyperspectral imaging
4.2.5.Illumination for hyperspectral imaging
4.2.6.Hyperspectral imaging as a development of multispectral imaging
4.2.7.Trade-offs between hyperspectral and multispectral imaging
4.2.8.Hyperspectral imaging and precision agriculture
4.2.9.Hyperspectral imaging from UAVs (drones)
4.2.10.Satellite imaging with hyperspectral cameras
4.2.11.Gamaya: Hyperspectral imaging for agricultural analysis
4.2.12.Supplier overview: Hyperspectral imaging
4.3.Artificial intelligence (AI)
4.3.1.What is Artificial Intelligence?
4.3.2.Key AI methods
4.3.3.Main deep learning (DL) approaches
4.3.4.DL makes automated image recognition possible
4.3.5.Image recognition AI is based on convolutional neural networks (CNNs)
4.3.6.Workings of CNNs: How are images processed?
4.3.7.Workings of CNNs: An additional example
4.3.8.Potential applications of machine learning in agriculture
4.3.9.AI for weed recognition
4.3.10.The challenge of image analysis
4.3.11.Deepening the neural network to increase accuracy
4.3.12.Deep learning: how accurate is "accurate enough"?
4.3.13.AI in agricultural robotics case study - ecoRobotix: deep learning for crop and weed recognition
4.3.14.AI in agricultural robotics case study - ecoRobotix: autonomous mobility
4.4.End effectors and gripper technology
4.4.1.End effector technology for fruit harvesting
4.4.2.Designing a harvesting end effector
4.4.3.End effectors for apple harvesting
4.4.4.End effectors for tomato harvesting
4.4.5.End effectors for cucumber harvesting
4.4.6.End effectors for pepper (capsicum) harvesting
4.5.Precision spraying technology
4.5.1.What is precision spraying?
4.5.2.Methods of spray control
4.5.3.Pulse width modulation (PWM) spraying
5.1.Market factors and business model considerations
5.1.1.Which crop sectors will see agricultural robots first?
5.1.2.Agricultural robotics and precision agriculture could lead to a new value chain emerging
5.1.3.Development in agricultural robotics remains slow
5.1.4.Revenues of major agricultural equipment suppliers
5.1.5.Robotics-as-a-service (RaaS) vs. equipment sales
5.1.6.Developing a successful business model
5.1.7.Investment strategies in agricultural robotics
5.2.Key market challenges in agricultural robotics
5.2.1.The cost of agricultural robots
5.2.2.IT infrastructure
5.2.3.Ownership and management of digital data
5.2.4.Adoption of robotics technology on farms
6.1.Agricultural robotics, market forecast by robot category
6.2.Agricultural robotics, market forecast by robot category: data tables
6.3.Agricultural robotics, market forecast by region
6.4.Agricultural robotics, market forecast by region: data tables
6.5.Milking robots, market forecast by region
6.6.Weeding robots and seeding robots, market forecast by region
6.7.Autonomous tractors and implement carrying robots, market forecast by region
6.8.Robots for fresh fruit and vegetable harvesting, market forecast by region
6.9.Agricultural drones, market forecast by region

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Report Statistics

Slides 292
Forecasts to 2032
Published Jan 2022
ISBN 9781913899707

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