The Autonomous Agriculture Equipment Market Size is valued at 11.15 billion in 2022 and is predicted to reach 60.30 billion by the year 2031 at a 20.81% CAGR during the forecast period for 2023-2031.
Autonomous agriculture equipment market is in a growing phase owing to different advanced technologies in the market. An increase in the adoption of automated machinery and equipment, sensors, and navigation systems, robots, autonomous vehicles, and other is expected to drive market growth. An autonomous tractor is the most important hardware component used in modern agriculture operations. The growth in the global autonomous agriculture equipment market is expected to be driven by an upsurge in the adoption of automated farming technologies due to labor depletion and high labor costs, a rise in optimization of crop yield and waste reduction, and a rise in dependence on advanced technologies to enhance productivity across the globe.
Due to the COVID-19 pandemic, research & development has decreased due to the lack of funding, halted manufacturing activities, and disruption of the raw material supply chain. In addition, the positive impact of COVID-19 has been seen in autonomous farming solution adoption, such as autonomous tractors, robots, drones, harvesters, planters, and sensors by farmers owing to the lack of workers, which is resulted in an increase in demand for autonomous agriculture equipment worldwide.
The autonomous agriculture equipment market is segmented on the basis of type, product and application. Type segment includes autonomous, semi-autonomous. The market is segmented as tractors, unmanned aerial vehicles (UAVs), robots, and other products based on product. By Applications, the market is segmented into planting, harvesting, spraying and other applications.
The robot segment is estimated to capture the highest market share during the forecast period. Agriculture robot is increasingly used in various farming operation such as weed control, planting, harvesting, and soil analysis. In addition, with the increasing adoption of intelligent or autonomous robots integrated with sensors and navigation systems, computer vision is expected to drive the growth of the autonomous agriculture equipment market worldwide. Moreover, an increase in penetration of integrated hardware and software technologies is expected to increase the production and efficiency of farms, which in turn is projected to push market growth in the future years.
The segment is inclusive of tractors, levelers, and agriculture implements. The demand for agriculture tractors is gaining traction owing to numerous factors such as the increasing demand for food propelling, the need to mechanize farm work, the availability of technologically advanced tractors, and increased urbanization causing a shortage in labor, forcing farmers to adopt machinery. This is anticipated to contribute to the maturation of the overall market.
North America generated the highest revenue in 2022. The Region is witnessing an upsurge in adopting autonomous agriculture equipment such as autonomous vehicles, drones, robots, irrigation systems, and other automation and control systems. In addition, the raised adoption of digital technologies for modern farm machinery in efficient crop production in the Region, along with the advancement in the popularity of indoor farming owing to urbanization. In addition, the North American Region is also anticipated to grow at a faster rate.
| Report Attribute | Specifications |
| Market size value in 2022 | USD 11.15 Bn |
| Revenue forecast in 2031 | USD 60.30 Bn |
| Growth rate CAGR | CAGR of 20.81% from 2023 to 2031 |
| Quantitative units | Representation of revenue in US$ Billion, Volume (Unit) and CAGR from 2023 to 2031 |
| Historic Year | 2019 to 2022 |
| Forecast Year | 2023-2031 |
| Report coverage | The forecast of revenue, the position of the company, the competitive market statistics, growth prospects, and trends |
| Segments covered | Type, Product And Application |
| Regional scope | North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
| Country scope | U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; The UK; France; Italy; Spain; China; Japan; India; South Korea; Southeast Asia; South Korea; Southeast Asia |
| Competitive Landscape | AGCO Corporation, Naio Technologies, CLAAS HGaA mbH, DJI, CNH Industrial N.V., Parrot Drone SAS, AgEagle Aerial Systems Inc, Deere & Company, YANMAR HOLDING Co., Ltd., YTO GROUP CORPORATION, Autonomous Tractor Corporation, Mahindra & Mahindra Ltd., Corporation, and Hexagon Agriculture. |
| Customization scope | Free customization report with the procurement of the report, Modifications to the regional and segment scope. Particular Geographic competitive landscape. |
| Pricing and available payment methods | Explore pricing alternatives that are customized to your particular study requirements. |
Autonomous Agriculture Equipment Market By Type-
Autonomous Agriculture Equipment Market By Application-
Autonomous Agriculture Equipment Market By Products-
Autonomous Agriculture Equipment Market By Region-
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
This study employed a multi-step, mixed-method research approach that integrates:
This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.
Secondary research for this study involved the collection, review, and analysis of publicly available and paid data sources to build the initial fact base, understand historical market behaviour, identify data gaps, and refine the hypotheses for primary research.
Secondary data for the market study was gathered from multiple credible sources, including:
These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.
Primary research was conducted to validate secondary data, understand real-time market dynamics, capture price points and adoption trends, and verify the assumptions used in the market modelling.
Primary interviews for this study involved:
Interviews were conducted via:
Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.
All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.
The data validation process included:
This ensured that the dataset used for modelling was clean, robust, and reliable.
The bottom-up approach involved aggregating segment-level data, such as:
This method was primarily used when detailed micro-level market data were available.
The top-down approach used macro-level indicators:
This approach was used for segments where granular data were limited or inconsistent.
To ensure accuracy, a triangulated hybrid model was used. This included:
This multi-angle validation yielded the final market size.
Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.
Given inherent uncertainties, three scenarios were constructed:
Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.