Agricultural robots and drones are already a $3bn market, rising to $12bn in 2026. This market is however a mixed bag of products, each serving a different sector, each utilising a different robotic technology, and each exhibiting a different degree of commercial maturity.
For example, static milking robots are already a $1.9bn commercial reality employing commonplace robotic arm technologies; autonomous de-weeding robots are in early stages of commercial trials employing autonomous navigation technology together with advanced vision and deep learning techniques; and robotic fresh fruit harvesting is in early stages of prototyping seeking to employ novel low-cost end-effectors and arrays of robotic arms.
This mixed picture has given rise to different business models. In general, less mature technologies are being commercialized as a service (RaaS model) whereas more mature ones are sold as traditional equipment. Here we explain why.
To learn more about agricultural robots and drones and the future of farming read the IDTechEx
report Agricultural Robots and Drones 2016-2026: Technologies, Markets, Players
. Here, a detailed technology roadmap outlining how robotic technology will enter into different aspects of agriculture, detailed market territory-segmented markets forecasts showing the market future of different agricultural robotic and drone technologies, and detailed interview-based profiles of leading players.
The RaaS model, increasingly trendy in the world of robotics, is becoming commonplace in agricultural robotics too. Here, companies price their service often in the amount of fruit picked ($/Kg) or the area of land de-weeded or thinned ($/acre). This pricing model fits into the operating model of many farmers who pay manual labours on a $/acre or $/Kg basis.
This approach de-risks the adoption of technology for the end-users (farm operators) and lowers the barrier to entry by not requiring the farmer to commit an upfront capital investment. This rings particularly true in the case expensive robotics equipment incorporating multiple sensors and complex algorithms.
In addition, this approach enables the suppliers to take the product into the market without having perfected its reliability. This is because the robot will be offered as a service run by skilled operators who will be at hand to deal with technical issues to minimise downtime.
This is critical because it enables suppliers to get early field trial results and customer feedback. It is also critical because it gives operators access to valuable data which can be fed into deep learning algorithms or be used to fine tune farm data analytics software.
The RaaS model is however inherently limiting. Unlike Software as a Service (SaaS) which can be made available to millions of users around the world simultaneously, the RaaS model is geographically limited by the physical location of the robots. It therefore works only when the farms are closely located or when many robots and skilled operators are available around the world ready for service.
This limitation explains why the business models tend to shift back to traditional equipment sale as the technologies/products matures. In this model, companies will come to rely on the well-established network of dealerships across the world to geographically scale their sales. Incidentally, this puts traditional agricultural vehicle makers in a strong position.
Evidence bears this out. In mature segments of agricultural robotics such as milking robots, autosteer technology, and master-slave tractors, traditional equipment sales prevail. In less mature segments such as robotic de-weeding/thinning or fresh fruit harvesting, the RaaS model still prevails.
As companies and technologies mature to become cheaper, more reliable and easier to use, this transition from RasS to equipment sales will increasingly take place. A major challenge for companies transitioning their business models will be in retaining access to farm data, which will only grow in value in the future.
This means that suppliers must start putting strategies in place that enables them to build in data acquisition loops into their future more-traditional equipment sale business models. This will be critical to improving their products and algorithms, but also to remaining relevant in the emerging value chain of agriculture robots in which robotics hardware becomes increasingly commoditized and the value shifts to data analytics.
To learn more, read the IDTechEx
Research report "Agricultural Robots and Drones 2016-2026: Technologies, Markets, Players" at www.IDTechEx.com/agri
. Here we will learn how robotics technology enters into different aspects of agriculture, how it changes the way farming is done, how it becomes the future of agrochemicals business and modifies the way we design agricultural machinery.