Agricultural Robots and AI: A Question of When and Not If

Robotics and artificial intelligence (AI) will drive a deep and transformative change in the agricultural world during the coming decades. Seeing, localising, and taking plant-specific intelligent action are no longer the exclusive realm of humans.

Machines have demonstrated the technical viability and the emphasis has long shifted to the finer details of ROI, reliability, business model, etc. As such, a new class of activities in agriculture are prone to automation, just as advances in power and motion technologies mechanised many agricultural tasks, or just as advances in seed and agrochemical technology removed the human from many activities.

A new report by research company IDTechEX shows that the upcoming changes are already a question of when and not if. The transformation will not be overnight, but nonetheless, robotics and AI are inevitability in the evolution of agricultural tools and practises. The scale of the potential is demonstrated in the chart below, which shows the forecasted long-term growth in annual unit sales (vs accumulated fleet size) of various autonomous and/or robotic solutions.

The report analyses all the emerging product types, including autonomous robots taking plant-specific precision action, intelligent vision-enabled robotic implements, diverse robotic fresh fruit harvesters, highly automated and autonomous tractors and high-power farm vehicles, drones, automatic milking, and so on. It provides interview-based company profiles and analysis of all of the key companies and innovators.

Machine vision technology is often a core competency of these robots, enabling the robots to see, identify, localise, and to take some intelligent site-specific action on individual plants. The machine vision increasingly uses deep learning algorithms often trained on expert-annotated image datasets, allowing the technology to far exceed the performance of conventional algorithms and to match or even exceed even that of expert agronomists. Crucially, this approach enables a long-term technology roadmap, which can be extended to recognise all types of crops and to analyse their associated conditions, such as water-stress and disease.

Many versions of this emerging robotic class are autonomous. The autonomy challenge is much simpler than a car. The environment is well controlled and predictable, and the speed of travel is low. The legislation is today a hindrance, but will become more accommodative relatively soon.

The first major target market is in weeding. The ROI benefits here are driven by labour savings, chemical savings, boosted yields, and less land compaction. Precision action (spraying, mechanical, or electrical) reduces consumption of agrochemicals by 90% compared to untargeted application. It also improves yield (e.g., by 5-10%) because collateral damage of the crops by untargeted chemical application can be minimised. This technology can further enable farmers to tackle herbicide-resistant weeds, which are a growing problem, especially in some hotspots. Finally, the robots leave behind no unusable compacted soil.

And robots are evolving. Many robots have already grown in size and capability, offering faster speeds, higher frame-per-seconds, more rugged designs, higher on-board energy for longer operation time and a heavier load, and so on. This evolution will inevitably continue, just as it did with all other agricultural tools and vehicles. We are still at the beginning. The deployed fleet sizes worldwide are small, but this is about to change.

In fruit picking, machine vision technology can identify and localise different visible fruits against complex and varying backgrounds with a high success rate.

The rise of deep learning-based image recognition technologies has caused a leap in performance, sending everybody back to the drawing board, including the older start-ups and some who had given up. This technology improves algorithm precision, lowering the false positives which waste time. Crucially, a clear pathway exists for algorithm development for new fruit- environment combinations, enabling the applicability of machine detection and localisation to be extended to many fruits.

The robotic path planning, picking strategy and the motion control of the robotic arm are also challenges. Here, too, there are algorithmic improvements. More importantly, companies are developing novel end-effectors which can accelerate gentle fresh fruit picking whilst lightening the computational load. Robotic fresh fruit picking has therefore become possible. Indeed, the first generation of companies have been demonstrating its technical viability over the past five years.

The key to commercial success lies in the development of robust robotic and associated AI platforms, which can be utilised across the harvesting season of different crops. This approach is already reflected in the latest late-state prototypes or products on the market.

To learn more please see the IDTechEx report, "Agricultural Robots, Drones, and AI: 2020-2040: Technologies, Markets, and Players."