It is often imagined that agriculture is alien to new technologies. This is far from reality. In fact, agriculture has always been a pioneer in the adoption of new technologies, embracing motor/power, modified seed, and agrochemical technologies over many decades to boost productivity. It is however true that agriculture is still largely non-automated and non-digitized. This has been mainly because the technological deficiencies have so far held back automation. This is, however, changing, largely (but not exclusively) thanks to leaps in two core technologies: (a) CNN-based machine vision and (b) autonomous mobility.
These technologies are enabling completely new product categories, new methods of farming, and even new business models. Despite the vanishingly small penetration of such technologies today, IDTechEx Research assesses that agricultural robots and AI-based machine vision represent the natural evolution of agricultural machines and tools.
Naturally, many companies – large and small- are actively pursuing the new commercial opportunities that technologies enable. This explores the latest technical and development trends, particularly focusing on assessing the readiness level of various companies and product categories/types. The content of this article is drawn from the latest research from IDTechEx, “Agricultural Robots, Drones, and AI: 2020-2040: Technologies, Markets, and Players”, which offers a deep and comprehensive analysis of all technologies, companies and markets in agricultural robots, drones, and AI.
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This report analyzes 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 the key companies and innovators. Finally, the report offers short- and long-term market forecasts, considering the addressable market size in area/tons and value, penetration rates, annual robot sales, accumulated fleet size, total RaaS (robot as a service) revenue projections and so on. The forecasts cover 15 robot types and farming sectors.
The article image shows the technical readiness level of various companies and products. Note that the color coding signals the type of robotic system. Clearly, many companies and product categories are in the system-subsystem development phase, whilst some have already reach technology readiness levels of 9-10. This distribution of companies and products across the technology readiness spectrum signals that the industry has advanced, further nearing market readiness.
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Autonomous Ultraprecision Robots
First let us consider those in blue, i.e., small/mid-sized ultraprecision robots. These are often small or mid-sized robots designed to autonomously navigate and to automatically take some ultraprecise plant-specific action. The CNN-based machine vision technology enables the robots to identify and localize individual plants. The first application is in precision weeding, promising to save chemicals, boost yield, and reduce soil compaction. In the future, this technology will allow the robots to analyze the health status and the growth conditions of each plant. The robots can take a site-specific precision action tailored to the needs of the individual plants. At first, these robots are mainly targeting row vegetables, but in the future their tasks will become more diverse, encompassing all farm management activities. They are often (but not always) autonomous and, in many cases, electrically driven. The former enables the realization of swarms or large fleets of small robots. These robots are in some instances sold as equipment and in many cases are offered as a service (RaaS)
Some companies have already gone through multiple product iterations (see image panel below). In fact, some firms also have a widened product portfolio, positioning their older and smaller robots as, for example, data scouts. In general, the key product development trends are the following: (1) improving the precision, recall, and speed of the algorithm on a specific plant/weed; (2) streamlining the process of data collection, annotation, and the training of the algorithms to extend the applicability of the robot to multiple plants; (3) making the robots larger so that they can have more onboard energy to support a longer operation time and a heavy load and so that they can cover more ha/day; and (4) ruggedizing the systems so that they can operate within an agricultural environment with minimal or no expert intervention.
In terms of commercial development, the emphasis has been on accumulating real-world experience. This is critical because the feedback enables the hardware design to evolve and to be perfected. Furthermore, operational time translates into more data which improves the algorithms. To accelerate deployment, many are offering their robots as a service even if it means that they remain a loss-making operation today.
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