(Source: Emmy Ljs/stock.adobe.com; generated with AI)
Industrial robots have been around since the 1950s, but their capabilities and intelligence have steadily advanced in line with new material, technology, fabrication, and computing advancements. By leveraging advanced computer science, sensing, and control capabilities that are now at their fingertips, engineers have rapidly transformed modern agricultural robots. Part of this transformation includes refining some of the more “intelligent” aspects of agricultural robots, such as the decision-making process, object perception, and advanced control and execution techniques.
Today, there are many types of agricultural robots used in various farming processes. Regardless of the agricultural application, many of these robots utilize similar core technologies; notably, multi-sensor capabilities, advanced visual image processing systems, complex algorithms, stable and mobile platforms, and flexible locomotion controls. In this blog, we look at how these technologies enable different agricultural robots to shape the present and future of smart farming.
Let’s start by defining agricultural robots. Agricultural robots are autonomous and semi-autonomous machines designed to perform specific tasks in the agricultural and farming space. Like many smart robots, they have advanced perception and autonomous decision-making abilities and can execute tasks with high precision, accuracy, and efficiency. Whereas human laborers are hindered by fatigue and harsh environmental conditions, agricultural robots are designed to withstand the demands of various agricultural settings for extended time periods while maintaining optimal productivity levels.
Since agricultural conditions tend to be complex and variable, these robots need high adaptability, precise navigation, and the ability to avoid obstacles effectively to survive. With these core features at the forefront of development, all agricultural robots are built around four key parts:
Most agricultural robots locomote using wheels or caterpillars, but drones and other uncrewed aerial vehicles (UAVs) are also an option depending on the application. Agricultural robots perceive their environment using a combination of visual sensors, global navigation satellite system (GNSS) sensors, light detection and ranging (lidar) sensors, and infrared (IR) sensors. The combination of different sensors allows agricultural robots to autonomously navigate their environment and detect a wide range of objects and obstacles based on the output of the sensors. These robots come in an array of shapes and sizes, so the number of sensors installed depends on the size of the robot―based on how many can physically fit, as well as how many and what types are needed to navigate constrained environments. Furthermore, agricultural robots can be deployed outside (crop fields) and indoors (greenhouses), so the number and types of sensors required will also vary depending on the environment where they will be working.
One of the most important assets on agricultural robots are their attachments, appendages, and end devices, since these parts enable the robot to physically perform tasks. Without them, all the navigation, automation, and decision-making capabilities mean nothing, as they would just move around without performing a specific function. The robot’s end devices are synonymous with human arms and fingers and come in many forms—depending on the intended task(s) of the robot—and include finger-based devices, needles, spray nozzles, robotic arms, scissors, and attractors. These different end devices allow the robot to perform a range of gripping, cutting, attaching, and pressing motions that allow agricultural processes like picking, harvesting, sowing, spraying, and transplanting to be performed.
The agricultural sector traditionally relies on manual labor that is physically demanding and time-consuming. Agricultural robots save on labor and make various agricultural processes more efficient, from picking to spraying pesticides on large crop areas, leading to improved agricultural production. Given the benefits for the agricultural sector, the types of agricultural robots deployed are ever-expanding and ever-diversifying as new areas emerge where these robots can be designed with greater efficiency than the status quo.
Considering the difference between large scale agricultural crop management and the more delicate picking requirements for fruit and vegetables, agricultural robots tend to be split into field robots and fruit and vegetable robots. Each area of crops requires different design specifications—especially for the appendages’ design—to perform the distinct tasks.
Field robots are mostly autonomous mobile robots (AMRs) that perform various crop production tasks. Many robots out in the field use wheels to travel, ranging from small robots to fully autonomous tractors and harvesters. The main tasks assigned to field robots are tilling, seeding, harvesting, data collection, and crop protection.
For example, tillage robots cultivate the land, which is a labor-intensive and repetitive task. Tillage robots have already reduced labor needs and improved the quality and efficiency of cultivation.[1] By adding productivity and efficiency, tillage robots have become a well-developed area in the agricultural robot industry, as many are intelligent robots that play a key role in digital agriculture.
Additionally, seeding robots sow fields and accurately sow seeds in exact positions each time, saving farmers time and money. During this process, seeding robots perform soil digging, seed planting, and seed covering tasks, and some can also add fertilizer and water to the seed. There are several different seeding robots in existence, signifying another well-developed area of smart farming.
In another instance, crop harvesting robots are adding to the digital nature of agriculture. One type of crop harvesting robot is rice cutter machines, which have been around for many years. Still, advances in advanced algorithms―such as deep learning―have turned these harvesters from semi-automatic to fully automated robots in recent years.
Another vital field robot is data-collecting robots, which gather different types of information in the field to help farmers make “invisible” decisions about their crop field. Robots not only collect a much wider range of data than humans, but they do so more efficiently and accurately. The data collected by these robots helps farmers improve their productivity levels and decrease long-term costs, as well as detect diseases and pests on their crops.
Of course, using data for productivity will be for naught if the crops are not protected, which is where crop protection robots come into play. These are one of the few field robots that primarily use UAVs to spray pesticides over crops. UAVs deliver precise pesticide treatment via advanced control algorithms, thereby reducing potential harm to humans, the crop yield, and the environment.
Alongside crop fields, agricultural robots can be used in more delicate scenarios to pick, sort, and plant fruit and vegetables. Recent years have seen many countries struggle to meet labor demands to feed the population[2]―which can lead to lower yields and higher consumer prices. Fruit and vegetable robots can increase planting areas for fruit and vegetables without requiring more manual labor. The main types of robots in this area are transplanting robots, fruit and vegetable patrolling robots, pesticide spraying robots, and picking robots.
Transplanting robots improve accuracy, stability, and transplanting performance compared to manual approaches. These robots use advanced control methods and end device manipulators to sow different plants, with the performance being governed by the accuracy of the control. Meanwhile, fruit and vegetable patrolling robots autonomously navigate through indoor and outdoor farms, collecting information and using their range of sensing capabilities. The acquired information is transmitted to the farmers to assess the maturity of different fruits and vegetables, look at what environmental parameters might affect the growing process, and detect if there are pests to be dealt with.
Similar to crop protection robots, pesticide spraying robots exist for fruits and vegetables, helping address the same manual issues related to pesticide overuse. Various pesticide robots have been developed to perform precise spraying operations. Ultrasonic sensors, flow control systems, and servo-controlled nozzles are some of the key design aspects for fruit and vegetable pesticide robots, as they need to be more precise than field crop robots that spray over larger areas.
Fruit and vegetable picking robots are automatic machines that can pick ripe fruits and vegetables on large scales. The sensors in these robots provide extensive detection capabilities across a range of planting areas to detect ripe fruits and vegetables and pick them. Both soft and hard gripper end devices are used for picking, which helps prevent damage to the produce. These picking robots can either be bulk or selective harvesters depending on what they have been programmed to pick.
The level of autonomy in robots has significantly increased in recent years. Despite agricultural robots being around for decades, their capabilities have grown exponentially, and as advanced AI and machine learning algorithms become more robust, it’s likely that the capabilities of different agricultural robots will increase further. This wide range of robots is used in many areas of agricultural production―on both large-scale crop fields and the fruit and vegetable picking―and has been alleviating the need for manual labor. With the lack of available workers in some places today and the limits of manual labor, coupled with an increasing need to grow more food, agricultural robots are improving production efficiency while ensuring a higher degree of safety for farmers.
[1] https://www.mdpi.com/2075-1702/11/1/48 [2] https://agamerica.com/blog/the-impact-of-the-farm-labor-shortage/
Liam Critchley is a writer, journalist and communicator who specializes in chemistry and nanotechnology and how fundamental principles at the molecular level can be applied to many different application areas. Liam is perhaps best known for his informative approach and explaining complex scientific topics to both scientists and non-scientists. Liam has over 350 articles published across various scientific areas and industries that crossover with both chemistry and nanotechnology.
Liam is Senior Science Communications Officer at the Nanotechnology Industries Association (NIA) in Europe and has spent the past few years writing for companies, associations and media websites around the globe. Before becoming a writer, Liam completed master’s degrees in chemistry with nanotechnology and chemical engineering.
Aside from writing, Liam is also an advisory board member for the National Graphene Association (NGA) in the U.S., the global organization Nanotechnology World Network (NWN), and a Board of Trustees member for GlamSci–A UK-based science Charity. Liam is also a member of the British Society for Nanomedicine (BSNM) and the International Association of Advanced Materials (IAAM), as well as a peer-reviewer for multiple academic journals.