The world’s growing population faces the most significant challenge of recent times: fulfilling the need for food in light of the scarcity of natural resources, environmental degradation, and labor shortages in agriculture. Generally speaking, the agricultural sector has a heavy reliance on migrant workers, and this dependence is crucial for food production, as these laborers are seasonal, and their effective utilization faces a set of challenges, such as geopolitical tensions, pandemic restrictions, political support, and demographic change, among others. Conversely, in the last few years, there has been continuous growth in the development of agricultural robotics and autonomous farming systems to improve food production. However, most agricultural operations are dynamic such as harvesting and post-harvesting, which is difficult to fully automate with a robotics solution. In addition, as shown in Fig. 1(a), a fully manual harvesting operation may include risks related to the health of human workers, such as lifting heavy loads may lead to back pain, pain in knees due to prolonged knee bending and hip osteoarthritis. On the other hand, the human–robot collaboration (HRC) paradigm may be a more beneficial and efficient operation way where the robots work together with on-field human laborers to accomplish various field tasks, as shown in Fig. 1(b), relieving them of the burden of non-repetitive and non-scalable manual activities.
In the RASBerry project, the human pickers work conveniently with robots exploiting the synergy mentioned above; humans are involved in harvesting fruits from crops, and robots take care of logistics during the harvesting operation. According to the HRC paradigm has a significant advantage as it supports increased productivity and decreased labor-intensive tasks. One such example is from a proof-of-concept demonstration conducted in Kent, United Kingdom, as presented in Fig. 1 of where robots were deployed and manually driven for scalability analysis of robotic in-field, as shown in Fig. 2. For the efficient application of HRC in agricultural scenarios, a robot should not be guided by humans and be capable of reacting (semi) autonomously based on information feed and reasoning capabilities. Mainly in the industry scenarios, there has been substantial development in HRC solutions that show enormous advantages of using robots alongside human workers, and now growing also in the agricultural sector.
In “Robot Farmers” concept, the authors develop perception and navigation systems for a family of autonomous orchard vehicles to assist people in tree fruit production. In this HRC demo, humans and robots perform different activities in three deployment examples: in mule mode, robots carry crates of apples for workers picking fruit; In pace mode, robots autonomously follow tree rows in apple blocks with different coverage patterns to mow the vegetation between the rows, inspect the canopy for disease and pests, and collect data for yield estimation; in scaffold mode, robots lift workers so they can perform agricultural tasks in the upper parts of trees. In particular, in the mule mode, the HRC approach adds to the prevention of workers’ fatigue due to the struggle of lifting heavy crates, which are now shouldered by the robots. The scaffold mode also allows the placing of pheromone dispensers with robot assistance which turned out to be twice more efficient as the purely manual process. However, the safe introduction of autonomous vehicles in orchards and other food production environments shared with humans still poses several technological challenges, such as extraction of features and information from workers’ behavioral patterns, handling environment data complexities, different ways of communication, and sensor interoperability.
Specifically in agriculture, robots must be able to work in more dynamic and unstructured scenarios, in which they have to deal with unforeseen events. To achieve an optimal, cost-effective design for such autonomous systems, it is essential to consider the specific farming operations, the number of workers involved, and the type of interaction between them. The robots’ autonomy level and decision-making ability to sense humans and their gestures depend on various sensory technologies and human–robot collaboration strategies. For example, gestures may be captured using touch, vision, sound, and inertial sensors and the processed data can be used for human activities detection and classification.
International Conference on Computer Vision
The International Research Awards on Computer Vision recognize groundbreaking contributions in the field of computer vision, honoring researchers, scientists and innovators whose work has significantly advanced the domain. This prestigious award highlights excellence in fundamental theories, novel algorithms and real-world applications, fostering progress in artificial intelligence, image processing and deep learning.
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