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Front. Robot. AI, 04 January 2023
Sec. Field Robotics
Volume 9 - 2022 |

Editorial: Robotics for smart farms

  • 1Centre for Automation and Robotics (UPM-CSIC), Madrid, Spain
  • 2Department of Signal Theory and Communications, Telematics and Computing, Rey Juan Carlos University, Madrid, Spain

Editorial on the Research Topic
Robotics for smart farms

Farming is facing many economic challenges in terms of productivity, cost-effectiveness and labor shortage (Meuwissen et al., 2019). Farming is approaching into the “smart” era, where operations become digitalized and data-driven, making possible to monitor and control many diversified and interconnected procedures such as crop growth assessment, pest control, disease monitoring, irrigation, planting, crop spraying, field analysis, among others (Klerkx et al., 2019). This approach will be carried out through an increasing number of automated devices and tools interacting with the need to track the production processes for operations optimization or traceability toward consumers. The introduction of automation, including intelligent robots, automated sensing, modeling, decision-making, artificial intelligence (AI), smart analyses, planning and actuation, and the role of genotypes versus the environment, allows the agricultural operations to be enhanced efficiency (Gonzalez-de-Santos et al., 2020). Additionally, improved actuation, sensing and monitoring of the production reduce the environmental impact, increase quality, quantity, and sustainability. Robotics for smart farms gives a unique possibility to assess and evaluate agricultural sustainability with an unprecedented level of accuracy and precision.

This Research Topic provides a useful overview of the capabilities and the applications of robotic systems (both aerial and ground robots) and human-robot collaboration in combination with artificial intelligence (AI) strategies for the development of different tasks in agricultural environments.

The applications in the current Research Topic focus on the use and implementation of robotic systems in various agricultural tasks. For example, Ulloa et al. propose an optical sensory system for data acquisition and processing in precision agriculture for Robotic Fertilization process. They integrate different sensory systems such as a LIDAR, an RGB and a multispectral camera, as well as a robotic arm equipped with a fertilization system in a robotic platform to reconstruct rows crops and extract information for fertilization with the robotic arm.

Moreover, in recent years site-specific sprayer solutions (including pesticides and fertilizers) applied by the use of unmanned aerial vehicles (UAVs) have gained interest in the agricultural sector. Ma et al. present a data-driven framework to predict density distribution patterns of vermiculite dispensed from a hovering unmanned aerial vehicle (UAV) as a function of UAV’s movement state, wind condition, and dispenser setting. The authors propose a model based on the UAV altitude, wind speed, wind direction, and the dispenser settings (aperture size and flow rate) to estimate the two-dimensional distribution of low-density materials as the vermiculite, which is often used as carrier in UAV-based distributions of biological control agents.

An important element in the introduction of robotic systems in smart farming is the cooperation between operators and robots. Harman and Sklar propose a multi-agent task allocation method that assists farm managers and team leaders to manage the harvesting workforce effectively and efficiently. The authors propose a variation of Round Robin (RR) for the problem of assigning workers to fields, and market-based task allocation mechanisms are applied to the challenge of assigning tasks to workers within the fields. The authors define a worker model, learned from observing each worker’s performance during the harvesting season, that can be a human or a robot, hence anticipating the methodology adoption for human-robot workforces.

One of the great challenges that robotic and AI systems currently face is the lack of a regulatory framework and guidelines that can guarantee safety and operability in agricultural production systems. For this Eastwood et al. propose a “responsibility by design” approach to innovation in robotics and automation, using the development of batch robotic milking in pasture-grazed dairy farming as a case study. The authors highlight wider considerations that technology developers and policymakers need to consider when envisaging future innovation trajectories for robotics in smart farming, including the impact on work design, worker wellbeing and safety, changes to farming systems, and the influences of market and regulatory constraints.

This Research Topic shows a small but important glimpse of what robotics and artificial intelligence can contribute to agricultural production systems, as a means of managing agricultural land, animals, and people more effectively, efficiently, and automated. The approaches presented in this Research Topic involve the use of sensors, machines, drones, and satellites to monitor animals, soil, water, and plants. The data obtained through monitoring are used to interpret the past and predict the future, with the aim of making more timely or accurate decisions, both on-farm and in the supply chain.

Author contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Gonzalez-de-Santos, P., Fernández, R., Sepúlveda, D., Navas, E., Emmi, L., and Armada, M. (2020). Field robots for intelligent farms—inhering features from industry. Agronomy 10, 1638. doi:10.3390/agronomy10111638

CrossRef Full Text | Google Scholar

Klerkx, L., Jakku, E., and Labarthe, P. (2019). A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS - Wagening. J. Life Sci. 90–91, 1–16. doi:10.1016/j.njas.2019.100315

PubMed Abstract | CrossRef Full Text | Google Scholar

Meuwissen, M. P. M., Feindt, P. H., Spiegel, A., Termeer, C. J. A. M., Mathijs, E., Mey, Y., et al. (2019). A framework to assess the resilience of farming systems. Agric. Syst. 176, 102656. doi:10.1016/j.agsy.2019.102656

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Keywords: smart farms, robotics, precision agriculture (PA), farm automation, AI in agriculture

Citation: Emmi L, Fernandez R and Guerrero JM (2023) Editorial: Robotics for smart farms. Front. Robot. AI 9:1113440. doi: 10.3389/frobt.2022.1113440

Received: 01 December 2022; Accepted: 15 December 2022;
Published: 04 January 2023.

Edited and reviewed by:

Dongbing Gu, University of Essex, United Kingdom

Copyright © 2023 Emmi, Fernandez and Guerrero. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Luis Emmi,