**4. System Assessment and Discussion**

The mission described in the previous section produced crop images, sensor data, and traffic information with the following characteristics:


**Figure 8.** Example of a wheat image acquired with the guiding vision system and uploaded to the cloud.

**Figure 9.** Example of a maize image acquired with the guiding vision system and uploaded to the cloud.

The system has been tested in a field with two different crops. Data related to cloud communication and robot guidance algorithms have been collected. The communication performance is similar to that obtained using conventional mechanisms, so we benefit from using ROS and FIWARE without compromising performance.

**Figure 10.** Example of a ToF intensity image acquired with the guidance system and uploaded to the cloud.

**Figure 11.** Example of a ToF intensity image acquired with the guidance system and uploaded to the cloud. (**a**) Message delay and (**b**) Kafka overhead.

#### **5. Conclusions**

An architecture is presented to configure autonomous robots for agriculture with access to cloud technologies. This structure takes advantage of new concepts and technologies, such as IoT and cloud computing, allowing big data, edge computing, and digital twins to be incorporated into modern agricultural robots.

The architecture is based on ROS, the most universally accepted collection of software libraries and tools for building robotic applications, and FIWARE, an open architecture that enables the creation of new applications and services on the Internet. ROS and FI-WARE provide attractive advantages for developers and farmers. ROS and FIWARE offer powerful tools for developers to build control architectures for complex robots with cloud computing/IoT features, making development easier and leveraging open-source frameworks. ROS and FIWARE, as in the proposed integration, provide reusability, scalability, and maintenance using the appropriate hardware resources. In addition, integrating the robot controller into the Internet allows the exploitation of autonomous robot services for agriculture through the Internet.

On the other hand, the use of this type of architecture reveals to farmers the advantages of communicating autonomous robots with the cloud, providing them with leading benefits to storing data safely and efficiently, eliminating physical storage, and, thus, reducing the risk of data loss. Data stored in the cloud makes it easy to access data from anywhere and share it with other farmers or platforms. In addition, the services offered in the cloud are very flexible to contract the actual storage needed at all times, optimizing the farmer's resources. Finally, farmers can use the analysis tools available in the cloud to make their own decisions. In any case, working in the cloud requires an initial investment, which is usually recovered quickly.

The different components of the robot, particularized for a laser-based weeding robot, are described, and the general architecture is presented, indicating the specific interfaces. Based on these components, the article presents the action sequence of the robot and the operating procedure to illustrate how farmers can use the system and what benefits they can obtain.

Several experiments with two crops were conducted to evaluate the proposed integration based on the data communication characteristics, demonstrating the system's capabilities. The crop row detection system works correctly for both crops, tracking the rows with an accuracy of ±0.02 m. The evaluation concluded that the system could send image frames to the cloud at 4 frames/s; messages between subsystems and modules can be passed with a 0.63% rejection rate. Regarding the traffic of the information exchanged, an average delay of 250 ms was detected in the messages between the robot and the OCB. In contrast, the OCB and the KAFKA bus measured an average message of 1.24 ms. This indicates the robustness of internal communications within the server and hosted cloud services. This performance is in the range obtained when a system communicates with the cloud using conventional methods, so ROS and FIWARE facilitate communication with the cloud without compromising performance.

Future work will focus on extending cloud computing architecture to integrate digital twins, orchestrate big data ensembles, and facilitate the work of robots with edge computing performance.

**Author Contributions:** Conceptualization, L.E., R.F., P.G.-d.-S., M.F., M.G., G.V., H.S., M.H. and M.W.; methodology, L.E. and R.F.; software, L.E., M.F., H.S. and M.W.; validation, L.E., M.F., G.V. and H.S.; investigation, L.E., R.F., P.G.-d.-S., M.F., M.G., G.V., H.S., M.H. and M.W.; writing—original draft preparation, P.G.-d.-S.; writing—review and editing, L.E., P.G.-d.-S. and R.F.; supervision, L.E. and P.G.-d.-S.; funding acquisition, P.G.-d.-S., G.V., M.G. and M.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This article is part of a project that has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 101000256.

**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of CSIC.

**Data Availability Statement:** Herrera-Diaz, J.; Emmi, L.A.; Gonzalez de Santos, P. Maize Dataset. 2022. Available online: http://doi.org/10.20350/digitalCSIC/14566 (accessed on 1 April 2023). Herrera-Diaz, J.; Emmi, L.; Gonzalez de Santos, P. Wheat Dataset. 2022. Available online: http: //doi.org/10.20350/digitalCSIC/14567 (accessed on 1 April 2023).

**Conflicts of Interest:** The authors declare no conflict of interest.
