Abstract
The world society needs to produce more food with the highest quality standards to feed the world population with the same level of nutrition. Microfarms and local food production enable growing vegetables near the population and reducing the operational logistics costs related to post-harvest food handling. However, it isn't economical viable neither efficient to have one person devoted to these microfarms task. To overcome this issue, we propose an open-source robotic solution capable of performing multitasks in small polyculture farms. This robot is equipped with optical sensors, manipulators and other mechatronic technology to monitor and process both biotic and abiotic agronomic data. This information supports the consequent activation of manipulators that perform several agricultural tasks: crop and weed detection, sowing and watering. The development of the robot meets low-cost requirements so that it can be a putative commercial solution. This solution is designed to be relevant as a test platform to support the assembly of new sensors and further develop new cognitive solutions, to raise awareness on topics related to Precision Agriculture. We are looking for a rational use of resources and several other aspects of an evolved, economically efficient and ecologically sustainable agriculture.

Abstract
This study analyzed the potential of proximal optical sensing as an effective approach for early disease detection. A compact, modular sensing system, combining direct UV–Vis spectroscopy with optical fibers, supported by a principal component analysis (PCA), was applied to evaluate the modifications promoted by the bacteria Xanthomonas euvesicatoria in tomato leaves (cv. cherry). Plant infection was achieved by spraying a bacterial suspension (108 CFU mL-1) until run-off occurred, and a similar approach was followed for the control group, where only water was applied. A total of 270 spectral measurements were performed on leaves, on five different time instances, including pre- and post-inoculation measurements. PCA was then applied to the acquired data from both healthy and inoculated leaves, which allowed their distinction and differentiation, three days after inoculation, when unhealthy plants were still asymptomatic.

Abstract
Thrombocytosis is a disorder with an excessive number of platelets in the blood, where total platelet counts (TPC) are crucial for diagnosis. This condition predisposes to blood vessels clotting and diseases such as stroke or heart attack. TPC is generally performed at the laboratory by flow cytometry with laser scattering or impedance detection. Due to the limited capacity of automated hematology in performing TPC quantification, a manual microscopy count is a very common quality assurance measure undertaken by clinical pathologists. Monitoring coagulation risk is key in many health conditions, and point-of-care platforms would simplify this procedure by taking platelet counts to the bedside. Spectroscopy has high potential for reagent-less point-of-care miniaturized technologies. However, platelets are difficult to detect in blood by standard spectroscopy analysis, due to their small size, low number when compared to red blood cells, and low spectral contrast to hemoglobin. In this exploratory research, we show that it is possible to perform TPC by advanced spectroscopy analysis, using a new processing methodology based on self-learning artificial intelligence. The results show that TPC can be measured by visible–near-infrared spectroscopy above the standard error limit of 61.19 × 109 cells/L (R2 = 0.7016), tested within the data range of 53 × 109 to 860 × 109 cells/L of dog blood. These results open the possibility for using spectroscopy as a diagnostic technology for the detection of high levels of platelets directly in whole blood, towards the rapid diagnosis of thrombocytosis and stroke prevention.

Abstract
Total white blood cells (WBC) count is an important indication for infection diagnosis, in both human and veterinary medicine. State-of-the-art WBC counts are performed by flow cytometry combined with light scattering or impedance measurements, in the clinical analysis laboratory. These technologies are complex and difficult to be miniaturized into a portable point-of-care (POC) system. Spectroscopy is one of the most powerful technologies for POC miniaturization due to its capacity to analyze low sample quantities, little to no sample preparation, and ‘real-time’ results. WBC is in the proportion of 1:1000 to red blood cells (RBC), and the latter dominate visible-near infrared (Vis-NIR) information due to their large quantities and hemoglobin absorbance. WBC are difficult to be detected by traditional spectral analysis because their information is contained within the interference of hemoglobin bands. Herein, we perform a feasibility study for the direct detection of WBC counts in canine blood by Vis-NIR spectroscopy for veterinary applications, benchmarking current chemometrics techniques with self-learning artificial intelligence—a new advanced method for high-accuracy quantification from spectral information. Results show that total WBC counts can be detected by Vis-NIR spectroscopy to an average detection limit of 7.8 ×109 cells/L, with an R2 of 0.9880 between impedance flow cytometry analysis and spectral quantification. This result opens new possibilities for reagent-less POC technology in infection diagnosis. As WBC counts in dogs range from 5 to 45 ×109 cells/L, the detection limit obtained in this research allows concluding that the combined use of spectroscopy with this SL-AI new algorithm is a step towards the existence of portable and miniaturized Spectral POC hemogram analysis.

Abstract
Waste is one of the biggest problems on Earth today. In the spring of 2020, a team of students enrolled in the European Project Semester at Instituto Superior de Engenharia decided to contribute with the design of an ethically and sustainability-oriented autonomous cleaning robot named MopBot. The project started with the research on similar solutions, ethics, marketing and sustainability to define a concept and create a functional, ethical and sustainability driven design, including the complete control system. Finally, given the undergoing pandemic, the operation of the MopBot was simulated using CoppeliaSim. MopBot is a medium-sized vacuum cleaner, with two vertical brushes, intended to clean autonomously large areas inside buildings such as shopping malls or corridors. It is shipped with a sustainable packaging solution which can be re-purposed as a disposal box for electrical components.

Abstract
Featured Application This work describes the design, modeling, and simulation of a free-rotating wing sail solution for an autonomous environmental land yacht probe. The adopted method involves the application of land sailing principles for the design, the usage of Fusion 360 tool for 3D modeling, and the integration of Gazebo with the Robotic Operating System (ROS) framework for the simulation of the land yacht. Autonomous land yachts can play a major role in the context of environmental monitoring, namely, in open, flat, windy regions, such as iced planes or sandy shorelines. This work addresses the design, modeling, and simulation of a land yacht probe equipped with a rigid free-rotating wing sail and tail flap. The wing was designed with a symmetrical airfoil and dimensions to provide the necessary thrust to displace the vehicle. Specifically, it proposes a novel design and simulation method for free rotating wing sail autonomous land yachts. The simulation relies on the Gazebo simulator together with the Robotic Operating System (ROS) middleware. It uses a modified Gazebo aerodynamics plugin to generate the lift and drag forces and the yawing moment, two newly created plugins, one to act as a wind sensor and the other to set the wing flap angular position, and the 3D model of the land yacht created with Fusion 360. The wing sail aligns automatically to the wind direction and can be set to any given angle of attack, stabilizing after a few seconds. Finally, the obtained polar diagram characterizes the expected sailing performance of the land yacht. The described method can be adopted to evaluate different wing sail configurations, as well as control techniques, for autonomous land yachts.