Abstract
Proximity sensing approaches with a wide array of sensors available for use in precision viticulture contexts can nowadays be considered both well-know and mature technologies. Still, several in-field practices performed throughout different crops rely on direct visual observation supported on gained experience to assess aspects of plants' phenological development, as well as indicators relating to the onset of common plagues and diseases. Aiming to mimic in-field direct observation, this paper presents VineInspector: a low-cost, self-contained and easy-to-install system, which is able to measure microclimatic parameters, and also to acquire images using multiple cameras. It is built upon a stake structure, rendering it suitable for deployment across a vineyard. The approach through which distinguishable attributes are detected, classified and tallied in the periodically acquired images, makes use of artificial intelligence approaches. Furthermore, it is made available through an IoT cloud-based support system. VineInspector was field-tested under real operating conditions to assess not only the robustness and the operating functionality of the hardware solution, but also the AI approaches' accuracy. Two applications were developed to evaluate Vinelnspector's consistency while a viticulturist' assistant in everyday practices. One was intended to determine the size of the very first grapevines' shoots, one of the required parameters of the well known 3-10 rule to predict primary downy mildew infection. The other was developed to tally grapevine moth males captured in sex traps. Results show that VineInspector is a logical step in smart proximity monitoring by mimicking direct visual observation from experienced viticulturists. While the latter traditionally are responsible for a set of everyday practices in the field, these are time and resource consuming. VineInspector was proven to be effective in two of these practices, performing them automatically. Therefore, it enables both the continuous monitoring and assessment of a vineyard's phenological development in a more efficient manner, making way to more assertive and timely practices against pests and diseases.

Abstract
There has been an increase in the variety of harvesting manipulators. However, sometimes the lack of efficiency of these manipulators makes it difficult to compete with harvesting tasks performed by humans. One of the key components of these manipulators is the end-effector, responsible for picking the fruits from the plant. This paper studies different types of end-effectors used by some harvesting manipulators and compares them. The objective is to analyse their advantages and limitations to better understand the requirements to design an end-effector to improve the performance of a custom Selective Compliance Assembly Robot Arm (SCARA) on the harvest of different types of fruits.

Abstract
The fusion of data from different sensors often requires that an accurate geometric transformation between the sensors is known. The procedure by which these transformations are estimated is known as sensor calibration. The vast majority of calibration approaches focus on specific pairwise combinations of sensor modalities, unsuitable to calibrate robotic systems containing multiple sensors of varied modalities. This paper presents a novel calibration methodology which is applicable to multi-sensor, multi-modal robotic systems. The approach formulates the calibration as an extended optimization problem, in which the poses of the calibration patterns are also estimated. It makes use of a topological representation of the coordinate frames in the system, in order to recalculate the poses of the sensors throughout the optimization. Sensor poses are retrieved from the combination of geometric transformations which are atomic, in the sense that they are indivisible. As such, we refer to this approach as ATOM - Atomic Transformations Optimization Method. This makes the approach applicable to different calibration problems, such as sensor to sensor, sensor in motion, or sensor to coordinate frame. Additionally, the proposed approach provides advanced functionalities, integrated into ROS, designed to support the several stages of a complete calibration procedure. Results covering several robotic platforms and a large spectrum of calibration problems show that the methodology is in fact general, and achieves calibrations which are as accurate as the ones provided by state of the art methods designed to operate only for specific combinations of pairwise modalities.

Abstract
Pseudomonas syringae pv. actinidiae (Psa) has been responsible for numerous epidemics of bacterial canker of kiwi (BCK), resulting in high losses in kiwi production worldwide. Current diagnostic approaches for this disease usually depend on visible signs of the infection (disease symptoms) to be present. Since these symptoms frequently manifest themselves in the middle to late stages of the infection process, the effectiveness of phytosanitary measures can be compromised. Hyperspectral spectroscopy has the potential to be an effective, non-invasive, rapid, cost-effective, high-throughput approach for improving BCK diagnostics. This study aimed to investigate the potential of hyperspectral UV-VIS reflectance for in-situ, non-destructive discrimination of bacterial canker on kiwi leaves. Spectral reflectance (325-1075 nm) of twenty plants were obtained with a handheld spectroradiometer in two commercial kiwi orchards located in Portugal, for 15 weeks, totaling 504 spectral measurements. Several modeling approaches based on continuous hyperspectral data or specific wavelengths, chosen by different feature selection algorithms, were tested to discriminate BCK on leaves. Spectral separability of asymptomatic and symptomatic leaves was observed in all multi-variate and machine learning models, including the FDA, GLM, PLS, and SVM methods. The combination of a stepwise forward variable selection approach using a support vector machine algorithm with a radial kernel and class weights was selected as the final model. Its overall accuracy was 85%, with a 0.70 kappa score and 0.84 F-measure. These results were coherent with leaves classified as asymptomatic or symptomatic by visual inspection. Overall, the findings herein reported support the implementation of spectral point measurements acquired in situ for crop disease diagnosis.

Abstract
Total white blood cells count is an important diagnostic parameter in both human and veterinary medicines. State-of-the-art is performed by flow cytometry combined with light scattering or impedance measurements. Spectroscopy point-of-care has the advantages of miniaturization, low sampling, and real-time hemogram analysis. While white blood cells are in low proportions, while red blood cells and bilirubin dominate spectral information, complicating detection in blood. We performed a feasibility study for the direct detection of white blood cells counts in canine blood by visible-near infrared spectroscopy for veterinary applications, benchmarking current chemometrics techniques (similarity, global and local partial least squares, artificial neural networks and least-squares support vector machines) with self-learning artificial intelligence, introducing data augmentation to overcome the hurdle of knowledge representativity. White blood cells count information is present in the recorded spectra, allowing significant discrimination and equivalence between hemogram and spectra principal component scores. Chemometrics methods correlate white blood cells count to spectral features but with lower accuracy. Self-Learning Artificial Intelligence has the highest correlation (0.8478) and a small standard error of 6.92 x 10(9) cells/L, corresponding to a mean absolute percentage error of 25.37%. Such allows the accurate diagnosis of white blood cells in the range of values of the reference interval (5.6 to 17.8 x 10(9) cells/L) and above. This research is an important step toward the existence of a miniaturized spectral point-of-care hemogram analyzer.

Abstract
Each year millions of tons of plastic end up in the oceans, lakes and rivers. In the spring of 2020, an European Project Semester team, composed of multicultural and multidisciplinary undergraduate students, decided to tackle this problem. This was achieved by designing, modelling and simulating a floating trash collector named Soaksy. The collector is expected to operate continuously and automatically on lakes at the view of everybody, becoming an educational and an environmental tool. This paper reports the team's journey from the initial studies, through the design, till the final simulation and tests.