Abstract
In this study, we analyze the potential of collaborative robotics in automated quality inspections in the industry. The development of a solution integrating an industrial vision system allowed evaluating the performance of collaborative robots in a real case. The use of these tools allows reducing quality defects as well as costs in the manufacturing process. In this system, image processing methods use resources based on depth and surface measurements with high precision. The system fully processes a panel, observing the state of the surface to detect any potential defect in the panels produced to increase the quality of production.

Abstract
Robotic manipulation, an area inside the field of industrial automation and robotics, consists of using a robotic arm to guide and grasp a desired object through actuators such as a vacuum or fingers, among others. Some objects, such as fragile ceramic pieces, require special attention to the force and the gripping method exerted on them. For this purpose, two grippers were developed, where one of them is a rotary vacuum gripper and the other is an impact gripper with three fingers, each one equipped with a load sensor capable of evaluating the values of load exerted by the grip actuators onto the object to be manipulated. The vacuum gripper was developed for the purpose of glazing a coffee saucer and the gripper with three fingers was developed for the purpose of polishing a coffee cup. Being that the impact gripper with sensorial feedback reacts to the excess and lack of grip force exerted, both these grippers were developed with success, handling with ease the ceramic pieces proposed.

Abstract
The spatial monitoring of plant diversity in the endangered species-rich grasslands of European mountain pastoral systems is an important step for fairer and more efficient Agri-Environmental policy schemes supporting conservation. This study assessed the underlying support for a spatially explicit monitoring of plant species richness at parcel level (policy making scale) in Southern European mountain grasslands, with statistical models informed by Sentinel-2 satellite and environmental factors. Twenty-four grassland parcels were surveyed for species richness in the Peneda-Geres National Park, northern Portugal. Using a multi-model inference approach, three competing hypotheses guided by the species-scaling theoretical framework were established: species-area (P1), species-energy (P2) and species-spectral heterogeneity (P3), each representing a candidate spatial pathway to predict species richness. To evaluate the statistical support of each spatial pathway, generalized linear models were fitted and model selection based on Akaike information criterion (AIC) was conducted. Later, the performance of the most supported spatial pathway(s) was assessed using a leave-one-out cross validation. A model guided by the species-energy hypothesis (P2) was the most parsimonious spatial pathway to monitor plant species richness in mountain grassland parcels (P2, AICc = 137.6, increment AIC = 0.0, wi = 0.97). Species-area and species-spectral heterogeneity pathways (P1 and P3) were less statistically supported (Delta AICc values in the range 5.7-10.0). The underlying support of the species-energy spatial pathway was based on Sentinel-2 satellite data, namely on the near-infrared (NIR) green ratio in the spring season (NIR/Green(spring)) and on its ratio of change between spring and summer (NIR/Green(change)). Both predictor variables related negatively to species richness. Grassland parcels with lower values of near-infrared (NIR) green ratio and lower seasonal amplitude presented higher species richness records. The leave-one-out cross validation indicated a moderate performance of the species-energy spatial pathway in predicting species richness in the grassland parcels covered by the dataset (R-2 = 0.44, RMSE = 4.3 species, MAE = 3.5 species). Overall, a species-energy framework based on Sentinel 2 data resulted in a promising spatial pathway for the monitoring of species richness in mountain grassland parcels and for informing decision making on Agri-Environmental policy schemes. The near-infrared (NIR) green ratio and its change in time seems a relevant variable to deliver predictions for plant species richness and further research should be conducted on that.

Abstract
Precisely monitoring the growth condition and nutritional status of maize is crucial for optimizing agronomic management and improving agricultural production. Multi-spectral sensors are widely applied in ecological and agricultural domains. However, the images collected under varying weather conditions on multiple days show a lack of data consistency. In this study, the Mini MCA 6 Camera from UAV platform was used to collect images covering different growth stages of maize. The empirical line calibration method was applied to establish generic equations for radiometric calibration. The coefficient of determination (R-2) of the reflectance from calibrated images and ASD Handheld-2 ranged from 0.964 to 0.988 (calibration), and from 0.874 to 0.927 (validation), respectively. Similarly, the root mean square errors (RMSE) were 0.110, 0.089, and 0.102% for validation using data of 5 August, 21 September, and both days in 2019, respectively. The soil and plant analyzer development (SPAD) values were measured and applied to build the linear regression relationships with spectral and textural indices of different growth stages. The Stepwise regression model (SRM) was applied to identify the optimal combination of spectral and textural indices for estimating SPAD values. The support vector machine (SVM) and random forest (RF) models were independently applied for estimating SPAD values based on the optimal combinations. SVM performed better than RF in estimating SPAD values with R-2 (0.81) and RMSE (0.14), respectively. This study contributed to the retrieval of SPAD values based on both spectral and textural indices extracted from multi-spectral images using machine learning methods.

Abstract
The interest in expanding the production of hops outside the traditional cultivation regions, mainly motivated by the growth of the craft brewery business, justifies the intensification of studies into its adaptation to local growing conditions. In this study, four field trials were undertaken on a twenty-year-old hop garden, over periods of up to three years to assess the effect of important agro-environmental variation factors on hop phenol and phenolic composition and to establish its relationship with the elemental composition of hop cones. All the field trials were arranged as factorial designs exploring the combined effect of: (1) plots of different vigour plants x year; (2) plots of different plant vigor x algae- and nutrient-rich foliar sprays x year; (3) plot x liming x year; and (4) cultivars (Nugget, Cascade, Columbus) x year. Total phenols in hops, were significantly influenced by most of the experimental factors. Foliar spraying and liming were the factors that least influenced the measured variables. The year had the greatest effect on the accumulation of total phenols in hop cones in the different trials and may have contributed to interactions that often occurred between the factors under study. The year average for total phenol concentrations in hop cones ranged from 11.9 mg g(-1) to 21.2 mg g(-1). Significant differences in quantity and composition of phenolic compounds in hop cones were also found between cultivars. The phenolic compounds identified were mainly flavonols (quercetin and kaempferol glycosides) and phenolic carboxylic acids (p-coumaric and caffeic acids).

Abstract
Wildfires can profoundly impact many aspects of matter flows and energy budgets in ecosystems. Exacerbated by projected shifts in climate, land use, and forest management, changes in fire regimes can lead to decreased ecosystem resilience, regime shifts, and ecosystem collapse. Thorough assessments of ecosystem resilience to wildfires are thus critical to bridge gaps between science, policy, and management. To that end, approaches based on ecosystem functioning offer an integrative view of ecosystem responses to wildfire-induced changes and provide quicker, quantifiable responses to disturbances that are more directly connected to ecosystem services. In that regard, satellite remote sensing can be employed to easily and frequently monitor multiple dimensions of ecosystem functioning over large areas and across time, and to evaluate ecosystem functioning resilience to wildfires. This study describes an approach for identifying potential regime shifts based on satellite-based surrogates of four key dimensions of ecosystem functioning: primary production, water content, albedo, and sensible heat. To that end, we classified the trajectories after wildfires in 2005, in NW Iberian Peninsula, for the 2000–2018 period, into five main types, using two metrics of medium-to-long term post-fire recovery. Then, we derived a synthetic indicator to analyse the overall “strength-of-evidence� of potential regime shifts across dimensions. Potential regime shifts were identified for each dimension of ecosystem functioning considered, with the main effects associated with the sudden removal of vegetation. For primary production, regime shifts may be linked to changes in land cover and use, as well as management. Changes in the concentrations of impervious and radiation-absorbing materials following wildfires may be responsible for regime shifts in water content and albedo, with loss of canopy moisture due to fire-related damage leading to vegetation mortality during post-fire recovery. On the other hand, regime shifts in sensible heat were less frequent, since wildfires tend to have transient effects on this dimension of ecosystem functioning. Overall, our results show that our approach successfully captured different patterns of post-fire recovery and resilience across multiple dimensions of ecosystem functioning. We argue that our approach can provide an enhanced characterization of ecosystem resilience to wildfires, and support the identification of potential regime shifts after such disturbances, ultimately upholding promising implications for post-fire ecosystem management.