Abstract
Traditionally farmers have used their perceptual sensorial systems to diagnose and monitor their crops health and needs. However, humans possess five basic perceptual systems with accuracy levels that can change from human to human which are largely dependent on the stress, experience, health and age. To overcome this problem, in the last decade, with the help of the emergence of smartphone technology, new agronomic applications were developed to reach better, cost-effective, more accurate and portable diagnosis systems. Conventional smartphones are equipped with several sensors that could be useful to support near real-time usual and advanced farming activities at a very low cost. Therefore, the development of agricultural applications based on smartphone devices has increased exponentially in the last years. However, the great potential offered by smartphone applications is still yet to be fully realized. Thus, this paper presents a literature review and an analysis of the characteristics of several mobile applications for use in smart/precision agriculture available on the market or developed at research level. This will contribute to provide to farmers an overview of the applications type that exist, what features they provide and a comparison between them. Also, this paper is an important resource to help researchers and applications developers to understand the limitations of existing tools and where new contributions can be performed.

Abstract
Precision Agriculture (PA) has a fundamental role in the sustainability of agricultural systems, supporting decision-making of agricultural crops, increasing yield and quality in production. In the present research a PA approach for viticulture was made combining remote sensing data and robotic monitoring. With this approach it was intended to perform a spatial-temporal analysis of the grapevine phenology, according the 3 periods of the grape's biological cycle reproductive cycle, peak of the season and vegetative dormancy - corresponding to the years of 2017/18, for a specific area of the Green Wine Region, from Celorico de Basto (Portugal). The proposed methodology is based in the automation of spatial analyses through Geographical Information Systems (GIS), Google Earth Engine (GEE) and Python programming language. GEE was used for image acquisition and processing data of several indices, as Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI) and Visible Atmospherically Resistant Index ( VARI). Regarding the geoprocessing of environmental factors, it was considered the following parameters: precipitation, temperature and soil moisture. Afterwards, NDVI was selected for a space-time analysis of the vineyard phenology, once this index represents a close dynamic to the vineyard biological cycle. From the relation between environmental factors and NDVI it was possible to interpret the space-time dynamics of the vineyard phenology. Finally, a spatial interpolation of yield and NDVI was made to understand the influence of NDVI in the yield. It can be assumed that the NDVI does not have a statistically significant influence on vineyard yield.

Abstract
Data processing (or the transformation of data into knowledge and/or information) has become an indispensable tool for decision-making in many areas of engineering. Engineering optimization problems with many objectives are common. However, the decision-making process for these problems is complicated since there are many trade-offs that are difficult to identify. Thus, in this work, multivariate statistical methods, Principal Component Analysis (PCA) and Cluster Analysis (CA), have been studied and applied to analyze the results of many objective engineering optimization problems. PCA reduces the number of objectives to a very small number, CA through the similarities and dissimilarities, creates groups of solutions, i.e., bringing together in the same group solutions with the same characteristics and behaviors. Two engineering optimization problems with many objectives are solved: a mechanical problem consisting in the optimal design of laminated plates, with four objectives and a problem of optimization of the radar waveform, with nine objectives. For the problem of the design of laminated plates through PCA allowed to reduce to two objectives and through CA it was possible to create three distinct groups of solutions. For the problem of optimization of the radar waveform, it was possible to reduce the objectives from nine to two objectives representing the greatest variability of the data, and CA defined three distinct groups of solutions. These results demonstrate that these tools are effective to assist the decision-making processes in the presence of a large number of solutions and/or objectives.

Abstract
<p>The WATER4EVER Project (http://water4ever.eu/) was built on the premise that agriculture is by far the largest consumer of water, with about 70% of the diverted water being used in irrigation. Agriculture is also considered as a key source of diffuse pollution with inefficient practices resulting in high water and nutrient (particularly N and P) surpluses that are transferred to water bodies through diffuse processes (runoff and leaching), promoting eutrophication, with associated biodiversity loss. WATER4EVER aims thus to develop new monitoring strategies at the plot and catchment scales to provide detailed information of water and nutrient flow, and gain new insights on the connectivity between both scales. New monitoring strategies were developed and tested in agricultural fields in Portugal, Spain, Italy and Turkey and included: (i) crop physiological indicators assessment using static sensors for defining improved deficit irrigation strategies for woody crops; (ii) crop stress and productivity maps from measurements taken with a smart sensor mounted on a tractor and equipped with LIDAR 2D, normalized difference vegetation index (NDVI) and thermal cameras, and a GNSS receiver; (iii) leaf area index maps at 30 m resolution derived from ATCOR and Landsat 8 imagery data using the NDVI and the Soil Adjusted Vegetation Index (SAVI); (iv) soil moisture maps at 100 m resolution by combining the 10 m resolution synthetic-aperture radar (SAR) images from Sentinel 1 with the 10 m resolution NDVI computed from Sentinel 2 images, averaged into 100 m cells, and then by considering the backscatter difference with the driest day, or alternatively the backscatter difference between two consecutive dates; (v) soil moisture maps at 1 km resolution created with the DISaggregation based on a Physical And Theoretical scale CHange (DISPATCH) algorithm for the downscaling of the 40 km SMOS (Soil Moisture and Ocean Salinity) soil moisture data using land surface temperature (LST) and NDVI data; (vi) conventional monitoring techniques combined with modeling tools for assessing the impact of different soil managements (conventional tillage, tillage with grass trips, grass cover) on soil infiltration, soil water content, runoff and soil erosion of hillslope vineyards; (vii) an improved deterministic model for irrigation and fertigation management at the plot scale; and (viii) a decision support system for irrigation water management at the plot scale which integrated a deterministic model for irrigation scheduling and the NDVI computed from Sentinel 2 imagery data for crop growth monitoring. Preliminary results derived from the use of the innovative monitoring and mapping strategies, besides model applications are presented. The remote sensing products described above were also applied for catchment modeling validation of streamflow, which results fall outside the scope of this communication. WATER4EVER activities were thus wide and diverse, aimed at optimizing crop management practices which will help to promote the sustainability of different Mediterranean production systems.</p><p> </p><p>WATER4EVER is funded by the European Commission under the framework of the ERA-NET COFUND WATERWORKS 2015 Programme</p>

Abstract
Contribution: An analysis of the extent to which sustainability is present in the syllabi, project briefs, report templates, and student final reports of the three Iberian European project semester (EPS) providers, over a five-year period. Background: EPS is a one-semester capstone project framework that adopts project-based learning and multicultural, multidisciplinary teamwork. Educating engineers for sustainable development requires fostering critical and ethical thinking and a desire for equity, solidarity and preservation of natural resources, and cultural and genetic diversity. Existing engineering capstone design programs emphasize solving real world problems, hands-on training, and soft skills, but few focus on sustainability aspects of engineering design. The three Iberian EPS providers adopt project-based learning and teamwork methodologies, promoting the development of transversal skills and addressing sustainability in a multicultural and multidisciplinary background. Intended Outcomes: To show that the three Iberian EPS providers follow these recommendations and contribute to raising students' awareness of sustainable development. Application Design: The proposed sustainability learning assessment method collects evidence from syllabi, project briefs, report templates, and final reports to extract faculty and student perspectives. The sustainability-related terms collected were processed into word cloud format, allowing a simple and intuitive interpretation of students' understanding of sustainability, and in co-occurrence network format, to understand if sustainability has a pervasive or confined presence within the reports. Findings: Iberian EPS faculty and students are aware of the social, economic, and environmental impact of their projects, in terms of quality of life, social responsibility, the use of resources, and environmentally friendly technology.

Abstract
Ocean exploration and monitoring with autonomous platforms can provide researchers and decision makers with valuable data, trends and insights into the largest ecosystem on Earth. Regardless of the recognition of the importance of such platforms in this scenario, their design and development remains an open challenge. In particular, energy efficiency, control and robustness are major concerns with implications in terms of autonomy and sustainability. Wingsails allow autonomous boats to navigate with increased autonomy, due to lower power consumption, and greater robustness, due to simpler control. Within the scope of a project that addresses the design, development and deployment of a rigid wing autonomous sailboat to perform long term missions in the ocean, this paper summarises the general principles for airfoil selection and wingsail design in robotic sailing, and are given some insights on how these aspects influence the autonomous sailboat being developed by the authors.