Abstract

Abstract
Spatial and temporal variability characterization in Precision Agriculture (PA) practices is often accomplished by proximity data gathering devices, which acquire data from a wide variety of sensors installed within the vicinity of crops. Proximity data acquisition usually depends on a hardware solution to which some sensors can be coupled, managed by a software that may (or may not) store, process and send acquired data to a back-end using some communication protocol. The sheer number of both proprietary and open hardware solutions, together with the diversity and characteristics of available sensors, is enough to deem the task of designing a data acquisition device complex. Factoring in the harsh operational context, the multiple DIY solutions presented by an active online community, available in-field power approaches and the different communication protocols, each proximity monitoring solution can be regarded as singular. Data acquisition devices should be increasingly flexible, not only by supporting a large number of heterogeneous sensors, but also by being able to resort to different communication protocols, depending on both the operational and functional contexts in which they are deployed. Furthermore, these small and unattended devices need to be sufficiently robust and cost-effective to allow greater in-field measurement granularity 365 days/year. This paper presents a low-cost, flexible and robust data acquisition device that can be deployed in different operational contexts, as it also supports three different communication technologies: IEEE 802.15.4/ZigBee, LoRa/LoRaWAN and GRPS. Software and hardware features, suitable for using heat pulse methods to measure sap flow, leaf wetness sensors and others are embedded. Its power consumption is of only 83 mu A during sleep mode and the cost of the basic unit was kept below the EUR 100 limit. In-field continuous evaluation over the past three years prove that the proposed solution-SPWAS'21-is not only reliable but also represents a robust and low-cost data acquisition device capable of gathering different parameters of interest in PA practices.

Abstract
Business models built upon multimedia/multisensory setups delivering user experiences within disparate contexts-entertainment, tourism, cultural heritage, etc.-usually comprise the installation and in-situ management of both equipment and digital contents. Considering each setup as unique in its purpose, location, layout, equipment and digital contents, monitoring and control operations may add up to a hefty cost over time. Software and hardware agnosticity may be of value to lessen complexity and provide more sustainable management processes and tools. Distributed computing under the Internet of Things (IoT) paradigm may enable management processes capable of providing both remote control and monitoring of multimedia/multisensory experiences made available in different venues. A prototyping software to perform IoT multimedia/multisensory simulations is presented in this paper. It is fully based on virtual environments that enable the remote design, layout, and configuration of each experience in a transparent way, without regard of software and hardware. Furthermore, pipelines to deliver contents may be defined, managed, and updated in a context-aware environment. This software was tested in the laboratory and was proven as a sustainable approach to manage multimedia/multisensory projects. It is currently being field-tested by an international multimedia company for further validation.

Abstract
The development and implementation of a virtual environment that aims to support farmers in managing their land and crops in a more sustainable way is presented in this paper. It allows both textual and 3D visualization of crop-related biophysical parameters, such as height, volume and length. Moreover, the latter can be dynamically altered according to various criteria. A case study was conducted in a Portuguese vineyard. The application was developed using the Unity software, while a real agricultural data feed was provided by mySense interface. The virtual environment can be seen as a valuable decision support system to assist farmers.

Abstract
In this paper we present a Deep Learning-based methodology to automatically classify 12 of the most representative grapevarieties existing in the Douro Demarked region, Portugal. The dataset used consisted of images of leaves at different stages of development, collected on their natural environment. The development of such methodologies becomes particularly important, in a scenario in which ampeleographers are disappearing, creating a gap in the task of inspection of grape varieties. Our approach was based on the transfer learning of the Xcepetion model, using Focal Loss, adaptive learning rate decay and SGD. The model obtained a F1 score of 0.93. To clearly understand the predictions of the model, and realize which regions of the image contributed the most to the classification, the LIME library was used. This way it was possible to identify the parts of the images that were considered for and against each prediction.

Abstract
Geological disasters are responsible for the loss of human lives and for significant economic and financial damage every year. Considering that these disasters may occur anywhere-both in remote and/or in highly populated areas-and anytime, continuously monitoring areas known to be more prone to geohazards can help to determine preventive or alert actions to safeguard human life, property and businesses. Remote sensing technology-especially satellite-based-can be of help due to its high spatial and temporal coverage. Indeed, data acquired from the most recent satellite missions is considered suitable for a detailed reconstruction of past events but also to continuously monitor sensitive areas on the lookout for potential geohazards. This work aims to apply different techniques and methods for extensive exploitation and analysis of remote sensing data, with special emphasis given to landslide hazard, risk management and disaster prevention. Multi-temporal SAR (Synthetic Aperture Radar) interferometry, SAR tomography, high-resolution image matching and data modelling are used to map out landslides and other geohazards and to also monitor possible hazardous geological activity, addressing different study areas: (i) surface deformation of mountain slopes and glaciers; (ii) land surface displacement; and (iii) subsidence, landslides and ground fissure. Results from both the processing and analysis of a dataset of earth observation (EO) multi-source data support the conclusion that geohazards can be identified, studied and monitored in an effective way using new techniques applied to multi-source EO data. As future work, the aim is threefold: extend this study to sensitive areas located in different countries; monitor structures that have strategic, cultural and/or economical relevance; and resort to artificial intelligence (AI) techniques to be able to analyse the huge amount of data generated by satellite missions and extract useful information in due course.