Abstract
The technological revolution that we have been witnessing recently has allowed components miniaturization and made electronic components accessible. Hyperspectral sensors benefited from these advances and could be mounted on unmanned aerial vehicles, which was unthinkable until recently. This fact significantly increased the applications of hyperspectral data, namely in agriculture, especially in the detection of diseases at an early stage. The vineyard is one of the agricultural sectors that has the most to gain from the use of this type of data, both by the economic value and by the number of diseases the plants are exposed to. The Flavescense dorée is a disease that attacks vineyards and may conduct to a significant loss. Nowadays, the detection of this disease is based on the visual identification of symptoms performed by experts who cover the entire area. However, this work remains tedious and relies only on the human eye, which is a problem since sometimes healthy plants are torn out, while diseased ones are left. If the experts think they have found symptoms, they take samples to send to the laboratory for further analysis. If the test is positive, then the whole vine is uprooted, to limit the spread of the disease. In this context, the use of hyperspectral data will allow the development of new disease detection methods. However, it will be necessary to reduce the volume of data used to make them usable by conventional resources. Fortunately, the advent of machine learning techniques empowered the development of systems that allow better decisions to be made, and consequently save time and money. In this article, a machine learning approach, which is based on an Autoencoder to automatically detect wine disease, is proposed.

Abstract
Botnets are responsible for some of the major malicious traffic on the Internet: DDoS attacks, Mail SPAM, brute force attacks, portscans, and others. Its dangerousness is due to the coordinated amount of infected hosts focusing on a single target. More contributions are in need, considering that (A) ML has been used for cyberattacks identification with better accuracy than standard NIDS equipments, (B) Botnet attacks are one of the most dangerous threats on the Internet. (C) the difficulties in getting representative datasets on some Botnets, and (D) Botnet traffic can be misunderstood by its infrastructure protocol. In this paper, we focus on the identification of Botnet traffic, preventing the communication from the Botmaster to the infected hosts and consequently the Botnet cyberattacks. CICFlowMeter and Machine Learning algorithms were used to analyse Botnet2014 public dataset on four different scenarios: all Botnet traffic on a single class, each class per Botnet traffic and the influence of the IPs address fields Botnet traffic detection. The results shows that Random Forest (RF) and Decision Tree (CART) archived similar accuracies on Botnet traffic classification. Important to say that CART obtained similar results with 10-20% of machine time. The metrics shown that the analysis per specific Botnet has higher accuracy than Any Botnet Traffic analysis. Also, the analysis with the IP addresses and L4 Ports scenario has higher accuracy but lower F1-Score that the equivalent without IP addresses or L4 Ports. At last, Feature Importance results confirms the literature, that Botnet traffic is not a single uniform protocol, but a collection of very different ways of communications between the botmaster and the infected hosts.

Abstract
Wine is the most important product from the Douro Region, in Portugal. Ampelographs are disappearing, and farmers need new solutions to identify grapevine varieties to ensure high-quality standards. The development of methodology capable of automatically identify grapevine are in need. In the scenario, deep learning based methods are emerging as the state-of-art in grapevines classification tasks. In previous work, we verify the deep learning models would benefit from focus classification patches in leaves images areas. Deep learning segmentation methods can be used to find grapevine leaves areas. This paper presents a methodology to segment grapevines images automatically based on the U-net model. A private dataset was used, composed of 733 grapevines images frames extracted from 236 videos collected in a natural environment. The trained model obtained a Dice of 95.6% and an Intersection over Union of 91.6%, results that fully satisfy the need of localise grapevine leaves.

Abstract
A colonic polyp is a growth in the lining of the colon or rectum and can be detected through colonoscopies. The efficiency of colonoscopies depends on the number of polyps detected. However, detecting and classifying polyps is difficult, tedious, and prone to error. Knowing that this process's performance is far from perfect, the objective of this project is to help colonoscopists in the detection of polyps during the medical intervention, using Deep Learning (DL) alongside the image recognition capabilities of Convolutional Neural Networks (CNN) models that can process colonoscopy images at high speed in real-time. In this paper, were tested different state-of-the-art CNNs using a transfer learning approach, achieving an average accuracy of 95,70% in the polyp detection task. Multiple public datasets were used in this study to train, test, and evaluate the classifiers. The negative class included images representative of healthy tissue as well as other pathologies, so the models would not mistake other diseases as polyps.

Abstract
In a precision agriculture context, the amount of geospatial data available can be difficult to interpret in order to understand the crop variability within a given terrain parcel, raising the need for specific tools for data processing and analysis. This is the case for data acquired from Unmanned Aerial Vehicles (UAV), in which the high spatial resolution along with data from several spectral wavelengths makes data interpretation a complex process regarding vegetation monitoring. Vegetation Indices (VIs) are usually computed, helping in the vegetation monitoring process. However, a crop plot is generally composed of several non-crop elements, which can bias the data analysis and interpretation. By discarding non-crop data, it is possible to compute the vigour distribution for a specific crop within the area under analysis. This article presents QVigourMaps, a new open source application developed to generate useful outputs for precision agriculture purposes. The application was developed in the form of a QGIS plugin, allowing the creation of vigour maps, vegetation distribution maps and prescription maps based on the combination of different VIs and height information. Multi-temporal data from a vineyard plot and a maize field were used as case studies in order to demonstrate the potential and effectiveness of the QVigourMaps tool. The presented application can contribute to making the right management decisions by providing indicators of crop variability, and the outcomes can be used in the field to apply site-specific treatments according to the levels of vigour.

Abstract
Deformation monitoring is a common practice in most of dams to ensure their structural health and safety status. Systematic monitoring is frequently carried out by means of geotechnical sensors and geodetic techniques that, although very precise an accurate, can be time-consuming and economically costly. Remote sensing techniques are proved to be very effective in assessing deformation. Changes in the structure, shell or associated infrastructures of dams, including adjacent slopes, can be efficiently recorded by using satellite Synthetic Aperture Radar Inteferometry (InSAR) techniques, in particular, Muti-Temporal InSAR time-series analyses. This is a mature technology nowadays but not very common as a routine procedure for dam monitoring. Today, thanks to the availability of spaceborne satellites with high spatial resolution SAR images and short revisit times, this technology is a powerful cost-effective way to monitor millimeter-level displacements of the dam structure and its surroundings. What is more, the potential of the technique is increased since the Copernicus C-band SAR Sentinel-1 satellites are in orbit, due to the high revisit time of 6 days and the free data availability. ReMoDams is a Spanish research project devoted to provide the deformation monitoring of several embankments dams using advances time-series InSAR techniques. One of these dams is The Arenoso dam, located in the province of Cordova (southern Spain). This dam has been monitored using Sentinel-1 SAR data since the beginning of the mission in 2014. In this paper, we show the processing of 382 SLC SAR images both in ascending and descending tracks until March 2019. The results indicate that the main displacement of the dam in this period is in the vertical direction with a rate in the order of -1 cm/year in the central part of the dam body. (C) 2020 The Authors. Published by Elsevier B.V.