Abstract
This study aims to evaluate the effect of Salvia officinalis L. (sage) essential oil (EO) on behavior of L. monocytogenes ATCC679 inoculated in beef processed by Sous-vide cook-chill (SVCC) and stored at 2 or 8 degrees C during 28 days. Minimum inhibitory concentration (MIC) of L. monocytogenes was obtained with 31.3 mu L/mL of EO. D values were determined for samples with EO (21'39 '') and without EO (21'17 ''). Beef samples were inoculated with L. monocytogenes at a concentration of 1 x 108 CFU/mL and vacuum-packed after EO addition at MIC value. Three heat treatments (F) were applied to reduce 1-log10 (F1), 2-log10 (F2) and 3-log10 (F3). EO composition was identified by gas-chromatography mass-spectrometry analysis. The main compounds identified were beta-pinene (11.70%), camphor (8.21%), beta-thujene (7.82%), 1.8-cineole (5.19%), alpha-humulene (6.07%) and endoborneol (4.87%).A reduction of approximately 1 log (CFU/g) of L. monocytogenes was observed in EO samples, compared to control samples at 2 degrees C. At 8 degrees C, despite exponential development from day 14, lower L. monocytogenes counts were observed in EO samples. Data showed that sage EO can help to control L. monocytogenes growth. However a possibility of using sage as a natural preservative, must be combined with other agents to control microbial growth more effectively.

Abstract
Currently, climate change poses a global threat, which may compromise the sustainability of agriculture, forestry and other land surface systems. In a changing world scenario, the economic importance of Remote Sensing (RS) to monitor forests and agricultural resources is imperative to the development of agroforestry systems. Traditional RS technologies encompass satellite and manned aircraft platforms. These platforms are continuously improving in terms of spatial, spectral, and temporal resolutions. The high spatial and temporal resolutions, flexibility and lower operational costs make Unmanned Aerial Vehicles (UAVs) a good alternative to traditional RS platforms. In the management process of forests resources, UAVs are one of the most suitable options to consider, mainly due to: (1) low operational costs and high-intensity data collection; (2) its capacity to host a wide range of sensors that could be adapted to be task-oriented; (3) its ability to plan data acquisition campaigns, avoiding inadequate weather conditions and providing data availability on-demand; and (4) the possibility to be used in real-time operations. This review aims to present the most significant UAV applications in forestry, identifying the appropriate sensors to be used in each situation as well as the data processing techniques commonly implemented.

Abstract
The optimisation of vineyards management requires efficient and automated methods able to identify individual plants. In the last few years, Unmanned Aerial Vehicles (UAVs) have become one of the main sources of remote sensing information for Precision Viticulture (PV) applications. In fact, high resolution UAV-based imagery offers a unique capability for modelling plant's structure making possible the recognition of significant geometrical features in photogrammetric point clouds. Despite the proliferation of innovative technologies in viticulture, the identification of individual grapevines relies on image-based segmentation techniques. In that way, grapevine and non-grapevine features are separated and individual plants are estimated usually considering a fixed distance between them. In this study, an automatic method for grapevine trunk detection, using 3D point cloud data, is presented. The proposed method focuses on the recognition of key geometrical parameters to ensure the existence of every plant in the 3D model. The method was tested in different commercial vineyards and to push it to its limit a vineyard characterised by several missing plants along the vine rows, irregular distances between plants and occluded trunks by dense vegetation in some areas, was also used. The proposed method represents a disruption in relation to the state of the art, and is able to identify individual trunks, posts and missing plants based on the interpretation and analysis of a 3D point cloud. Moreover, a validation process was carried out allowing concluding that the method has a high performance, especially when it is applied to 3D point clouds generated in phases in which the leaves are not yet very dense (January to May). However, if correct flight parametrizations are set, the method remains effective throughout the entire vegetative cycle.

Abstract
Currently, the use of free and open-source software is increasing. The flexibility, availability, and maturity of this software could be a key driver to develop useful and interesting solutions. In general, open-source solutions solve specific tasks that can replace commercial solutions, which are often very expensive. This is even more noticeable in areas requiring analysis and manipulation/visualization of a large volume of data. Considering that there is a major gap in the development of web applications for photogrammetric processing, based on open-source technologies that offer quality results, the application presented in this article is intended to explore this niche. Thus, in this article a solution for photogrammetric processing is presented, based on the integration of MicMac, GeoServer, Leaflet, and Potree software. The implemented architecture, focusing on open-source software for data processing and for graphical manipulation, visualization, measuring, and analysis, is presented in detail. To assess the results produced by the proposed web application, a case study is presented, using imagery acquired from an unmanned aerial vehicle in two different areas.

Abstract
Unmanned aerial vehicles (UAVs) are used nowadays as a standard tool to derive very high-resolution geospatial data. However, UAV payload limitation imposes the use of not such reliable hardware affecting the georeferencing precision. In the literature it is possible to find numerous studies investigating the parameters influencing UAV-based products quality. Even if new photogrammetry methods could, in theory, avoid the use of ground control points (GCPs), they still play a key role to assure quality products. Nevertheless, usually only the number and distribution of GCPs are taking into account, since both change the geometric accuracy of the final products. In order to improve the understanding of the actual influence of GCPs, in this study we evaluate how can different physical characteristics affect GCPs identification in aerial images. The results demonstrate that GCPs' color, material, size and shape, among others, may influence a precise identification in aerial imagery.

Abstract
In this study machine learning methods were applied to RGB data obtained by an unmanned aerial vehicle (UAV) to assess this effectiveness in vineyard classification. The very high-resolution UAV-based imagery was subjected to a photogrammetric processing allowing the generation of different outcomes: orthophoto mosaic, crop surface model and five vegetation indices. The orthophoto mosaic was used in an object-based image analysis approach to group pixels with similar values into objects. Three machine learning techniques-support vector machine (SVM), random forest (RF) and artificial neural network (ANN)-were applied to classify the data into four classes: grapevine, shadow, soil and other vegetation. The data were divided with 22% (n=240, 60 per class) for training purposes and 78% (n = 850) for testing purposes. The mean value of the objects from each feature were used to create a dataset for prediction. The results demonstrated that both RF and ANN models showed a good performance, yet the RF classifier achieved better results.