Abstract
The bioelectrical impedance analysis (BIA) is a non-destructive technique that has been successfully used to assess the body and carcass composition of farm species. This study aimed to predict intramuscular fat (IMF) and physicochemical traits in the longissimus thoracis et lumborum muscle (LM) of beef, using BIA. These traits were evaluated in LM samples of 52 crossbred heifer carcasses. The BIA was performed in LM, using a 50 Hz frequency high precision impedance converter system. A correlation analysis of the studied variables was performed. Then a stepwise with a k-folds cross validation procedure was used to modelling the prediction of IMF and physicochemical traits from BIA parameters (24.5% <= CV <= 47.3%). Wide variation was found for IMF and BIA parameters. In general, correlations of BIA parameters with IMF and physicochemical traits were moderate to high and were similar for all BIA parameters (-0.50 <= r <= 0.50 only for total pigments, a* and pH48). It was possible to predict IMF and physicochemical traits from BIA. The best fit explained 79.3% of the variation in IMF, while for physicochemical traits the best fits were for sarcomere length and shear force (64.4% and 60.5%, respectively). The results confirmed the potential of BIA for objective measurement of meat quality.

Abstract
Developed focusing agriculture sustainability, mySense is a comprehensive close-range sensor-based data management environment to improve precision farming practices. It integrates discussion platforms for quick problem solving through experts support and a computational intelligence layer for multipurpose application (e.g. vine variety discrimination, plant disease detection and identification). Attending the need for keeping track of agricultural crops not only based on close-range sensing but also at a macro perspective, mySense was complemented with proper functionalities to unlock macro-monitoring features, through the implementation of a Web-based Geographical Information System (WebGIS) planned as a sidekick application that provides agriculture professionals with visual decision support tools over remote sensed data. This paper presents and discusses its specification and implementation.

Abstract
The reconstitution of road traffic accidents scenes is a contemporary and important issue, addressed both by private and public entities in different countries around the world. However, the task of collecting data on site is not generally focused on with the same orientation and relevance. Addressing this type of accident scenario requires a balance between two fundamental yet competing concerns: (1) information collecting, which is a thorough and lengthy process and (2) the need to allow traffic to flow again as quickly as possible. This technical note proposes a novel methodology that aims to support road traffic authorities/professionals in activities involving the collection of data/evidences of motor vehicle collision scenarios by exploring the potential of using low-cost, small-sized and light-weight unmanned aerial vehicles (UAV). A high number of experimental tests and evaluations were conducted in various working conditions and in cooperation with the Portuguese law enforcement authorities responsible for investigating road traffic accidents. The tests allowed for concluding that the proposed method gathers all the conditions to be adopted as a near future approach for reconstituting road traffic accidents and proved to be: faster, more rigorous and safer than the current manual methodologies used not only in Portugal but also in many countries worldwide.

Abstract
The use of unmanned aerial vehicles (UAVs) for remote sensing applications in precision viticulture significantly increased in the last years. UAVs' capability to acquire high spatiotemporal resolution and georeferenced imagery from different sensors make them a powerful tool for a better understanding of vineyard spatial and multitemporal heterogeneity, allowing the estimation of parameters directly impacting plants' health status. In this way, the decision support process in precision viticulture can be greatly improved. However, despite the proliferation of these innovative technologies in viticulture, most of the published studies rely only on data from a single sensor in order to achieve a specific goal and/or in a single/small period of the vineyard development. In order to address these limitations and fully exploit the advantages offered by the use of UAVs, this study explores the multi-temporal analysis of vineyard plots at a grapevine scale using different imagery sensors. Individual grapevine detection enables the estimation of biophysical and geometrical parameters, as well as missing grapevine plants. A validation procedure was carried out in six vineyard plots focusing on the detected number of grapevines and missing grapevines. A high overall agreement was obtained concerning the number of grapevines present in each row (99.8%), as well as in the individual grapevine identification (mean overall accuracy of 97.5%). Aerial surveys were conducted in two vineyard plots at different growth stages, being acquired for RGB, multispectral and thermal imagery. Moreover, the extracted individual grapevine parameters enabled us to assess the vineyard variability in a given epoch and to monitor its multi-temporal evolution. This type of analysis is critical for precision viticulture, constituting as a tool to significantly support the decision-making process.

Abstract
Unmanned aerial vehicles (UAVs) have become popular in recent years and are now used in a wide variety of applications. This is the logical result of certain technological developments that occurred over the last two decades, allowing UAVs to be equipped with different types of sensors that can provide high-resolution data at relatively low prices. However, despite the success and extraordinary results achieved by the use of UAVs, traditional remote sensing platforms such as satellites continue to develop as well. Nowadays, satellites use sophisticated sensors providing data with increasingly improving spatial, temporal and radiometric resolutions. This is the case for the Sentinel-2 observation mission from the Copernicus Programme, which systematically acquires optical imagery at high spatial resolutions, with a revisiting period of five days. It therefore makes sense to think that, in some applications, satellite data may be used instead of UAV data, with all the associated benefits (extended coverage without the need to visit the area). In this study, Sentinel-2 time series data performances were evaluated in comparison with high-resolution UAV-based data, in an area affected by a fire, in 2017. Given the 10-m resolution of Sentinel-2 images, different spatial resolutions of the UAV-based data (0.25, 5 and 10 m) were used and compared to determine their similarities. The achieved results demonstrate the effectiveness of satellite data for post-fire monitoring, even at a local scale, as more cost-effective than UAV data. The Sentinel-2 results present a similar behavior to the UAV-based data for assessing burned areas.

Abstract
Phytosanitary conditions can hamper the normal development of trees and significantly impact their yield. The phytosanitary condition of chestnut stands is usually evaluated by sampling trees followed by a statistical extrapolation process, making it a challenging task, as it is labor-intensive and requires skill. In this study, a novel methodology that enables multi-temporal analysis of chestnut stands using multispectral imagery acquired from unmanned aerial vehicles is presented. Data were collected in different flight campaigns along with field surveys to identify the phytosanitary issues affecting each tree. A random forest classifier was trained with sections of each tree crown using vegetation indices and spectral bands. These were first categorized into two classes: (i) absence or (ii) presence of phytosanitary issues. Subsequently, the class with phytosanitary issues was used to identify and classify either biotic or abiotic factors. The comparison between the classification results, obtained by the presented methodology, with ground-truth data, allowed us to conclude that phytosanitary problems were detected with an accuracy rate between 86% and 91%. As for determining the specific phytosanitary issue, rates between 80% and 85% were achieved. Higher accuracy rates were attained in the last flight campaigns, the stage when symptoms are more prevalent. The proposed methodology proved to be effective in automatically detecting and classifying phytosanitary issues in chestnut trees throughout the growing season. Moreover, it is also able to identify decline or expansion situations. It may be of help as part of decision support systems that further improve on the efficient and sustainable management practices of chestnut stands.