Abstract
In agricultural applications hyperspectral imaging is used in cases where differences in spectral reflectance of the examined objects are small. However, the large amount of data generated by hyperspectral sensors requires advance processing methods. Machine learning approaches may play an important role in this task. They are known for decades, but they need high volume of data to compute accurate results. Until recently, the availability of hyperspectral data was a big drawback. It was first used in satellites, later in manned aircrafts and data availability from those platforms was limited because of logistics complexity and high price. Nowadays, hyperspectral sensors are available for unmanned aerial vehicles, which enabled to reach a high volume of data, thus overcoming these issues. This way, the aim of this paper is to present the status of the usage of machine learning approaches in the hyperspectral data processing, with a focus on agriculture applications. Nevertheless, there are not many studies available applying machine learning approach to hyperspectral data for agricultural applications. This apparent limitation was in fact the inspiration for making this survey. Preliminary results using UAV-based data are presented, showing the suitability of machine learning techniques in remote sensed data.

Abstract
Advances in Unmanned Aerial Systems (UAS) allowed them to become both flexible and cost-effective. When combined with computer vision data processing techniques they are a good way to obtain high-resolution imagery and 3D information. As such, UAS can be advantageous both for agriculture and forestry areas, where the need for data acquisition at specific times and within a specific time frame is crucial, enabling the extraction of several measurements from different crop types. In this study a low-cost UAS was used to survey an area mainly composed by chestnut trees (Castanea sativa Mill.). Flights were performed at different heights (ranging from 30 to 120 m), in single and double grid flight patterns, and photogrammetric processing was then applied. The obtained information consists of orthophoto mosaics and digital elevation models which enable the measurement of individual tree’s parameters such as tree crown diameter and tree height. Results demonstrate that despite its lower spatial resolution, data from single grid flights carried out at higher heights provided more reliable results than data acquired at lower flight heights. Higher number of images acquired in double grid flights also improved the results. Overall, the obtained results are encouraging, presenting a R² higher than 0.9 and an overall root mean square error of 44 cm.

Abstract
The cost-effectiveness of unmanned aerial systems (UAS) makes them suitable platforms to survey cultural heritage sites. Developments in photogrammetry provide methods capable to generate accurate 3D models out of 2D aerial images. Considering the involved technologies, the purpose of this paper is to document the Chapel of Espiríto Santo: a very relevant monument for Vila Real (Portugal) that is currently located at the campus of the University of Trás-os-Montes and Alto Douro. The UAS-based aerial imagery survey approach is presented along with photogrammetric process to build chapel’s 3D model. Moreover, two photogrammetric software were compared – Pix4Dmapper Pro and Agisoft Photoscan – in terms of modelling accuracy and functionalities ease of use.

Abstract
To differentiate between canopy and vegetation cover is particularly challenging. Nonetheless, it is pivotal in obtaining the exact crops' vegetation when using remote-sensing data. In this article, a method to automatically estimate and extract vineyards' canopy is proposed. It combines vegetation indices and digital elevation models - derived from high-resolution images, acquired using unmanned aerial vehicles - to differentiate between vines' canopy and inter-row vegetation cover. This enables the extraction of relevant information from a specific vineyard plot. The proposed method was applied to data acquired from some vineyards located in Portugal's north-eastern region, and the resulting parameters were validated. It proved to be an effective method when applied with consumer-grade sensors, carried by unmanned aerial vehicles. Moreover, it also proved to be a fast and efficient way to extract vineyard information, enabling vineyard plots mapping for precision viticulture management tasks.

Abstract
Machine Learning (ML) progressed significantly in the last decade, evolving the computer-based learning/prediction paradigm to a much more effective class of models known as Deep learning (DL). Since then, hyperspectral data processing relying on DL approaches is getting more popular, competing with the traditional classification techniques. In this paper, a valid ML/DL-based works applied to hyperspectral data processing is reviewed in order to get an insight regarding the approaches available for the effective meaning extraction from this type of data. Next, a general DL-based methodology focusing on hyperspectral data processing to provide farmers and winemakers effective tools for earlier threat detection is proposed.

Abstract
It is reported a new optical sensing system, based on long period fiber gratings (LPFGs) coated with cuprous oxide (Cu2O), for the quantification of ethanol concentration in ethanol-gasoline mixtures. The detection principle is based on the spectral features dependence of the Cu2O coated LPFGs on the refractive index of the surrounding medium. The chemical constitution of the ethanol-gasoline samples was obtained by gas chromatography mass spectrometry (GC) and GC thermal conductivity detection. Two different modes of operation are presented, wavelength shift and optical power shift mode of operation, with good linear relations between ethanol concentration and the corresponding spectral features of the LPFGs, R-2 = 0.999 and 0.996, respectively. In the range of ethanol concentration up to 30% v/v, the sensitivities were 0.76 +/- 0.01 nm/% v/v and 0.125 +/- 0.003 dB/% v/v with resolutions of 0.21% v/v and 0.73% v/v and limits of detection of 1.63% v/v and 2.10% v/v, for the for the same operation modes, respectively.