Abstract
Insect pests are the main cause of loss of productivity and quality in crops worldwide. Insect monitoring becomes necessary for the early detection of pests and thus avoiding the excessive use of pesticides. Automatic detection of insects attracted by traps is a form of monitoring. Modern data-driven methods present great results for object detection when representative datasets are available, but public datasets for insect detection are few and small. Pest24 public dataset is extensive, but noisy resulting in a poor detection rate. In this work, we aim to improve insect detection in the Pest24 dataset. We propose the creation of three sub-datasets selecting the highest represented classes, the highest colour discrepancy, and the one with the highest relative scale, respectively. Several Faster R-CNN and YOLOv5 architectures are explored, and the best results are achieved with the YOLOv5 with an mAP of 95.5%.

Abstract
On rugged slopes overlooking the Douro River we find the Alto Douro Wine Region in Portugal, populated by plantations in schist lands of difficult access and mostly manual work. The combined features of this region are a source of motivation to explore remote sensing techniques associated with artificial intelligence. In this paper, a preliminary approach for terrace vineyards detection is presented. This is a key-enabling task towards the achievement of important goals such as multi-temporal crop evaluation and cultures characterization. The proposed methodology consists in the application of a deep learning model (U-net) to detect the terrace vineyards using satellite images dataset acquired with Google Earth Pro. The proposed methodology showed very promising detection capabilities.

Abstract
This work focuses on a reservoir with water leaks since its construction, the Beninar reservoir. The purpose of this reservoir was to regulate the Adra River basin, lying between the provinces of Almeria and Granada, and located south of Sierra Nevada Mountains (in the Inner Zones of the Betic Cordilleras, SE Spain). This basin extends over 746 km(2), at an altitude of 2780 m, with a very rough terrain and frequent torrential water flow. Due to the continuous extension of greenhouses in the east and west parts of Almeria, the water demand for agriculture and urban consumption increases day by day. As a consequence, aquifers are being overexploited, causing the current system to not be sustainable for a long time, that is, the storage capacity of the underground media and their possible contributions to an efficient management of resources have not been adequately taken into account. The Beninar dam has always had problems with water leaks. The dam was built even knowing that the land was not the most suitable, due to the frequent earth movements that took place in the town of Beninar, which was submerged beneath the waters of the reservoir. In this work, we process multi-temporal SAR datasets coming from the C-band satellites ERS-1/2, Envisat, and Sentinel-1A/B using MT-InSAR techniques, being able to monitor the deformation behavior of this dam for a long time period of more than 25 years, from 1992 to 2018.

Abstract
Three-dimensional (3D) image mapping of real-world scenarios has a great potential to provide the user with a more accurate scene understanding. This will enable, among others, unsupervised automatic sampling of meaningful material classes from the target area for adaptive semi-supervised deep learning techniques. This path is already being taken by the recent and fast-developing research in computational fields, however, some issues related to computationally expensive processes in the integration of multi-source sensing data remain. Recent studies focused on Earth observation and characterization are enhanced by the proliferation of Unmanned Aerial Vehicles (UAV) and sensors able to capture massive datasets with a high spatial resolution. In this scope, many approaches have been presented for 3D modeling, remote sensing, image processing and mapping, and multi-source data fusion. This survey aims to present a summary of previous work according to the most relevant contributions for the reconstruction and analysis of 3D models of real scenarios using multispectral, thermal and hyperspectral imagery. Surveyed applications are focused on agriculture and forestry since these fields concentrate most applications and are widely studied. Many challenges are currently being overcome by recent methods based on the reconstruction of multi-sensorial 3D scenarios. In parallel, the processing of large image datasets has recently been accelerated by General-Purpose Graphics Processing Unit (GPGPU) approaches that are also summarized in this work. Finally, as a conclusion, some open issues and future research directions are presented.

Abstract
The water content in an agricultural crop is of crucial importance and can either be estimated through proximal or remote sensing techniques, allowing better irrigation scheduling and avoiding extreme water stress periods. However, the current climate change context is increasing the use of eco-friendly practices to reconcile water management and thermal protection from sunburn. These approaches aim to mitigate summer stress factors (high temperature, high radiation, and water shortage) and improve the plants' thermal efficiency. In this study, data from unmanned aerial vehicles (UAVs) were used to monitor the efficiency of foliar kaolin application (5%) in a commercial vineyard. Thermal infrared imagery (TIR) was used to compare the canopy temperature of grapevines with and without kaolin and to compute crop water stress and stomatal conductance indices. The gas exchange parameters of single leaves were also analysed to ascertain the physiological performance of vines and validate the UAV-based TIR data. Generally, plants sprayed with kaolin presented a lower temperature compared to untreated plants. Moreover, UAV-based data also showed a lower water stress index and higher stomatal conductance, which relate to eco-physiological measurements carried out in the field. Thus, the suitability of UAV-based TIR data proved to be a good approach to monitor entire vineyards in regions affected by periods of heatwaves, as is the case of the analysed study area.

Abstract
Nowadays, many drone models are available, designed for the most diverse applications. However, the various models fall into one of two types of drone: multi-copter or fixed-wing. The first type of drone consumes a lot of energy, since motors have to turn during all the flight. The former type of drone, in general needs a runway to take-off and landing. Moreover, they fly fast and cannot be motionless, which is unsuitable for many applications. In this paper we present a new paramotor drone, conceived and designed to overcome the highlighted limitations and to be a low-cost solution adapted for most applications. The selection of the various components of the presented prototype was based on a very thorough study, considering aerodynamic and efficiency criteria.