Abstract
Global warming presents a significant threat to the sustainability of agricultural systems, demanding increased irrigation to mitigate the impacts of prolonged dry seasons. Efficient water management strategies, including deficit irrigation, have thus become essential, requiring continuous crop monitoring. However, conventional monitoring methods are laborious and time-consuming. This study investigates the potential of aerial imagery captured by unmanned aerial vehicles (UAVs) to predict critical water stress indicators-relative water content (RWC), midday leaf water potential (psi MD), stomatal conductance (gs)-as well as the pigment content (chlorophyll ab, chlorophyll a, chlorophyll b and carotenoids) of trees in an olive orchard. Both thermal and spectral vegetation indices are calculated and correlated using linear and exponential regression models. The results reveal that the thermal vegetation indices contrast in estimating the water stress indicators, with the Crop Water Stress Index (CWSI) demonstrating higher precision in predicting the RWC (R2 = 0.80), psi MD (R2 = 0.61) and gs (R2 = 0.72). Additionally, the Triangular Vegetation Index (TVI) shows superior accuracy in predicting the chlorophyll ab (R2 = 0.64) and chlorophyll a (R2 = 0.61), while the Modified Chlorophyll Absorption in Reflectance Index (MCARI) proves most effective for estimating the chlorophyll b (R2 = 0.52). This study emphasizes the potential of UAV-based multispectral and thermal infrared imagery in precision agriculture, enabling assessments of the water status and pigment content. Moreover, these results highlight the vital importance of this technology in optimising resource allocation and enhancing olive production, critical steps towards sustainable agriculture in the face of global warming.

Abstract
Grape varieties play an important role in wine's production chain, its identification is crucial for controlling and regulating the production. Nowadays, two techniques are widely used, ampelography and molecular analysis. However, there are problems with both of them. In this scenario, Deep Learning classifiers emerged as a tool to automatically classify grape varieties. A problem with the classification of on-field acquired images is that there is a lot of information unrelated to the target classification. In this study, the use of segmentation before classification to remove such unrelated information was analyzed. We used two grape varieties identification datasets to fine-tune a pre-trained EfficientNetV2S. Our results showed that segmentation can slightly improve classification performance if only unrelated information is removed.

Abstract
Grape moths are a significant pest in vineyards, causing damage and losses in wine production. Pheromone traps are used to monitor grape moth populations and determine their developmental status to make informed decisions regarding pest control. Smart pest monitoring systems that employ sensors, cameras, and artificial intelligence algorithms are becoming increasingly popular due to their ability to streamline the monitoring process. In this study, we investigate the effectiveness of using segmentation as a pre-processing step to improve the detection of grape moths in trap images using deep learning models. We train two segmentation models, the U-Net architecture with ResNet18 and InceptionV3 backbonesl, and utilize the segmented and non-segmented images in the YOLOv5s and YOLOv8s detectors to evaluate the impact of segmentation on detection. Our results show that segmentation preprocessing can significantly improve detection by 3% for YOLOv5 and 1.2% for YOLOv8. These findings highlight the potential of segmentation pre-processing for enhancing insect detection in smart pest monitoring systems, paving the way for further exploration of different training methods.

Abstract
Materials represented by measured BRDF (Bidirectional Reflectance Distribution function) with reflectance data captured from real-world materials have become increasingly prevalent due to the development of novel measurement approaches. Nowadays, important limitations can be highlighted in the current material scanning process, mostly related to the high diversity of existing materials in the real-world and the tedious process for material scanning. Consequently, new approaches are required both for the automatic material acquisition process and for the generation of measured material databases. In this study, a novel approach is proposed for modelling the material appearance by sampling hyperspectral measurements on the BRDF domain. An unmanned aerial vehicle (UAV)-based hyperspectral sensor was used to capture high spatial and spectral resolution data. The generated hyperspectral data cubes were used to identify materials with a similar spectral behaviour. Then, a sparse mapping of collected samples is developed to study the appearance of natural and artificial materials in an urban scenario. © 2021 IEEE.

Abstract
The detection of invasive plant species in aquatic ecosystems is important to help in the control or to mitigate its spread and impacts. Remote sensing (RS) can be explored in this context, helping to monitor this type of plants. This study intends to present a free to use and open-source software application that, through a graphical user interface, can process remote sensed data to monitor the spread of invasive plant species in aquatic environments, enabling a multi-temporal monitoring. Both unmanned aerial vehicle and satellite-based data were used to validate the potential of the proposed application. A site containing water hyacinth (Eichhornia crassipes) was selected as case study. Both RS platforms provided effective data to detect the areas containing water hyacinth. Thus, this tool provides an alternative and user-friendly way to include RS-based data in ecological studies allowing the detection of invasive plants in water channels.

Abstract
The estimation of dendrometric parameters of tree crops is crucial to decision making support for ecological and economic reasons. However, traditional methods for its measurement are time-consuming and laborious. Remote sensing data acquired from unmanned aerial vehicles (UAVs) combined with computer vision and Structure from Motion (SfM) algorithms can provide an easier and reliable solution to estimate those parameters. Nevertheless, various UAV flight settings can influence the quality of parameters derived from these data (e.g., flight height, imagery overlap). Thus, the main goal of this study is to assess the impact of different flight configurations on the detection of olive trees and on height and crown diameter estimation. The results showed that not only the configuration of the flight affects the dendrometric results, but also the topography of the terrain. Automatic tree detection revealed to be insensitive to the different flight configurations, whereas the tree height estimation was strongly affected. Among the analysed flights, the plan in double grid at 60 m of flight altitude and 90% of frontal overlap showed the best performance.