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
Bogota, the largest urban center and capital city of Colombia, is located within the Bogota savanna, which originated as a lake in the central part of the Colombian Eastern Cordillera. Over time, the lake transformed into a gently undulating plain with horizontally deposited sediments that formed around five million years ago. Over the last few decades, the region has undergone significant population growth and rapid urban development, largely driven by migration from rural areas. This development has substantially impacted the subsidence observed in the city, primarily due to the extraction of groundwater. A previous study by the Servicio Geologico Colombiano (SGC) utilized data from GNSS stations and synthetic aperture radar interferometry (InSAR) with TerraSAR-X SAR between 2011 and 2017 to identify a subsidence pattern in the central region of Bogota. The purpose of the study was to evaluate the risks and potential disasters associated with the subsidence phenomenon. Our study investigates both the subsidence in Bogota, previously studied, as well as the rural savanna area, which is currently undergoing significant residential and industrial development. We utilized multi-temporal InSAR (MT-InSAR) techniques with Sentinel-1 SAR images from 2014 to 2021. The analysis results indicate that the outer regions of the city display the most significant subsidence, extending from the center to the north. The subsidence velocities in these areas reach approximately 5-6 cm/year.

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
The knee is one of the most stressed joints of the human body, being susceptible to ligament injuries and degenerative diseases. Due to the rising incidence of knee pathologies, the number of knee X-rays acquired is also increasing. Such X-rays are obtained for the diagnosis of knee injuries, the evaluation of the knee before and after surgery, and the monitoring of the knee joint's stability. These types of diagnosis and monitoring of the knee usually involve radiography under physical stress. This widely used medical tool provides a more objective analysis of the measurement of the knee laxity than a physical examination does, involving knee stress tests, such as valgus, varus, and Lachman. Despite being an improvement to physical examination regarding the physician's bias, stress radiography is still performed manually in a lot of healthcare facilities. To avoid exposing the physician to radiation and to decrease the number of X-ray images rejected due to inadequate positioning of the patient or the presence of artefacts, positioning systems for stress radiography of the knee have been developed. This review analyses knee positioning systems for X-ray environment, concluding that they have improved the objectivity and reproducibility during stress radiographs, but have failed to either be radiolucent or versatile with a simple ergonomic set-up.

Abstract
This scientific paper delves into the effects of water stress on grapevines, specifically focusing on gene expression and polyphenol production. We conducted a controlled greenhouse experiment with three hydric conditions and analyzed the expression of genes related to polyphenol biosynthesis. Our results revealed significant differences in the expression of ABCC1, a gene linked to anthocyanin metabolism, under different irrigation treatments. These findings highlight the importance of anthocyanins in grapevine responses to abiotic stresses. By integrating genomics, metabolomics, and systems biology, this study contributes to our understanding of grapevine physiology under water stress conditions and offers insights into developing sensor technologies for real-world applications in viticulture. © 2023 by the authors.

Abstract
The potential of hyperspectral UV–VIS–NIR reflectance for the in-field, non-destructive discrimination of bacterial canker on kiwi leaves caused by Pseudomonas syringae pv. actinidiae (Psa) was analyzed. Spectral data (325–1075 nm) of twenty kiwi plants were obtained in vivo and in situ with a handheld spectroradiometer in two commercial kiwi orchards in northern Portugal over 15 weeks, resulting in 504 spectral measurements. The suitability of different vegetation indexes (VIs) and applied predictive models (based on supervised machine learning algorithms) for classifying non-symptomatic and symptomatic kiwi leaves was evaluated. Eight distinct types of VIs were identified as relevant for disease diagnosis, highlighting the relevance of the Green, Red, Red-Edge, and NIR spectral features. The class prediction was achieved with good model metrics, achieving an accuracy of 0.71, kappa of 0.42, sensitivity of 0.67, specificity of 0.75, and F1 of 0.67. Thus, the present findings demonstrated the potential of hyperspectral UV–VIS–NIR reflectance for the non-destructive discrimination of bacterial canker on kiwi leaves. © 2023 by the authors.