Abstract
Almond trees in Portugal are susceptible to aphid infestation, which can result in reduced fruit production. To effectively tackle this issue, the combination of remote sensing (RS) data and machine learning (ML) classifiers can be used to accurately detect the presence of aphids. This study focuses in the implementation of ML classifiers and RS data analysis to identify aphids on almond trees, using high-resolution multispectral data collected through an unmanned aerial vehicle (UAV) in a Portuguese almond orchard. Four ML classifiers, kNN, SVM, RF and XGBoost, were employed and fine-tuned using vegetation indices derived from spectral data. The results revealed that the SVM classifier achieved an overall accuracy (OA) of 77%, followed by kNN with an OA of 74%, while XGBoost and RF achieved OAs of 71% and 69%, respectively. Consequently, this study demonstrates the viability of employing RS data and ML classifiers for aphid identification in almond orchards.

Abstract
Grapevines (Vitis vinifera L.) are one of the most economically relevant crops worldwide, yet they are highly vulnerable to various diseases, causing substantial economic losses for winegrowers. This systematic review evaluates the application of remote sensing and proximal tools for vineyard disease detection, addressing current capabilities, gaps, and future directions in sensor-based field monitoring of grapevine diseases. The review covers 104 studies published between 2008 and October 2024, identified through searches in Scopus and Web of Science, conducted on 25 January 2024, and updated on 10 October 2024. The included studies focused exclusively on the sensor-based detection of grapevine diseases, while excluded studies were not related to grapevine diseases, did not use remote or proximal sensing, or were not conducted in field conditions. The most studied diseases include downy mildew, powdery mildew, Flavescence dor & eacute;e, esca complex, rots, and viral diseases. The main sensors identified for disease detection are RGB, multispectral, hyperspectral sensors, and field spectroscopy. A trend identified in recent published research is the integration of artificial intelligence techniques, such as machine learning and deep learning, to improve disease detection accuracy. The results demonstrate progress in sensor-based disease monitoring, with most studies concentrating on specific diseases, sensor platforms, or methodological improvements. Future research should focus on standardizing methodologies, integrating multi-sensor data, and validating approaches across diverse vineyard contexts to improve commercial applicability and sustainability, addressing both economic and environmental challenges.

Abstract
The Douro Demarcated Region is fundamental to local cultural and economic identity. Despite its importance, the region faces the challenge of abandoned vineyard plots, caused, among other factors, by the high costs of maintaining vineyards on hilly terrain. To solve this problem, the European Union (EU) offers subsidies to encourage active cultivation, with the aim of protecting the region's cultural and environmental heritage. However, monitoring actively cultivated vineyards and those that have been abandoned presents considerable logistical challenges. With 43,843 vineyards spread over 250,000 hectares of rugged terrain, control of these plots is limited, which hampers the effectiveness of preservation and incentive initiatives. Currently, the EU only inspects 5 per cent of farmers annually, which results in insufficient coverage to ensure that subsidies are properly used and vineyards are actively maintained. To complement this limited monitoring, organisations such as the Instituto dos Vinhos do Douro e do Porto (IVDP) use aerial and satellite images, which are manually analysed to identify abandoned or active plots. To overcome these limitations, images can be analysed using deep learning methods, which have already shown great potential in agricultural applications. In this context, our research group has carried out some preliminary evaluations for the automatic detection of abandoned vineyards using deep learning models, which, despite showing promising results on the dataset used, proved to be limited when applied to images of the entire region. In this study, a new dataset was expanded to 137,000 images collected between 2018 and 2023, filling critical gaps in the previous datasets by including greater temporal and spatial diversity. Subsequently, a careful evaluation was carried out with various DL models. As a result, the ViT_b32 model demonstrated superior performance, achieving an average accuracy of 0.99 and an F1 score of 0.98, outperforming CNN-based models. In addition to the excellent results obtained, this dataset represents a significant contribution to advancing research in precision viticulture, providing a solid and relevant basis for future studies and driving the development of solutions applied to vineyard monitoring in the Douro Demarcated Region. These advances not only improve efficiency in detecting abandoned plots, but also contribute significantly to optimising the use of subsidies in the region.

Abstract
The Eurasian grapevine (Vitis vinifera L.) is one of the most extensively cultivated horticultural crop worldwide, with significant economic relevance, particularly in wine production. Accurate grapevine variety identification is essential for ensuring product authenticity, quality control, and regulatory compliance. Traditional identification methods have inherent limitations limitations; ampelography is subjective and dependent on skilled experts, while molecular analysis is costly and time-consuming. To address these challenges, recent research has focused on applying deep learning (DL) and machine learning (ML) techniques for grapevine variety identification. This study systematically analyses 37 recent studies that employed DL and ML models for this purpose. The objective is to provide a detailed analysis of classification pipelines, highlighting the strengths and limitations of each approach. Most studies use DL models trained on leaf images captured in controlled environments at distances of up to 1.2 m. However, these studies often fail to address practical challenges, such as the inclusion of a broader range of grapevine varieties, using data directly acquired in the vineyards, and the evaluation of models under adverse conditions. This review also suggests potential directions for advancing research in this field.

Abstract
Reduced-impact logging (RIL) has been recognized as a promising strategy for biodiversity conservation and carbon sequestration within sustainable forest management (SFM) areas. However, monitoring the forest understory-a critical area for assessing logging impacts-remains challenging due to limitations in conventional methods such as field inventories and global navigation satellite system (GNSS) surveys, which are time-consuming, costly, and often lack accuracy in complex environments. Additionally, aerial and satellite imagery frequently underestimate the full extent of disturbances as the forest canopy obscures understory impacts. This study examines the effectiveness of the relative density model (RDM), derived from airborne LiDAR data, for mapping and monitoring understory disturbances. A field-based validation of LiDAR-derived RDM was conducted across 25 sites, totaling 5504.5 hectares within the Jamari National Forest, Rond & ocirc;nia, Brazil. The results indicate that the RDM accurately delineates disturbances caused by logging infrastructure, with over 90% agreement with GNSS field data. However, the model showed the greatest discrepancy for skid trails, which, despite their lower accuracy in modeling, accounted for the largest proportion of the total impacted area among infrastructure. The findings include the mapping of 35.1 km of primary roads, 117.4 km of secondary roads, 595.6 km of skid trails, and 323 log landings, with skid trails comprising the largest proportion of area occupied by logging infrastructure. It is recommended that airborne LiDAR assessments be conducted up to two years post-logging, as impacts become less detectable over time. This study highlights LiDAR data as a reliable alternative to traditional monitoring approaches, with the ability to detect understory impacts more comprehensively for monitoring selective logging in SFM areas of the Amazon, providing a valuable tool for both conservation and climate mitigation efforts.

Abstract
Almond cultivation is of great socio-economic importance worldwide. With the demand for almonds steadily increasing due to their nutritional value and versatility, optimizing the management of almond orchards becomes crucial to promote sustainable agriculture and ensure food security. The present systematic literature review, conducted according to the PRISMA protocol, is devoted to the applications of remote sensing technologies in almond orchards, a relatively new field of research. The study includes 82 articles published between 2010 and 2023 and provides insights into the predominant remote sensing applications, geographical distribution, and platforms and sensors used. The analysis shows that water management has a pivotal focus regarding the remote sensing application of almond crops, with 34 studies dedicated to this subject. This is followed by image classification, which was covered in 14 studies. Other applications studied include tree segmentation and parameter extraction, health monitoring and disease detection, and other types of applications. Geographically, the United States of America (USA), Australia and Spain, the top 3 world almond producers, are also the countries with the most contributions, spanning all the applications covered in the review. Other studies come from Portugal, Iran, Ecuador, Israel, Turkey, Romania, Greece, and Egypt. The USA and Spain lead water management studies, accounting for 23% and 13% of the total, respectively. As far as remote sensing platforms are concerned, satellites are the most widespread, accounting for 46% of the studies analyzed. Unmanned aerial vehicles follow as the second most used platform with 32% of studies, while manned aerial vehicle platforms are the least common with 22%. This up-to-date snapshot of remote sensing applications in almond orchards provides valuable insights for researchers and practitioners, identifying knowledge gaps that may guide future studies and contribute to the sustainability and optimization of almond crop management.