Abstract
Glaucoma is a silent disease that shows symptoms when severe, leading to partial vision loss or irreversible blindness. Early screening permits treating patients in time. For glaucoma screening, retinal images are very important since they enable the observation of initial glaucoma lesions, which typically begins with the cupping formation in the optic disc (OD). In clinical settings, practical indicators such as Cup-to-Disc Ratio (CDR) are frequently used to evaluate the presence and stage of glaucoma. The ratio between the cup and the optic disc can be measured using the vertical or horizontal diameter, or the area of the two. Mass screening programs are limited by the high costs of specialised teams and equipment. Current deep learning (DL) methods can assist the glaucoma mass screening, lower the cost and allow it to be extended to larger populations. With DL methods in the OD and optic cup (OC) segmentation, is possible to evaluate the presence of glaucoma in the patient more quickly based on cupping formation in the OD, using CDR. In this work, is assessed the contribution of Multi-Class and Single-Class segmentation methods for glaucoma screening using the 3 types of CDR. U-Net architecture is trained using transfer learning models (Inception V3 and Inception ResNet V2) to segment the OD and OC and then evaluate glaucoma prediction based on different types of CDRs indicators. The models were trained and evaluated on main public known databases (REFUGE, RIM-ONE r3 and DRISHTI-GS). The segmentation of both OD and OC reach Dice over 0.8 and IoU above 0.7. The CDRs were computed to glaucoma assessment where was reach sensitivity above 0.8, specificity of 0.7, F1-Score around 0.7 and AUC above 0.85. Finally, conclusions of segmentation methods showing adequate performance to be used in practical glaucoma screening.

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
Since the 1970s, extensive halite extraction in Macei & oacute;, Brazil, has resulted in significant geological risks, including ground collapses, sinkholes, and infrastructure damage. These risks became particularly evident in 2018, following an earthquake, which prompted the cessation of mining activities in 2019. This study investigates subsidence deformation resulting from these mining operations, focusing on the collapse of Mine 18 on 10 December 2023. We utilized the Quasi-Persistent Scatterer Interferometric Synthetic Aperture Radar (QPS-InSAR) technique to analyze a dataset of 145 Sentinel-1A images acquired between June 2019 and April 2024. Our approach enabled the analysis of cumulative displacement, the loss of amplitude stability, the evolution of amplitude time series, and the amplitude change matrix of targets near Mine 18. The study introduces an innovative QPS-InSAR approach that integrates phase and amplitude information using amplitude time series to assess the lifecycle of radar scattering targets throughout the monitoring period. This method allows for effective change detection following sudden events, enabling the identification of affected areas. Our findings indicate a maximum cumulative displacement of -1750 mm, with significant amplitude changes detected between late November and early December 2023, coinciding with the mine collapse. This research provides a comprehensive assessment of deformation trends and ground stability in the affected mining areas, providing valuable insights for future monitoring and risk mitigation efforts.

Abstract
Synthetic aperture radar (SAR) technology has emerged as a pivotal tool in viticulture, offering unique capabilities for various applications. This study provides a comprehensive overview of the current state-of-the-art applications of SAR in viticulture, highlighting its significance in addressing key challenges and enhancing viticultural practices. The historical evolution and motivations behind SAR technology are also provided, along with a demonstration of its applications within viticulture, showcasing its effectiveness in various aspects of vineyard management, including delineating vineyard boundaries, assessing grapevine health, and optimizing irrigation strategies. Furthermore, future perspectives and trends in SAR applications in viticulture are discussed, including advancements in SAR technology, integration with other remote sensing techniques, and the potential for enhanced data analytics and decision support systems. Through this article, a comprehensive understanding of the role of SAR in viticulture is provided, along with inspiration for future research endeavors in this rapidly evolving field, contributing to the sustainable development and optimization of vineyard management practices.

Abstract
The grapevine variety identification is important in the wine's production chain since it is related to its quality, authenticity and singularity. In this study, we addressed the data augmentation approach to identify grape varieties with images acquired in-field. We tested the static transformations, RandAugment, and Cutmix methods. Our results showed that the best result was achieved by the Static method generating 5 images per sample (F1 = 0.89), however without a significative difference if compared with RandAugment generating 2 images. The worst performance was achieved by CutMix (F1 = 0.86).

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.