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Percorrer Escola Superior de Tecnologia e Gestão por Objetivos de Desenvolvimento Sustentável (ODS) "02:Erradicar a Fome"
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- Automated preprocessing of olive leaf images for cultivar classification using YOLO11Publication . Mendes, João; Lima, José; Rodrigues, Nuno; Pereira, Ana I.Olive cultivation is a pillar of Mediterranean agriculture, deeply rooted in both tradition and economic importance. This paper presents a novel two-phase methodology for the automated preprocessing of olive leaf images to facilitate accurate cultivar classification. Leveraging the state-of-the-art YOLO11 framework, two models (YOLO11n and YOLO11s) were employed for detection and segmentation tasks. A comprehensive dataset, combining in-situ captured images with publicly available data, was meticulously annotated using both manual and semi-automatic processes. The detection model identifies individual olive leaves, while the segmentation model isolates the leaves by replacing the background with a uniform white, thereby simulating laboratory conditions. Experimental results demonstrate that YOLO11n outperforms YOLO11s in terms of mean Average Precision and F1-score, confirming the feasibility of deploying the system on mobile devices for real-time, in-field classification.
- Culture as a model for the social inclusion of Brazilian migrants in PortugalPublication . Almeida, Lígia; Moutinho, Raquel; Silva, Natacha Jesus; Leite, Jorge; Oliveira, Marcelo; Caldas, JoséMigrants and refugees’ influx into Europe has been steadily rising, and social indicators consistently show a decrease in economic and social protection towards vulnerable populations. Recent research shows that a sense of belonging is central towards integration in a new country. Unfamiliarity with culture contributes to isolating migrants and prevents participatory citizenship. Our objectives are to identify gaps in cultural competence and accessibility to culture in our country, understanding their cross-sectoral consequences – namely regarding the Brazilian community. Our study followed a qualitative methodology (online semi-structured questionnaires) for collecting and analysing data (content analysis) and was conducted in Porto, the second largest Portuguese city. Participants were 38 Brazilian immigrants, contacted through NGOS’s, social associations and institutions during the pandemic. Brazilian collective is a strong consumer of cultural events and resources, having a very strong perception regarding its value and role when it comes to their own paths of integration.
- Empowering olive cultivation with artificial intelligence: a systematic literature review on advancements and prospectsPublication . Mendes, João; Lima, José; Costa, Lino; Pereira, Ana I.This study provides a Systematic Literature Review on the application of Artificial Intelligence algorithms in the primary sector of olive cultivation. It compiles and analyses a collection of studies that leverage AI to enhance the efficiency and sustainability of olive production, maintenance, and harvesting processes. In this study, 43 papers were reviewed from the databases IEEE, Scopus, and Web of Science through the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method. This research aims to identify AI applications in the primary olive growing sector. The findings highlight a significant trend toward adopting advanced AI techniques, particularly Deep Learning algorithms such as Convolutional Neural Networks, for many tasks ranging from cultivar identification and foliar disease classification to crop yield forecasting with high accuracies.
- UAV-assisted navigation for insect traps in olive grovesPublication . Berger, Guido S.; Bonzatto Junior, Luciano; Pinto, Milena F.; Oliveira Júnior, Alexandre de; Mendes, João; Pereira, Ana I.; Silva, Yago M. R. da; Valente, António; Lima, JoséUnmanned Aerial Vehicles (UAVs) have emerged as valuable tools in precision agriculture due to their ability to provide timely and detailed information over large agricultural areas. In this sense, this work aims to evaluate the semi-autonomous navigation capacity of a multirotor UAV when applied in the field of precision agriculture. For this, a small aircraft is used to identify and track a set of fiducial markers (Ar Track Alvar) in an environment that simulates inspections of insect traps in olive groves. The purpose of this marker is to provide a visual reference point for the drone’s navigation system. Once the Ar Track Alvar marker is detected, the robot will receive navigation information based on the marker’s position to approach the specific trap. The experimental setup evaluated the computer vision algorithm applied to the UAV to make it recognize the Ar Track Alvar marker and then reach the trap efficiently. Experimental tests were conducted in a indoor and outdoor environment using DJI Tello. The results demonstrated the feasibility of applying these fiducial markers as a solution for the UAV’s navigation in this proposed scenario.