Mercedes Marzoa Tanco

Agricultural production is a cornerstone of Uruguay's economy, contributing significantly to its GDP with an impact of approximately 11 points. This sector is characterized by its high technological demand and a declining labor force. Traditionally, the agricultural workforce has not required extensive technical skills, but the rapid advancement of technology necessitates a shift towards more technologically adept labor. Thus, the adoption of advanced technologies and the retraining of the workforce are imperative. Labor retraining within the agricultural domain is essential to fortify the sector's competitiveness, sustainability, and resilience amidst contemporary challenges. Precision agriculture advocates integrating cutting-edge technologies to optimize crop management and agricultural resource utilization. Implementing autonomous robotics has the potential to mitigate labor requirements, foster workforce technological education, and propel advancements toward precision agriculture. Founded in 2013, the MINA group at the Faculty of Engineering of the University of the Republic (Uruguay) has been actively engaged in robotics projects tailored for agricultural applications. Initially focusing on orchards of pome fruits from 2013 to 2023, the group undertook tasks such as harvest support and estimation of harvest quantity and quality. Subsequently, efforts have been directed towards pest control measures targeting pests such as birds and ants, and weed management. This paper delineates the intricacies of these distinct projects, elucidating the technologies employed and developed, outlining achieved results to date, and envisaging the potential for widespread adoption of this technology at a feasible cost.

In light of escalating concerns over climate change, harnessing oceanic data becomes increasingly urgent. Oceans serve as linchpins in understanding the intricate dynamics governing climate phenomena, exerting pivotal influence over global weather patterns and ecological systems. Despite scientific consensus on climate change impacts, including temperature shifts and acidification, a lack of data infrastructure hampers understanding marine ecosystems.This paper presents an adaptive control system for autonomous sailboats that aims to navigate efficiently in varied conditions by favoring the acquisition of oceanographic data. Combining reinforcement and imitation learning, the controller emulates human decision-making, enabling robust navigation. While showcasing promising results, challenges persist in adverse varied conditions. This challenge is exacerbated when confronting necessitating adaptive control mechanisms resilient to wear and tear.Simulators are vital for training due to the need for vast data volumes. Real-world data collection is costly and risky, while simulations accelerate learning. This study employs a simulator operating at ten times real-time speed, significantly simplifying scenario generation by adjusting factors such as sailboat position, orientation, target location, water current, and wind.Nevertheless, this novel approach signifies progress in addressing climate challenges and advancing oceanic research, using advanced computational methods

The agricultural industry is being transformed, thanks to recent innovations in computer vision and deep learning. However, the lack of specific datasets collected in natural agricultural environments is, arguably, the main bottleneck for novel discoveries and benchmarking. The present work provides a novel dataset, Magro, and a framework to expand data collection. We present the first version of the Magro Dataset V1.0, consisting of nine ROS bags (and the corresponding raw data) containing data collected in apple and pear crops. Data were gathered, repeating a fixed trajectory on different days under different illumination and weather conditions. To support the evaluation of loop closure algorithms, the trajectories are designed to have loop closures, revisiting some places from different viewpoints. We use a Clearpath’s Jackal robot equipped with stereo cameras pointing to the front and left side, a 3D LIDAR, three inertial measurement units (IMU), and wheel encoders. Additionally, we provide calibrated RTK GPS data that can be used as ground truth. Our dataset is openly available, and it will be updated to have more data and variability. Finally, we tested two existing state-of-the-art algorithms for vision and point cloud-based localization and mapping on our novel dataset to validate the dataset’s usability.

During the last decades the presence of women in the area of Computer Science has decreased in most countries. In the last years, at the Facultad de Ingeniería (School of Engineering), Universidad de la República, Uruguay, we have carried out several activities with the goal of bringing girls closer to Computer Science area, on the occasion of Girls in ICT Day. Through these activities, we intend to eliminate certain preconceived ideas and negative stereotypes that promote girls’ distancing from careers of this area. This paper presents the experience of the three virtual workshops carried out during 2021 (in virtual modality, due to the COVID-19 pandemic), which focused on programming, data, and geographic information systems, and the analysis of a set of virtual platforms considered for each workshop.