Search

Understanding geometric and biophysical characteristics is essential for determining grapevine vigor and improving input management and automation in viticulture. This study compares point cloud data obtained from a Terrestrial Laser Scanner (TLS) and various UAV sensors including multispectral, panchromatic, Thermal Infrared (TIR), RGB, and LiDAR data, to estimate geometric parameters of grapevines. Descriptive statistics, linear correlations, significance using the F-test of overall significance, and box plots were used for analysis. The results indicate that 3D point clouds from these sensors can accurately estimate maximum grapevine height, projected area, and volume, though with varying degrees of accuracy. The TLS data showed the highest correlation with grapevine height (r = 0.95, p < 0.001; R2 = 0.90; RMSE = 0.027 m), while point cloud data from panchromatic, RGB, and multispectral sensors also performed well, closely matching TLS and measured values (r > 0.83, p < 0.001; R2 > 0.70; RMSE < 0.084 m). In contrast, TIR point cloud data performed poorly in estimating grapevine height (r = 0.76, p < 0.001; R2 = 0.58; RMSE = 0.147 m) and projected area (r = 0.82, p < 0.001; R2 = 0.66; RMSE = 0.165 m). The greater variability observed in projected area and volume from UAV sensors is related to the low point density associated with spatial resolution. These findings are valuable for both researchers and winegrowers, as they support the optimization of TLS and UAV sensors for precision viticulture, providing a basis for further research and helping farmers select appropriate technologies for crop monitoring. [ABSTRACT FROM AUTHOR]

The widespread adoption of digital technologies in educational systems has been globally reflecting a shift in pedagogic content delivery that seems to fit modern generations of students while tackling relevant challenges faced by the current scholar context, e.g., progress traceability, pedagogic content fair access and intuitive visual representativeness, mobility issue mitigation, and sustainability in crisis situations. Among these technologies, augmented reality (AR) emerges as a particularly promising approach, allowing the visualization of computer-generated interactive data on top of real-world elements, thus enhancing comprehension and intuition regarding educational content, often in mobile settings. While the application of AR to education has been widely addressed, issues related to performance interaction and cognitive performance are commonly addressed, with lesser attention paid to the limitations associated with setup complexity, mostly related to experiences configurating tools, or contextual range, i.e., technical/scientific domain targeting versatility. Therefore, this paper introduces ARPocketLab, a digital, mobile, flexible, and scalable solution designed for the dynamic needs of modern tutorship. With a dual-interface system, it allows both educators and students to interactively design and engage with AR content directly tied to educational outcomes. Moreover, ARPocketLab's design, aimed at handheld operationalization using a minimal set of physical resources, is particularly relevant in environments where educational materials are scarce or in situations where remote learning becomes necessary. Its versatility stems from the fact that it only requires a marker or a surface (e.g., a table) to function at full capacity. To evaluate the solution, tests were conducted with 8th-grade Portuguese students within the context of Physics and Chemistry subject. Results demonstrate the application's effectiveness in providing didactic assistance, with positive feedback not only in terms of usability but also regarding learning performance. The participants also reported openness for the adoption of AR in pedagogic contexts. [ABSTRACT FROM AUTHOR]

Crop sustainability is essential for balancing economic development and environmental care, mainly in strong and very competitive regions in the agri-food sector, such as the Region of Murcia in Spain, considered to be the orchard of Europe, despite being a semi-arid area with an important scarcity of fresh water. In this region, farmers apply efficient techniques to minimize supplies and maximize quality and productivity; however, the effects of climate change and the degradation of significant natural environments, such as, the "Mar Menor", the most extent saltwater lagoon of Europe, threatened by resources overexploitation, lead to the search of even better irrigation management techniques to avoid certain effects which could damage the quaternary aquifer connected to such lagoon. This paper describes the Irriman Platform, a system based on Cloud Computing techniques, which includes low-cost wireless data loggers, capable of acquiring data from a wide range of agronomic sensors, and a novel software architecture for safely storing and processing such information, making crop monitoring and irrigation management easier. The proposed platform helps agronomists to optimize irrigation procedures through a usable web-based tool which allows them to elaborate irrigation plans and to evaluate their effectiveness over crops. The system has been deployed in a large number of representative crops, located along near 50,000 ha of the surface, during several phenological cycles. Results demonstrate that the system enables crop monitoring and irrigation optimization, and makes interaction between farmers and agronomists easier. [ABSTRACT FROM AUTHOR]