This systematic review analyzed the role of microlearning in basic education, identifying the most widely used Digital Information and Communication Technologies, relevant learning theories, and the role of social technologies from a Science, Technology, Society, and Environment (STSE) perspective. Following PRISMA 2020, searches were conducted in Web of Science, Scopus, ERIC, and IEEE Xplore databases. Studies on microlearning were selected based on previously defined eligibility criteria. The review process in Rayyan involved deduplication, screening, and full-text analysis. Data were qualitatively analyzed using content analysis, and methodological quality was assessed with CASP and the Downs and Black. The findings highlight that microlearning, integrated with digital tools such as online platforms, mobile apps, and short videos, significantly enhances student motivation, performance, and interaction; content in short modules facilitates knowledge retention and connects concepts to real-life situations. Promising trends include mobile technologies and gamification, which foster active, meaningful learning. Grounded in theories like Self-Determination, Constructionism, and Constructivism, microlearning personalizes teaching and promotes engagement, critical thinking, and accessibility, contributing to inclusive and sustainable education. From a STSE perspective, social technologies enhance autonomy, social interaction, and ethical–environmental awareness. In Brazil, further research on digital platforms and gamified strategies is needed to drive innovative educational practices.

Estimating foliar damage is essential in agricultural processes to provide proper crop management, such as monitoring the defoliation level to take preventive actions. Furthermore, it is helpful to avoid the reduction of plant energy production, nutrition decrement, and consequently, the reduction of the final production of the crop and economic losses. In this sense, numerous proposals support the defoliation estimate task, ranging from traditional methodologies to computational solutions. However, subjectivity characteristics, reproducibility limitations, and imprecise results persist. Then, these circumstances justify the search for new solutions, especially in defoliation assessments. The main goal of this paper consists of developing an automatic method to estimate the percentage of damaged leaf areas consumed by insects. As a novelty, our method provides high precision in calculating defoliation severity caused by insect predation on the leaves of various plant species and works effectively to estimate leaf loss in leaves with border damage. We describe our method and evaluate its performance concerning 12 different plant species. Our experimental results demonstrate high accuracy in the determination of leaf area loss with a correlation coefficient superior to 0.84 for apple, blueberry, cherry, corn, grape, bell pepper, potato, raspberry, soybean, and strawberry leaves, and mean absolute error (MAE) less than 4% in defoliation levels up to 54% in soybean, strawberry, potato, and corn leaves. In addition, the method maintains a mean error of less than 50%, even for severe defoliation levels up to 99%.

The understanding of the complexity of outdoor environments is an essential issue for the development of efficient processes of autonomous mobility, especially in areas with uneven illumination and without a well-defined road. In this context, the detection of ground and obstacles plays a relevant role in giving the first impressions of the external surroundings to a machine. Furthermore, it can guide independent movements and decisions. In this study, we introduce a segmentation method that detects ground and non-ground points of complex scenes under different exposures to illumination, textures, and shading. We prepared a dataset with images collected from some environments in which trees are prominent obstacles. The proposed method uses contrast templates, statistical measures, and morphological operators to reach the ground segmentation. Experiments showed satisfactory results in which trees were well detected and the ground was efficiently segmented with the maintenance of the structure of the image.