Investigating the contribution of image time series observations to cauliflower harvest-readiness prediction.

Cauliflower cultivation is subject to high-quality control criteria during sales, which underlines the importance of accurate harvest timing. Using time series data for plant phenotyping can provide insights into the dynamic development of cauliflower and allow more accurate predictions of when the crop is ready for harvest than single-time observations. However, data acquisition on a daily or weekly basis is resource-intensive, making selection of acquisition days highly important.

Data-driven crop growth simulation on time-varying generated images using multi-conditional generative adversarial networks.

Background: Image-based crop growth modeling can substantially contribute to precision agriculture by revealing spatial crop development over time, which allows an early and location-specific estimation of relevant future plant traits, such as leaf area or biomass. A prerequisite for realistic and sharp crop image generation is the integration of multiple growth-influencing conditions in a model, such as an image of an initial growth stage, the associated growth time, and further information about the field treatment. While image-based models provide more flexibility for crop growth modeling than process-based models, there is still a significant research gap in the comprehensive integration of various growth-influencing conditions.

Estimating daily semantic segmentation maps of classified ocean eddies using sea level anomaly data from along-track altimetry.

Mesoscale eddies, which are fast-moving rotating water bodies in the ocean with horizontal scales ranging from 10 km to 100 km and above, are considered to be the weather of the oceans. They are of interest to marine biologists, oceanographers, and geodesists for their impact on water mass, heat, and nutrient transport. Typically, gridded sea level anomaly maps processed from multiple radar altimetry missions are used to detect eddies.

Recognizing protected and anthropogenic patterns in landscapes using interpretable machine learning and satellite imagery.

The accurate and comprehensive mapping of land cover has become a central task in modern environmental research, with increasing emphasis on machine learning approaches. However, a clear technical definition of the land cover class is a prerequisite for learning and applying a machine learning model. One of the challenging classes is naturalness and human influence, yet mapping it is important due to its critical role in biodiversity conservation, habitat assessment, and climate change monitoring.

Diversity of CRISPR-Cas type II-A systems in .

is a commensal Streptococcal species that is often associated with invasive bacterial infections. However, little is known about its molecular genetic background. Many Streptococcal species, including , harbor clustered regularly interspaced short palindromic repeats (CRISPR)-Cas systems.