Simulation of Automatically Annotated Visible and Multi-/Hyperspectral Images Using the Helios 3D Plant and Radiative Transfer Modeling Framework.

Deep learning and multimodal remote and proximal sensing are widely used for analyzing plant and crop traits, but many of these deep learning models are supervised and necessitate reference datasets with image annotations. Acquiring these datasets often demands experiments that are both labor-intensive and time-consuming. Furthermore, extracting traits from remote sensing data beyond simple geometric features remains a challenge.

Practical Considerations and Limitations of Using Leaf and Canopy Temperature Measurements as a Stomatal Conductance Proxy: Sensitivity across Environmental Conditions, Scale, and Sample Size.

Stomatal conductance () is a crucial component of plant physiology, as it links plant productivity and water loss through transpiration. Estimating indirectly through leaf temperature () measurement is common for reducing the high labor cost associated with direct measurement. However, the relationship between observed and can be notably affected by local environmental conditions, canopy structure, measurement scale, sample size, and itself.