A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy.

The trait-based approach in plant ecology aims at understanding and classifying the diversity of ecological strategies by comparing plant morphology and physiology across organisms. The major drawback of the approach is that the time and financial cost of measuring the traits on many individuals and environments can be prohibitive. We show that combining near-infrared spectroscopy (NIRS) with deep learning resolves this limitation by quickly, non-destructively, and accurately measuring a suite of traits, including plant morphology, chemistry, and metabolism.

Shifts in soil and plant functional diversity along an altitudinal gradient in the French Alps.

Objectives: Altitude integrates changes in environmental conditions that determine shifts in vegetation, including temperature, precipitation, solar radiation and edaphogenetic processes. In turn, vegetation alters soil biophysical properties through litter input, root growth, microbial and macrofaunal interactions. The belowground traits of plant communities modify soil processes in different ways, but it is not known how root traits influence soil biota at the community level.