Carlos A. Robles‐Zazueta

This study evaluates hyperspectral reflectance for predicting biomass and traits in duckweed, finding limited accuracy for some traits but improved performance with PLSR modeling.

This study evaluates how hyperspectral reflectance from different grapevine organs predicts cluster architecture and juice quality traits, finding that cluster reflectance outperforms leaf reflectance for most traits.

This study evaluates the sensitivity of multispectral vegetation indices to leaf water and carotenoid content across biomes and finds that leaf area index explains most of the variability.

A 1,096-image pixel-labelled wheat organ dataset plus 52,078 unlabelled field images from 11 institutions enables Segformer leaf and spike segmentation near 90% mIoU, while stems lag.

This study quantifies the role of hidden canopy layers in wheat productivity, showing that photosynthetic rates in middle and bottom layers predict grain yield and that optimizing these layers can enhance food security.

The paper characterizes 124 duckweed accessions from UK sites using genome sequencing and ionomics to explore their elemental composition and ecological interactions.