Results 171 to 180 of about 12,889,375 (285)

Collaborative representation and confidence-driven semi-supervised learning for hyperspectral image classification. [PDF]

Sci Rep
Chen Y, Lu H, Huang X.
europepmc   +1 more source

Phenomic prediction of club wheat milling yields

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Near‐infrared reflectance spectroscopy (NIRS) provides a nondestructive method for estimating physical and chemical grain properties and is widely used in breeding programs to phenotype traits such as texture, color, moisture, protein, and oil content. Club wheat (Triticum aestivum subsp.
Peter Schmuker, Alecia Kiszonas, Sheri Rynearson, Kim Garland‐Campbell, Michael Pumphrey   +4 more
wiley   +1 more source

HSICNet a novel deep learning architecture for hyperspectral image classification in remote sensing and environmental monitoring. [PDF]

Sci Rep
Purnachand K, Samrin R, Guttikonda JB, Kotoju R, Addepalli PS, Mohan A.   +5 more
europepmc   +1 more source

Scaling up soybean breeding: Satellite imagery delivers accurate maturity estimation across plot sizes

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Accurate and scalable phenotyping is essential for accelerating genetic gain in soybean (Glycine max (L.) Merr.) breeding programs. Traditional methods for estimating physiological maturity are labor‐intensive and prone to subjectivity, limiting throughput and consistency. This study evaluates the potential of high‐resolution satellite imagery
Anastasios Mazis, Sarah Anderson, Guilherme Simiqueli, Adam Barbeau, Sara Tirado Tolosa, Julien Linares, Nathan Coles   +6 more
wiley   +1 more source

CauseHSI: Counterfactual-Augmented Domain Generalization for Hyperspectral Image Classification via Causal Disentanglement. [PDF]

J Imaging
Li X, Yang Z, Li W.
europepmc   +1 more source

Multiscale Feature-Learning with a Unified Model for Hyperspectral Image Classification. [PDF]

Sensors (Basel), 2023
Arshad T, Zhang J, Ullah I, Ghadi YY, Alfarraj O, Gafar A.   +5 more
europepmc   +1 more source

Evaluation of uncrewed aerial system‐derived vegetation indices for forage trait phenomic prediction in oat–pea intercrop breeding trials

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Accelerating genetic gain in intercrop forage breeding requires scalable phenotyping approaches that can capture both yield and quality variation across complex genotype combinations. This study evaluated the use of uncrewed aerial system (UAS)‐derived multispectral vegetation indices (VIs) for predicting forage yield and nutritive quality in ...
Milcah Kigoni, Raysa Gevartosky, Lucas B. Munaro, Tim Mies, Luis Gehrke, Jessica Rutkoski, Juan David Arbelaez   +6 more
wiley   +1 more source

Dynamic gated fusion network with hierarchical multi-scale attention for hyperspectral image classification. [PDF]

Sci Rep
Shi X, Liu L, Bao X, Pan B, Hussain S.
europepmc   +1 more source

Determination of dry bean physiological maturity from UAS RGB time series using a stacking ensemble of color‐derived spectral features

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Evaluating physiological maturity is an important trait in dry bean breeding (Phaseolus vulgaris L.), but field scoring can be inaccurate, subjective, and a persistent bottleneck in multi‐environment trials. To address this, we developed a low‐cost, high‐throughput pipeline that predicts plot‐level days after planting at maturity from time ...
Aliasghar Bazrafkan, Lovepreet Singh, John Hawkins, Evan M. Wright, Valerio Hoyos‐Villegas   +4 more
wiley   +1 more source

SGFNet: Redundancy-Reduced Spectral-Spatial Fusion Network for Hyperspectral Image Classification. [PDF]

Entropy (Basel)
Wang B, Cao C, Kong D.
europepmc   +1 more source