Results 111 to 120 of about 4,378 (202)

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

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

MaizeEar‐SAM: Zero‐shot maize ear phenotyping

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Quantifying the variation in yield component traits of maize (Zea mays L.), which collectively determine the overall productivity of this globally significant crop, is critical to plant genetics research, plant breeding, and the development of improved agronomic practices.
Hossein Zaremehrjerdi, Lisa Coffey, Talukder Jubery, Huyu Liu, Jon Turkus, Kyle Linders, James C. Schnable, Patrick S. Schnable, Baskar Ganapathysubramanian   +8 more
wiley   +1 more source

High‐throughput unmanned aerial vehicle phenomics and machine learning enables accurate early single‐plant biomass prediction in lettuce

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Lettuce (Lactuca sativa L.) exhibits pronounced genotype and environment‐dependent growth variation, making early, non‐destructive single‐plant biomass prediction critical for breeding. However, harvest‐based measurements fail to capture growth dynamics.
Görkem Eren Özdemir, Ahmet Rıza Ağır, Burak Aktürk, Nida Selen Baysal, Adem Dogan, Aaron J. DeSalvio, Mustafa A. Arik, Yasin Topcu, Alper Adak   +8 more
wiley   +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

Evaluation of LiDAR‐based canopy trait estimation in Midwestern row crops

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Stand count, the number of plants per unit ground area, and leaf area index (LAI), the ratio of leaf area to ground area, are critical traits for crop research but are traditionally measured using labor‐intensive methods. While new sensing technologies are being developed, quantifying improvement in measurement efficiency and data quality ...
Matthew H. Siebers, Peng Fu, Stephen P. Long, Justin M. McGrath, Carl J. Bernacchi   +4 more
wiley   +1 more source

Genomic and phenomic prediction models improve selection accuracy for complex traits in alfalfa

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Predictive breeding has been proposed as an effective approach to accelerate genetic gain for complex traits. Genomic prediction (GP) models have been developed in alfalfa (Medicago sativa L.) for key traits in the last decade. More recently, phenomic prediction (PP) models have been proposed as a low‐cost, high‐throughput alternative to GP in
Pablo Sipowicz, Ayush K. Sharma, Mario H. Andrade, Claudio C. Fernandes Filho, L. Felipe V. Ferrão, Aditya Singh, Anju Biswas, Charlie Messina, Esteban Rios   +8 more
wiley   +1 more source

Early‐life stage phenomic prediction of field agronomic traits across breeding cycles in intermediate wheatgrass

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Perennial grains represent a promising frontier for sustainable agriculture, but breeding progress is constrained by the accessibility of genotyping and the difficulty of evaluating complex traits expressed for multiple years after establishment across heterogeneous environments.
Zachary N. Harris, Jackson Braley, Eric Cassetta, Jared Crain, Lee DeHaan, David Van Tassel, Allison Miller, Matthew J. Rubin   +7 more
wiley   +1 more source

ARADEEPOPSIS, an Automated Workflow for Top-View Plant Phenomics using Semantic Segmentation of Leaf States. [PDF]

Plant Cell, 2020
Hüther P, Schandry N, Jandrasits K, Bezrukov I, Becker C.   +4 more
europepmc   +1 more source

Phenomic prediction of wheat yield and grain quality using multispectral, hyperspectral, and thermal imagery

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Grain quality in durum wheat (Triticum turgidum L. subsp. durum (Desf.) Husn.) remains a major challenge for plant breeders because grain yield and grain protein concentration often show a trade‐off, and both are influenced by grain nitrogen concentration.
A. Pacheco, I. Ortiz‐Monasterio, F. A. Rodrigues Junior, M. Quemada, J. Burgueño, S. R. Mothukuri   +5 more
wiley   +1 more source