Results 191 to 200 of about 13,368,143 (296)

Phenotypic selection of high-yielding, water-productive wheat (Triticum aestivum L.) putative mutants under well-watered and limited-irrigation conditions. [PDF]

Sci Rep
Saber AA.
europepmc   +1 more source

Transgenic approach to improve wheat (Triticum aestivum L.) nutritional quality

, 2009
Kisgyorgy, B. N., Tamas, L., Yang, M-S., Rakszegi, M., Wilkinson, M. D., Tamas, C., Lang, L., Bedo, Z.   +7 more
core   +1 more source

Predicting soybean aboveground biomass in the short‐season region of Canada: Integrating vegetative cover and canopy height

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Aboveground biomass (ABM) is a key determinant of soybean (Glycine max [L.] Merr.) yield and can be used to select for stress‐resilient cultivars. The objective of our study was to develop a predictive model describing ABM in short‐season soybean from vegetative cover (VC) and canopy height (CH).
Malcolm J. Morrison, Thomas L. Hotte, A. Claire Gahagan, Nathaniel W. W. Ort, Elroy R. Cober   +4 more
wiley   +1 more source

Identifying candidate genes for grain protein content in wheat (Triticum aestivum L.) by integrated GWAS and transcriptome analysis. [PDF]

BMC Plant Biol
Shang J, Diao Y, Wang Y, Zhang X, Yao X, Yao Y.   +5 more
europepmc   +1 more source

Drone‐based phenotyping of maize for multiple disease resistance and yield in breeding field trials

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Improving selection for multiple disease resistance (MDR) and yield in maize (Zea mays L.) requires high‐throughput, objective phenotyping tools, particularly under field conditions where several foliar diseases co‐occur. We evaluated drone‐based multispectral vegetation indices (VIs) for predicting resistance to northern leaf blight (NLB ...
Danilo E. Moreta, Nina Blahut, Nicholas Kaczmar, Judith M. Kolkman, Gary Bergstrom, Margaret Smith, Michael A. Gore, Rebecca Nelson   +7 more
wiley   +1 more source

Genome-wide association mapping and haplotype analysis reveal the genetic architecture of sodicity tolerance in bread wheat (Triticum aestivum L.). [PDF]

Sci Rep
Kumar A, Sathishkumar R, Kant C, Mohapatra A, Sharma D, Kulshrestha N, Mehla S, Pandey R, Mir RR, Jacob SR, Sathee L, Sharma R, Bhaskar Y, Singh AK, Singh GP, Kumar S.   +15 more
europepmc   +1 more source

Phenotype imputation using high‐throughput phenotyping produces a new secondary trait for further selection modeling

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Data from high‐throughput phenotyping (HTP) could be used for phenotype imputation to enhance genomic selection (GS) or gene discovery, but this has not been explored in crop species. Three machine learning models: multiple linear regression (MLR), missForest, and k‐nearest neighbors, were evaluated for grain yield (GY) phenotype imputation in
Raysa Gevartosky, Lucas Berger Munaro, Jessica E. Rutkoski   +2 more
wiley   +1 more source

Zinc nanoparticles alleviate vanadium stress in wheat (Triticum aestivum L.): impacts on vanadium accumulation, nitrogen assimilation, and amino acid profile. [PDF]

BMC Plant Biol
Wang Z, Zhao Y, Tang C, Yao D, Liu Y, Mi X, Li Y.   +6 more
europepmc   +1 more source

UAV‐based deep transfer learning to improve grain yield prediction in winter wheat across temporal and spatial variability

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Accurate prediction of grain yield (GY) remains a major challenge in plant breeding due to complex interactions between genotype, environment, and management (G × E × M) factors. Remote sensing data from unmanned aerial vehicles (UAVs) equipped with multispectral sensors have emerged as a pivotal resource for high‐throughput phenotyping.
Swas Kaushal, Mohammad Maruf Billah, Mandeep Singh, Dante Koupal, Pradeep Raj Sitaula, Jyotirmoy Halder, Jagdeep Sidhu, Maitiniyazi Maimaitijiang, Sunish K. Sehgal   +8 more
wiley   +1 more source

Molecular mapping of QTLs for stripe rust resistance in bread wheat (Triticum aestivum L.). [PDF]

BMC Plant Biol
Sharma R, Kumar S, Prasad P, Kumar U, Yashveer S, Gangwar OP, Udupa S, Sharma I, Tiwari R, Sharma P.   +9 more
europepmc   +1 more source