Results 181 to 190 of about 95,757 (255)

Overexpression of TaCR4-A positively regulates grain size in Triticum aestivum. [PDF]

BMC Plant Biol
Qian Q, Hu W, Yu Y, Liu Y, Tian Y, Zhang X, Min D.   +6 more
europepmc   +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

Phytochemical, in silico, and in vitro studies of wheatgrass (Triticum aestivum L.) juice powder. [PDF]

Sci Rep
Demirag AD, Bicak B, Akpinar R, Akman G, Bastemur GY, Ozkorucuklu SP, Yildirim M.   +6 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

Novel genomic regions associated with adult-plant resistance to multiple fungal pathogens in wheat (Triticum aestivum L.) revealed by DArT marker sequencing. [PDF]

J Appl Genet
Czembor E, Stępień Ł, Czembor JH.
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

Evaluating biochar's potential to reduce nitrate leaching and enhance wheat (Triticum aestivum L.) yield under different nitrogen management practices. [PDF]

Sci Rep
Ul Haq M, Khan Z, Nawaz A, Jaafar AAK.
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

Invasive Argemone mexicana's suppressive effects on germination and early growth of Triticum aestivum and Hordeum vulgare in South-western Saudi Arabia. [PDF]

PLoS One
Alshaqhaa MA, Souid I, Alshehri MD, Alyahya N, Msaada K, Guedri MM.   +5 more
europepmc   +1 more source

Assessing robustness and transferability of image‐based semantic segmentation models for major field crops

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Plant breeding is essential for crop improvement, yet progress is often hindered by slow, laborious, and subjective field phenotyping methods. High‐throughput phenotyping (HTP), particularly image‐based methodologies powered by machine learning, offers a pathway to overcome these limitations.
Gustavo N. Santiago, Pedro Cisdeli, German Mandrini, Mitchell Tuinstra, Ignacio A. Ciampitti   +4 more
wiley   +1 more source