Results 221 to 230 of about 144,931 (354)

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 identification and expression pattern analysis of the COBRA-like gene family of Triticum aestivum (L.) under drought and heat stresses. [PDF]

BMC Plant Biol
Du C, Cui D, Zhou Z, Yang N, Chang Y, Zhu J.   +5 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

Transcriptional signatures associated with female receptivity and longevity in genetically male-sterile wheat (Triticum aestivum L.). [PDF]

Sci Rep
Whitford R, Baumann U, Yang X, Tucker MR, Ogink K, Mazdeh FM, Dissanayake I, Davey MW, Rohde A.   +8 more
europepmc   +1 more source

Knockout of TaCKX2.2 in Wheat Improved Grain Yield by Increasing Grain Number Per Spike and Grain Size Under Field Production Conditions

Plant, Cell &Environment, EarlyView.
Zhenzhen Zhu, Fangyao Sun, Guihua Bai, Yanpeng Ding, Fuping Zhang, Xi He, Zhongfu Ni, Qixin Sun, Zhenqi Su   +8 more
wiley   +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

Genetic identification and characterization of putatively novel, stable, and validated loci for plant height in wheat (Triticum aestivum L.). [PDF]

BMC Genomics
Yao Q, Chen B, Lohani MN, Chen Q, Wang T, Chen J, Wang C, Zhang H, Lu L, Habib A, Ma J, Xie Q.   +11 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

Retraction: Assessing the potential of exogenous caffeic acid application in boosting wheat (Triticum aestivum L.) crop productivity under salt stress. [PDF]

PLoS One
PLOS One Editors.
europepmc   +1 more source

AGIcam: An open‐source Internet of Things–based camera system for automated in‐field phenotyping and yield prediction

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Continuous, high‐frequency monitoring is essential to capture rapid phenological transitions and dynamic crop responses to the environment. However, most phenotyping platforms lack the temporal resolution and automation required for consistent, season‐long trait assessment.
Worasit Sangjan, Nisit Pukrongta, Trevor Buchanan, Arron H. Carter, Michael O. Pumphrey, Sindhuja Sankaran   +5 more
wiley   +1 more source