Results 131 to 140 of about 10,835 (252)

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

The role of phenomics and genomics in delineating the genetic basis of complex traits in millets [PDF]

Millets, comprising a diverse group of small-seeded grains, have emerged as vital crops with immense nutritional, environmental, and economic significance.
Ceasar, S A, Ingle, K P, Thakur, N R, Jadhav, Y   +3 more
core   +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

High‐throughput image‐based phenotyping of soybean hilum color

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Hilum color in soybean (Glycine max [L.] Merr.) is an important morphological trait influencing market classification and seed quality traits, yet its phenotyping is largely subjective, relying on visual inspection and assignment to one of eight color classes. This study developed an image‐based high‐throughput pipeline to measure and classify
Katherine Fortune, Ali Heidari, Milad Eskandari   +2 more
wiley   +1 more source

Advanced Studies Institute: The link between Genomics and Phenomics [PDF]

Advanced Studies Institute: The link between Genomics and Phenomics. The ASI will gather promising reseachers who have obtained their doctoral degrees within the preceding 10 years, together with eminent senior researchers from the region in a program ...

core  

Spatiotemporal dynamics of benzylisoquinoline alkaloid gene expression and co-expression networks during Papaver Somniferum developmental stages

Scientific Reports
Benzylisoquinoline alkaloids (BIAs) are essential secondary metabolites produced by Papaver somniferum, widely recognized for their pharmaceutical importance.
Zishi Wang, Quanzheng Yun, Jinyuan Hu, Zhen Wei, Deri Feng, Ning Li, He Xu, Lihong Fu, Zhenlong Wang, Shujin Li, Fangru Liu, Yan Wang, Bin Cong, Baishi Wang   +13 more
doaj   +1 more source

Genomics, phenomics, and machine learning in transforming plant research: Advancements and challenges

Horticultural Plant Journal
Advances in gene editing and natural genetic variability present significant opportunities to generate novel alleles and select natural sources of genetic variation for horticulture crop improvement.
Sheikh Mansoor, Ekanayaka M.B.M. Karunathilake, Thai Thanh Tuan, Yong Suk Chung   +3 more
doaj   +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

Philosophy of phenomic prediction and its incompatibility with causal inference

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Breeding programs need to make decisions frequently to improve populations and develop varieties efficiently. These needs led to the development of genomic prediction in the early 2000s and phenomic prediction in the mid‐2010s. In practice, phenomic prediction techniques rely on the same statistical tools and computational frameworks as other ...
Mitchell J. Feldmann, Fangyi Wang, Daniel E. Runcie   +2 more
wiley   +1 more source

Bayesian optimized color filter: A fast method for segmentation of plant phenotypes from 3D point cloud images

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Multispectral three‐dimensional (3D) imaging offers substantial potential for plant phenotyping, yet manual segmentation of plant organs remains a bottleneck in breeding programs. We developed a color‐based filtering workflow for faba bean (Vicia faba L.) point clouds that optimizes lower and upper thresholds of spectral indices and broadband ...
Lennart Scheer, Benjamin Wittkop, Andreas Stahl, Olaf Sass, Gregor Welna, Hanna Tietgen, Rod J. Snowdon   +6 more
wiley   +1 more source