Results 101 to 110 of about 2,457,508 (205)

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

Comparative conventional and phenomics approaches to assess symbiotic effectiveness of Bradyrhizobia strains in soybean (Glycine max L. Merrill) to drought

Scientific Reports, 2017
Symbiotic effectiveness of rhizobitoxine (Rtx)-producing strains of Bradyrhizobium spp. in soybean (cultivar NRC-37/Ahilya-4) under limited soil moisture conditions was evaluated using phenomics tools such as infrared(IR) thermal and visible imaging. Red,
Venkadasamy Govindasamy, Priya George, Lalitkumar Aher, Shunmugiah V. Ramesh, Arunachalam Thangasamy, Sivalingam Anandan, Susheel Kumar Raina, Mahesh Kumar, Jagadish Rane, Kannepalli Annapurna, Paramjit Singh Minhas   +10 more
doaj   +1 more source

Model-based plant phenomics on morphological traits using morphometric descriptors. [PDF]

Breed Sci, 2022
Noshita K, Murata H, Kirie S.
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

Zegami - Plant Phenomics Data Visualisation Tool

<p>The Adelaide node of the Australian Plant Phenomics Facility, The Plant Accelerator, measures the growth and performance of hundreds of plants using automation and digital imaging, referred to as plant phenomics. To facilitate the exploration of

core  

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

Phenomics: How next-generation phenotyping is revolutionizing plant breeding

, 2015
This book represents a pioneer initiative to describe the new technologies available for next-generation phenotyping and applied to plant breeding. Over the last several years plant breeding has experienced a true revolution.
Fritsche-Neto, Roberto, Borém, Aluízio
core   +1 more source

Leveraging sensor technologies for seed phenotyping by genebanks

Frontiers in Plant Science
Genebanks serve as critical repositories for preserving the genetic diversity of plant species, including crops, forages, and their wild relatives, which is essential for adapting to climate change, enhancing food security, and improving agricultural ...
Kioumars Ghamkhar, David Rousseau
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

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

Plant Cell, 2021
Hüther P, Schandry N, Jandrasits K, Bezrukov I, Becker C.   +4 more
europepmc   +1 more source