Results 101 to 110 of about 3,348 (213)

Spatial and temporal scales in plant phenotyping for crop water stress assessment: A review

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Water stress is a major limiting factor for crop productivity worldwide, and its impacts are intensifying due to climate variability and increasing water scarcity. This review focuses on the spatial and temporal scales in plant phenotyping as a critical approach to improving crop water‐stress assessment and supporting precision water ...
Daniel Kingsley Cudjoe, Yazhou Sun, Sanai Li, Wenxuan Guo   +3 more
wiley   +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

The crop QTLome comes of age [PDF]

, 2015
Recent progress in genomics and phenomics allows for a more accurate and comprehensive characterization of the Quantitative Trait Loci (QTLs) hereafter defined 'QTLome' as a whole that govern the variation targeted in breeding programs.
TUBEROSA, ROBERTO, SALVI, SILVIO
core   +1 more source

Combining phenomic and genomic selection for pea breeding improvement

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Pea (Pisum sativum L.) is a strategic crop in the development of sustainable agriculture. However, the genetic gain remains limited despite advances in breeding. Genomic selection holds promise to accelerate varietal improvement, but its high implementation cost restricts its use in crops.
Anthony Klein, Virgilio Freitas, Abdou Wade, Grégoire Aubert, Myriam Naudet‐Huart, Michael Touratier, Jennifer Régulier, Jean‐François Herbommez, Catherine Kraut, Guillaume Roullet, Aude Carlier‐Lézie, Nadim Tayeh, Renaud Rincent, Philippe Dufour, Judith Burstin, Gaëtan Touzy   +15 more
wiley   +1 more source

Unmanned aerial vehicle–based spatiotemporal phenotyping and growth modeling for forecasting potato yield

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Monitoring spatial variations in plant growth and forecasting yield before harvest provides valuable insights for optimizing agronomic decision‐making in potato (Solanum tuberosum L.) cultivation. Although unmanned aerial vehicle (UAV)‐based remote sensing has recently enabled the development of tuber fresh weight (TW) estimation models, their
Yuto Imachi, Kunihiro Tanaka, Minato Miyauchi, Kyosuke Miyamoto, Masahiro Okada, Pieter M. Blok, Wei Guo, Hiroyoshi Iwata   +7 more
wiley   +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

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

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

Bridging affordable phenomics with high-efficiency controlled environment agriculture for data-driven agriculture

Frontiers in Agronomy
Controlled environment agriculture (CEA) is essential for resilient crop production but faces high energy demands and operational costs. While high-throughput phenotyping (HTP) provides critical biological feedback to optimize these systems, conventional
Hsing-Ying Chung, Yuan-Kai Tu, Han-Wei Chen, Ching-Shan Tseng, Daisuke Sugiura   +4 more
doaj   +1 more source