Results 91 to 100 of about 1,165,106 (235)

FieldDino: Rapid In‐Field Stomatal Anatomy and Physiology Phenotyping

Plant, Cell &Environment, EarlyView.
ABSTRACT Stomatal anatomy and physiology define CO2 availability for photosynthesis and regulate plant water use. Despite being key drivers of yield and dynamic responsiveness to abiotic stresses, conventional measurement techniques of stomatal traits are laborious and slow, limiting adoption in plant breeding.
Edward Chaplin, Guy Coleman, Andrew Merchant, William Salter   +3 more
wiley   +1 more source

Time‐series multi‐omics analysis of micronutrient stress in Sorghum bicolor reveals iron and zinc crosstalk and regulatory network conservation

Plant Biology, EarlyView.
Overlap between Fe and Zn responsive gene regulatory networks (GRNs) were found, indicative of micronutrient crosstalk, and conservation of root and leaf GRNs and genes suggests strong constraint on homeostasis networks in plants. Abstract Micronutrient stress impacts growth, biomass production, and grain yield in crops.
A. Mishra, A. Bhat, S. Kumari, R. Sharma, J. Braynen, D. Tadesse, S. El Alaoui, S. Seaver, N. Grosjean, D. Ware, M. Xie, T. Paape   +11 more
wiley   +1 more source

Study on advancement in bioinformatics for crop improvement: integrating genomics, phenomics, and machine learning

BOHR Journal of Biocomputing and Nano Technology
Bioinformatics has emerged as a vital tool in agriculture, performing an important role in crop improvement and precision agriculture. This review study explores the applications of bioinformatics in these areas, highlighting the importance of next ...
Avil Alva, N. Hemalatha
semanticscholar   +1 more source

Artificial intelligence‐powered plant phenomics: Progress, challenges, and opportunities

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Artificial intelligence (AI), a key driver of the Fourth Industrial Revolution, is being rapidly integrated into plant phenomics to automate sensing, accelerate data analysis, and support decision‐making in phenomic prediction and genomic selection.
Xu Wang, Daeun Choi, Damian Adams, Jaehyun Ahn, Kelly Balmant, Raquel Dias, Carlos D. Messina, Rafael Muñoz‐Carpena, Vance M. Whitaker, Haipeng Yu, Ziwen Yu, Chang Zhao, Changying Li   +12 more
wiley   +1 more source

Root and floral effects of flavonoid transport via an Arabidopsis MATE family transporter [PDF]

, 2014
Amongst their numerous roles in plants, flavonoid pigments are hypothesised to affect growth and development via interactions with components of auxin transport networks.
Ferrara, Billy, Mogg, Robert, Stevenson, Philip, Thompson, Elinor   +3 more
core  

Allele specific expression analysis identifies regulatory variation associated with stress-related genes in the Mexican highland maize landrace Palomero Toluqueño. [PDF]

, 2017
BackgroundGene regulatory variation has been proposed to play an important role in the adaptation of plants to environmental stress. In the central highlands of Mexico, farmer selection has generated a unique group of maize landraces adapted to the ...
Aguilar-Rangel, M Rocío, Chávez Montes, Ricardo A, González-Segovia, Eric, Ross-Ibarra, Jeffrey, Sawers, Ruairidh JH, Simpson, June K   +5 more
core   +2 more sources

Multiple ortho‐mosaicking software pipelines produce comparable imagery‐derived wheat phenotypes

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Unmanned aerial systems (UAS) equipped with multispectral and RGB sensors offer valuable data for monitoring crop health and assessing disease severity. However, the wide range of available photogrammetric software complicates software selection for high‐throughput plant phenotyping.
Sanju Shrestha, Phillip D. Alderman, Grishma Ojha, Brett F. Carver   +3 more
wiley   +1 more source

Measuring the dynamic photosynthome [PDF]

, 2018
Background: Photosynthesis underpins plant productivity and yet is notoriously sensitive to small changes inenvironmental conditions, meaning that quantitation in nature across different time scales is not straightforward.
Alonso, Ananyev, Araus, Athanasiou, Ač, Baker, Barrada, Berra, Bulanon, Burgess, Burgess, Busemeyer, Cabrera-Bosquet, Cabrera-Bosquet, Carmo-Silva, Carter, Casadesús, Casadesús, Challinor, Chazdon, Chazdon, Cogliati, Cointault, Coupe, Dhondt, Driever, Drusch, Elliott-Kingston, Erik H Murchie, Farquhar, Filella, Fiorani, Fiorani, Flood, Flood, Flood, Frankenberg, Furbank, Gamon, Gamon, Gibbs, Gitelson, Gitelson, Godin, Godin, Gonzalez-Dugo, Gracia-Romero, Grant, Großkinsky, Guanter, Guanter, Guilioni, Hammer, Hancock, Hashimoto, Hawkesford, Hedberg, Hirose, Hubbart, Hunt, Hunt, James, Johnson, Joiner, Joiner, Jones, Jones, Jones, Jones, Jose Luis Araus, Kaiser, Kaiser, Kefauver, Kefauver, Kira, Kirchgessner, Kirschbaum, Kolber, Kolber, Kromdijk, Kuhlgert, Lawson, Lawson, Lawson, Lawson, Leakey, Lebourgeois, Lee, Leinonen, Li, Lobos, Long, Lopes, Malenovský, Matthews, Maxwell, McAusland, McAusland, McAusland, Merlot, Meroni, Meroni, Migliavacca, Mott, Moya, Muller, Murchie, Murchie, Murchie, Nevado, Omasa, Onno Muller, Ort, Osmond, Patel, Paul, Paulus, Pearcy, Pieruschka, Pieruschka, Pieruschka, Pinto, Plascyk, Poorter, Poorter, Porcar-Castell, Porcar-Castell, Pound, Prasanna, Pádraic J Flood, Quan, Rabatel, Raesch, Rascher, Rascher, Rascher, Rasmussen, Ray, Rossini, Rossini, Sanchez-Bragado, Schreiber, Schurr, Shawn Kefauver, Simmer, Sinclair, Smith, Song, Song, Song, Tambussi, Tambussi, Tattaris, Tester, Tietz, Tinoco-Ojanguren, Townsend, Tracy Lawson, Ustin, Uwe Rascher, Vergara-Diaz, Vialet-Chabrand, Virlet, Vogelmann, Walker, Wang, Wang, Wang, Watanabe, White, Wieneke, Winterhalter, Wohlfahrt, Wyber, Xie, Yang, Yano, Zarco-Tejada, Zhou, Zhu, Zhu, Zhu, Zhu, Zhu   +183 more
core   +5 more sources

PlantCV v4: Image analysis software for high‐throughput plant phenotyping

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract PlantCV is an open‐source Python project aimed at developing tools to address a range of image‐based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data, and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding ...
Haley Schuhl, Keely E. Brown, Hudanyun Sheng, Parag K. Bhatt, Jorge Gutierrez, Dominik Schneider, Anna L. Casto, Lucia Acosta‐Gamboa, Joe G. Ballenger, Fabio Barbero, Jackson Braley, Autumn M. Brown, Leonardo Chavez, Shannon Cunningham, Malinda Dilhara, Adam M. Dimech, Joseph G. Duenwald, Annika Fischer, Jared M. Gordon, Chloe Hendrikse, Gabriela L. Hernandez, John G. Hodge, Martina Huber, Brandon M. Hurr, Sanaz Jarolmasjed, Karina Medina Jimenez, Samuel Kenney, Grant Konkel, Alexander Kutschera, Sunita Lama, Matthew Lohbihler, Argelia Lorence, Collin Luebbert, Nathaniel Ly, Heather K. Manching, Annarita Marrano, Susan Meerdink, Nicholas M. Miklave, Pavan Mudrageda, Katherine M. Murphy, J. David Peery, Ronald Pierik, Seth Polydore, Caleb Robey, Tess Rogers, Tyler J. Schultz, Eliza Seigel, Dhiraj Srivastava, Stephan Summerer, Josh Sumner, Chong Teng, Adriane E. Thompson, Jose C. Tovar, Tim van Daalen, Mark Watson, John J. Wheeler, Mark C. Wilson, Kaitlyn R. Ying, Alina Zare, Yutai Zhou, Malia A. Gehan, Noah Fahlgren   +61 more
wiley   +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