Results 81 to 90 of about 760,293 (222)

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

Crop phenomics: Emerging tools for next-generation field crop improvement

Journal of Current Opinion in Crop Science
Crop phenomics has emerged as a transformative discipline that bridges genomics and agronomy by enabling precise, high-throughput, and non-destructive measurement of plant traits. Over the past decade, more than 50 advanced phenotyping platforms have been established globally, ranging from controlled-environment facilities to field-based systems ...
K Ashokkumar, M Gogulavasan, V Sharvesh Prabhu   +2 more
openaire   +1 more source

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

Affordable Phenomics special topic—Foreword for The Plant Phenome Journal

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract The Affordable Phenomics special topic in The Plant Phenome Journal showcased recent advances that expand the accessibility, cost‐effectiveness, and scalability of plant phenotyping technologies. This collection of 15 articles presented innovative approaches, ranging from low‐cost sensors and open‐source analytical pipelines to artificial ...
Valerio Hoyos‐Villegas, Michael A. Gore   +1 more
wiley   +1 more source

Integrated Crop Management Conference Provides Latest Research and Technologies to Benefit Your Operation [PDF]

, 2015
The annual Integrated Crop Management Conference will be held Dec. 2-3, 2015 at the Scheman Building on the Iowa State University campus. This year, the conference has added additional speakers to its program, providing topics and information on the ...
Integrated Crop Management News
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A highly accurate, low‐cost method for detecting and quantifying soybean leaf flipping phenotype during drought stress

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract A genome‐wide association study (GWAS) using digital images was conducted to delineate regions of the genome that govern the leaf flipping quantitative trait in soybean (Glycine max (L.) Merr). However, converting the digital data to numerical scores for downstream analyses was challenging.
Mohammad Anisur Rahaman, Atit Parajuli, Adnan Hossain Khan, Liang Dong, Madan K Bhattacharyya   +4 more
wiley   +1 more source

Scan-o-matic: High-Resolution Microbial Phenomics at a Massive Scale [PDF]

, 2016
The capacity to map traits over large cohorts of individuals—phenomics—lags far behind the explosive development in genomics. For microbes, the estimation of growth is the key phenotype because of its link to fitness.
Anders Blomberg, Luciano Fernandez-Ricaud, L\ue4ndstr\uf6m, Erik, Blomberg, Anders,, Martin Zackrisson, Dahl, Peter,, Ländström, Erik, Zackrisson, Martin, Ottosson, Lars-Göran, Fernandez-Ricaud, Luciano,, Omholt, Stig William, Erik Ländström, Petra Kaferle, Andreas Skyman, Dahl, Peter, Simon Stenberg, Uroš Petrovič, Warringer, Jonas, Warringer, Jonas,, Esteban Fernandez-Parada, Jonas Warringer, Ottoson, Lars-Göran, Omholt, Stig, Fernandez-Parada, Esteban, Ländström, Erik,, Skyman, Andreas, Peter Dahl, Ottosson, Lars-G\uf6ran, Skyman, Andreas,, Fernandez-Ricaud, Luciano, Lars-Göran Ottosson, Stig Omholt, Stenberg, Simon, Kaferle, Petra, Fernandez-Parada, Esteban,, Petrovic, Uros, Zackrisson, Martin,, Ottosson, Lars-Göran,, Johan Hallin, Hallin, Johan, Stenberg, Simon,, Blomberg, Anders, Hallin, Johan,, Omholt, Stig,, Petrovic, Uros,, Kaferle, Petra,   +45 more
core   +1 more source

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

Crop and Weather Update - First Week of October [PDF]

, 2009
ISU Extension climatologist Elwynn Taylor, economist Dan Otto, and soybean agronomist Palle Pedersen are guests this week for the crop and weather ...
Integrated Crop Management News
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