Results 81 to 90 of about 4,378 (202)

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

Spectral Reflectance Modeling by Wavelength Selection: Studying the Scope for Blueberry Physiological Breeding under Contrasting Water Supply and Heat Conditions

Remote Sensing, 2019
To overcome the environmental changes occurring now and predicted for the future, it is essential that fruit breeders develop cultivars with better physiological performance.
Gustavo A. Lobos, Alejandro Escobar-Opazo, Félix Estrada, Sebastián Romero-Bravo, Miguel Garriga, Alejandro del Pozo, Carlos Poblete-Echeverría, Jaime Gonzalez-Talice, Luis González-Martinez, Peter Caligari   +9 more
doaj   +1 more source

Erratum: A perspective on plant phenomics: Coupling deep learning and near-infrared spectroscopy. [PDF]

Front Plant Sci, 2022
Frontiers Production Office.
europepmc   +1 more source

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

Optimizing Crop Production With Plant Phenomics Through High‐Throughput Phenotyping and AI in Controlled Environments

Food and Energy Security
Plant phenomics deals with the measurement of plant phenotypes associated with genetic and environmental variation in controlled environment agriculture (CEA).
Cengiz Kaya
doaj   +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

TraitCapture: genomic and environment modelling of plant phenomic data

Current Opinion in Plant Biology, 2014
Agriculture requires a second green revolution to provide increased food, fodder, fiber, fuel and soil fertility for a growing population while being more resilient to extreme weather on finite land, water, and nutrient resources. Advances in phenomics, genomics and environmental control/sensing can now be used to directly select yield and resilience ...
Brown, Tim B, Cheng, Riyan, Sirault, Xavier RR, Rungrat, Tepsuda, Murray, Kevin D, Trtilek, Martin, Furbank, Robert T, Badger, Murray, Pogson, Barry J, Borevitz, Justin O   +9 more
openaire   +3 more sources

Transdisciplinary Graduate Training in Predictive Plant Phenomics

Agronomy, 2018
Novel methods to increase crop productivity are required to meet anticipated demands for food, feed, fiber, and fuel. It is becoming feasible to use modern sensors and data analysis techniques for predicting plant growth and productivity based on genomic,
Carolyn J. Lawrence-Dill, Theodore J. Heindel, Patrick S. Schnable, Stephanie J. Strong, Jill Wittrock, Mary E. Losch, Julie A. Dickerson   +6 more
doaj   +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

A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy. [PDF]

Front Plant Sci, 2022
Vasseur F, Cornet D, Beurier G, Messier J, Rouan L, Bresson J, Ecarnot M, Stahl M, Heumos S, Gérard M, Reijnen H, Tillard P, Lacombe B, Emanuel A, Floret J, Estarague A, Przybylska S, Sartori K, Gillespie LM, Baron E, Kazakou E, Vile D, Violle C.   +22 more
europepmc   +1 more source