Results 221 to 230 of about 59,653 (294)

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

A Geospatial atlas of honey bee forage plants and their distribution patterns in Africa and beyond. [PDF]

Sci Rep
Nganso BT, Agboka KM, Atagong SD, Topé SF, Massing T, Landmann T, Sevgan S, Mwiza W, Odera F, Piiru ED, Otieno-Ayayo ZN, Soroker V, Guimapi RA.   +12 more
europepmc   +1 more source

High‐throughput phenotyping for the prediction and quantification of flower‐related traits in sugarcane

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Sugarcane (Saccharum spp.), a C4 plant, is a vital renewable biofuel and sugar source for industries worldwide. However, synchronizing flowering between parental lines often poses challenges for breeders, hindering effective crossbreeding efforts.
Paulo H. da Silva Santos, João R. Vieira Manechini, Carlos Augusto Dias Kanthack Jr., Mauro Alexandre Xavier, Samira Domingues Carlin, Dilermando Perecin, Thiago G. Marconi, Leandro G. Marconi, Elisson Antonio da Costa Romanel, Stephen Derek Jackson, Marcos. G. A. Landell, Luciana R. Pinto   +11 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

Integrated meteocean and seismic dataset for AI-based seawater CO₂ estimation at Deception Island, Antarctica. [PDF]

Sci Data
Flecha S, Amaya-Vías S, Medina M, Stips A, Fraile-Nuez E, Jiménez-Morales V, Contreras-Guijarro G, Díez-Minguito M, Caballero-Martínez OA, Navarro G, Tovar-Sanchez A, Huertas IE.   +11 more
europepmc   +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

The records from citizen science of an "endemic" agouti are not thoroughly evaluated nor examined. [PDF]

BMC Res Notes
Turcios-Casco MA, Schiavetti A, Teta P.
europepmc   +1 more source

Pixel‐level supervision resolves overlap: Benchmarking YOLOv12 segmentation for accurate multi‐cluster dry bean stand counting from time series unoccupied aerial systems imagery

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract An agronomic trait such as stand count is important for cultivar development and crop management practices. Manually counting the number of plants is time consuming, labor‐intensive, and prone to error. The use of unoccupied aerial systems (UAS)‐collected red, green, blue (RGB) imagery in conjunction with advanced deep learning and image ...
Aliasghar Bazrafkan, Valerio Hoyos‐Villegas   +1 more
wiley   +1 more source

Terrestrial and Airborne Laser Scanning Dataset of Trees in the Shivalik Range, India with Field Measurements and Leaf-Wood Classifications. [PDF]

Sci Data
Ali M, Biswas A, Iglseder A, Kumar V, Kumar S, Gupta S, Hollaus M, Pfeifer N, Lohani B.   +8 more
europepmc   +1 more source