Results 141 to 150 of about 2,542 (213)

Deep learning and computer vision for image‐based high‐throughput phenotyping of canning quality traits in dry beans

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Canning color retention is a key quality trait in dry bean (Phaseolus vulgaris L.) breeding, influencing consumer acceptance and commercial value. Public breeding programs maintain canning quality as a selection trait of importance, but existing color evaluation methods such as visual rating are subjective, while instrument colorimetry is ...
Lovepreet Singh, Om Sai Krishna Madhav Lella, Evan M. Wright, Karen Cichy, Valerio Hoyos‐Villegas   +4 more
wiley   +1 more source

Two-lens telecentric model eyes for image distortion measurement in adaptive optics ophthalmoscopes. [PDF]

Biomed Opt Express
Xia Y, Ayubi GA, Bentley J, Dubra A.
europepmc   +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

In vitro optical characterization of the RayOne Galaxy spiral extended depth-of-focus intraocular lens using high-resolution Mach-Zehnder interferometry. [PDF]

Biomed Opt Express
Gatinel D, Entin A, Stern B.
europepmc   +1 more source

Bayesian optimized color filter: A fast method for segmentation of plant phenotypes from 3D point cloud images

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Multispectral three‐dimensional (3D) imaging offers substantial potential for plant phenotyping, yet manual segmentation of plant organs remains a bottleneck in breeding programs. We developed a color‐based filtering workflow for faba bean (Vicia faba L.) point clouds that optimizes lower and upper thresholds of spectral indices and broadband ...
Lennart Scheer, Benjamin Wittkop, Andreas Stahl, Olaf Sass, Gregor Welna, Hanna Tietgen, Rod J. Snowdon   +6 more
wiley   +1 more source

Linear Variable Filter-Based Wide-Field Hyperspectral Camera for Multiplex Immunoplasmonics Imaging. [PDF]

J Biophotonics
Darviot C, Meunier M.
europepmc   +1 more source

High‐throughput unmanned aerial vehicle phenomics and machine learning enables accurate early single‐plant biomass prediction in lettuce

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Lettuce (Lactuca sativa L.) exhibits pronounced genotype and environment‐dependent growth variation, making early, non‐destructive single‐plant biomass prediction critical for breeding. However, harvest‐based measurements fail to capture growth dynamics.
Görkem Eren Özdemir, Ahmet Rıza Ağır, Burak Aktürk, Nida Selen Baysal, Adem Dogan, Aaron J. DeSalvio, Mustafa A. Arik, Yasin Topcu, Alper Adak   +8 more
wiley   +1 more source

Nature inspired design methodology for a wide field of view achromatic metalens. [PDF]

Nanophotonics
Engelberg J, Mazurski R, Levy U.
europepmc   +1 more source

NIRS Coupled With Machine Learning Algorithms for the Authentication and Quality Assessment of Indigenous Cattle and Buffalo Meat

eFood, Volume 7, Issue 5, October 2026.
Portable NIRS combined with machine learning enabled rapid and accurate authentication of cattle and buffalo meat. Optimized models (XGBoost, CNN) achieved > 98% accuracy, while SHAP revealed key spectral regions. This dual approach offers a field‐ready, reagent‐free tool for fraud prevention and traceability. ABSTRACT Ensuring the authenticity of high‐
Dip Ghosh, Raad Al Deen, Md. Abul Hashem
wiley   +1 more source

An achromatic neutron lens. [PDF]

Nat Commun
Dhanalakshmi Veeraraj MR, Qu D, Chen HY, Strebel S, Qi P, Fedrigo A, Helfen L, Tengattini A, Busi M, Wang H, Tranchida P, Kaestner A, David C, Strobl M, Vila-Comamala J.   +14 more
europepmc   +1 more source