Results 231 to 240 of about 204,035 (336)

Hyperspectral Imaging for the Classification and Analysis of Drusen in Age-Related Macular Degeneration. [PDF]

Invest Ophthalmol Vis Sci
Chen C, Razavi R, Romano F, Niessen H, Sabokrohiyeh S, Konda A, Chevrefils C, Arbour JD, Rhéaume MA, Nissan R, Rojewski A, Sorya P, Santucci L, Campbell S, Ploumi I, Zhu Y, Ding X, Sylvestre JP, Miller JB, Nassar K.   +19 more
europepmc   +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

Early Detection and Classification of Gibberella Zeae Contamination in Maize Kernels Using SWIR Hyperspectral Imaging and Machine Learning. [PDF]

Sensors (Basel)
Liu K, Li S, Shi W, Guo Z, Shao X, Guo Y, Zhao J, Sun X, Khujamshukurov NA, Du F.   +9 more
europepmc   +1 more source

Target identification with hyperspectral lidar

, 2015
Matleena Myntti
openalex   +1 more source

A systematic color correction pipeline for controlled‐environment imaging

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
ABSTRACT We present a stepwise color correction (CC) pipeline for controlled imaging environments. The workflow integrates flat‐field correction (FFC), gamma correction, and white‐balance correction, followed by a color‐mapping (CM) stage using machine‐learning regression—linear, partial least squares, and neural networks (NNs)—to deliver reliable CC ...
Collins Wakholi, Michael A. Hardigan, Jungmin Lee, Scott B. Lukas, Max J. Feldman, Kayla R. Altendorf, Jeffrey L. Neyhart, Devin A. Rippner   +7 more
wiley   +1 more source

Analysis of Intracellular Fatty Acid Metabolism during Doxorubicin-Induced Senescence of MCF7 Cells Using Raman Imaging. [PDF]

ACS Omega
Pundlik SS, Venkateshvaran A, Suresh Y, Mamgain H, Ramanathan A.   +4 more
europepmc   +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

HMI-LUSC: A Histological Hyperspectral Imaging Dataset for Lung Squamous Cell Carcinoma. [PDF]

Sci Data
Yan Z, Huang H, Guo Y, Shi J, Geng R, Zhang J, Chen Y, Nie Y.   +7 more
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

Correction: Hyperspectral technology and machine learning models to estimate the fruit quality parameters of mango and strawberry crops. [PDF]

PLoS One
Elsayed S, Galal H, Abd El-Baki MS, Ibrahim MM, Elbeltagi A, Salem A, Elwakeel AE, Elsherbiny O, Abd El-Fattah NG.   +8 more
europepmc   +1 more source