Results 211 to 220 of about 75,553 (263)

SUnSeT: spectral unmixing of hyperspectral images for phenotyping soybean seed traits. [PDF]

Plant Cell Rep
Jeong SW, Lyu JI, Jeong H, Baek J, Moon JK, Lee C, Choi MG, Kim KH, Park YI.   +8 more
europepmc   +1 more source

Demystifying Piecewise and Localized Scatter Correction Methods

Journal of Chemometrics, Volume 40, Issue 3, March 2026.
ABSTRACT Multiplicative scatter is a common source of noise in near‐infrared spectroscopy and other related instrumental techniques. A wide variety of methods are commonly used for the correction of multiplicative scatter. However, the majority of such methods assume that the parameters that describe the scatter are constant throughout the measured ...
Cannon Giglio, Jean‐Michel Roger, Erik Andries   +2 more
wiley   +1 more source

Virtual cleaning of sooty mural hyperspectral images using the LIME model and improved dark channel prior. [PDF]

Sci Rep
Sun P, Hou M, Lyu S, Li S, Wang W, Cheng C, Zhang T.   +6 more
europepmc   +1 more source

Broadening the Workflow for Synchrotron‐Based X‐Ray Fluorescence and X‐Ray Absorption Spectroscopy Imaging of Low‐Abundance Cellular Metals

Chemistry–Methods, Volume 6, Issue 3, March 2026.
We present an accessible plunge‐freezing and freeze‐drying workflow for preparing adherent mammalian cells for synchrotron‐based nanofocused X‐ray fluorescence (XRF) microscopy and X‐ray absorption spectroscopy (XAS), enabling sub‐micron mapping of metal distribution and chemical speciation.
Joaquin Tamargo‐Azpilicueta, Rafael L. Giner‐Arroyo, Pablo Rivero‐García, Sofia Diaz‐Moreno, Miguel A. Gomez‐Gonzalez, Abbey Telfer, Michelangelo Campanella, Miguel A. De la Rosa, Irene Díaz‐Moreno   +8 more
wiley   +1 more source

Estimation of potato leaf area index based on spectral information and Haralick textures from UAV hyperspectral images. [PDF]

Front Plant Sci
Fan J, Liu Y, Fan Y, Yao Y, Chen R, Bian M, Ma Y, Wang H, Feng H.   +8 more
europepmc   +1 more source

The progressive modernization of cassava breeding programs at International Institute of Tropical Agriculture and future prospects

Crop Science, Volume 66, Issue 2, March/April 2026.
Abstract Modernizing cassava breeding programs (CBPs) is critical to increase genetic gain, close the yield gap, and ensure food availability, accessibility, and affordability to support livelihood transformation and economic development in sub‐Saharan Africa (SSA).
Edwige Gaby Nkouaya Mbanjo, Afolabi Agbona, John Derera, Alick Mulenga, Ibnou Dieng, Béla Teeken, Hapson Mushoriwa, Jim Lorenzen, Chiedozie Egesi, Peter Kulakow, Ismail Yusuf Rabbi   +10 more
wiley   +1 more source

Efficient residual network using hyperspectral images for corn variety identification. [PDF]

Front Plant Sci
Li X, Zhai M, Zheng L, Zhou L, Xie X, Zhao W, Zhang W.   +6 more
europepmc   +1 more source

Drone‐imaging assisted genome‐wide association studies reveal key quantitative trait loci for emergence and late blight resistance in tetraploid potato

Crop Science, Volume 66, Issue 2, March/April 2026.
Abstract Solanum tuberosum L. (potato) is the world's most important vegetable crop, and developing improved cultivars is paramount for global food security. The efficacy of the genomic prediction models that accelerate breeding and genome‐wide association studies (GWAS) depends on large, high‐quality phenotypic datasets, which are often associated ...
Trine Aalborg, Mathias Timmermann Christiansen, Hélène Romé, Aakash Chawade, Merethe Bagge, Kåre Lehmann Nielsen   +5 more
wiley   +1 more source

Lossless and Near-Lossless Compression Algorithms for Remotely Sensed Hyperspectral Images. [PDF]

Entropy (Basel)
Altamimi A, Ben Youssef B.
europepmc   +1 more source

Estimating Plant Species Richness With Sentinel and Landsat Data Across Ecosystems in China

Ecology and Evolution, Volume 16, Issue 3, -Not available-.
438 field plots were used to estimate plant diversity across ecosystems; 18 spectral indices were derived from Sentinel and Landsat data; EVI, DVI, PSRI, NDVI, PRI, GNDVI, and GMEVI were identified as powerful indicators for predicting plant alpha diversity.
Keman Wang, Yu Peng, Ying Ye, Yue Qi, Lizheng He   +4 more
wiley   +1 more source