Results 231 to 240 of about 3,522,681 (315)

PhenoGazer: A high-throughput phenotyping system to track plant stress responses using hyperspectral reflectance, nighttime chlorophyll fluorescence and RGB imaging in controlled environments. [PDF]

Plant Phenomics
Hassan MA, Chang CY.
europepmc   +1 more source

Hyperspectral Reflectance Response of Wild Rocket (Diplotaxis tenuifolia) Baby-Leaf to Bio-Based Disease Resistance Inducers Using a Linear Mixed Effect Model. [PDF]

Plants (Basel), 2021
Pane C, Galieni A, Riefolo C, Nicastro N, Castrignanò A, Castrignanò A.   +5 more
europepmc   +1 more source

KPI‐NeRF: Hyperspectral Neural Radiance Fields from a Single Kaleidoscopic Plenoptic Image

Computer Graphics Forum, EarlyView.
KPI‐NeRF reconstructs hyperspectral light field images from a single kaleidoscopic plenoptic image. By leveraging neural radiance fields, it enables high‐quality spectral and angular de‐multiplexing for dynamic scene capture. Abstract Plenoptic imaging has excessive sampling requirements associated with the high dimensionality of the desired data.
Erqi Huang, John Restrepo, Xun Cao, Ivo Ihrke   +3 more
wiley   +1 more source

Hyperspectral reflectance imaging for visualizing reducing sugar content, moisture, and hollow rate in red ginseng. [PDF]

Heliyon
Bai X, You Y, Wang H, Zhao D, Wang J, Zhang W.   +5 more
europepmc   +1 more source

A novel method based on high‐resolution imaging spectroscopy to predict fine‐root traits and the economics space of fresh tree roots

New Phytologist, EarlyView.
Maps of the estimated root nitrogen concentration in the < 0.5‐mm‐diameter classes of 20 woody species based on the imaging spectroscopy using the shortwave infrared spectral region. Summary Tree fine‐root morphological, anatomical, and chemical traits are important to reflect belowground resource acquisition strategies to support tree growth, but ...
Naoki Makita, Natsuko Tanikawa, Tatsuro Nakaji   +2 more
wiley   +1 more source

Phenotyping cotton leaf chlorophyll via <i>in situ</i> hyperspectral reflectance sensing, spectral vegetation indices, and machine learning. [PDF]

Front Plant Sci
Thorp KR, Thompson AL, Herritt MT.
europepmc   +1 more source

Harnessing genetic variation for grain quality from diverse wheat landraces

New Phytologist, EarlyView.
Grain protein content is usually measured on bulk samples, hiding variation between individual grains. We used hyperspectral imaging to predict protein in c. 15 000 single grains from over 500 Watkins wheat landraces grown in two environments, resolving both mean protein content and grain‐to‐grain uniformity.
Luqman B. Safdar, M. John Foulkes, Alix Cornish, Elizabeth D. Goodman, Andrew B. Riche, David Steele, Charlie Philp, Shiva Rahimitanha, Martin Whitworth, Iain R. Searle, Matt Kerton, Malcolm J. Hawkesford, Simon Griffiths, Rahul A. Bhosale, Scott A. Boden, Ian D. Fisk   +15 more
wiley   +1 more source

Non-destructive estimation of needle leaf chlorophyll and water contents in Chinese fir seedlings based on hyperspectral reflectance spectra. [PDF]

For Res (Fayettev)
Xing D, Sun P, Wang Y, Jiang M, Miao S, Liu W, Huang H, Lin E.   +7 more
europepmc   +1 more source

MicroRNA 160 controls the auxin response factor–class III peroxidase module and couples salicylic acid and redox signaling networks in the potato hypersensitive response to potato virus Y

New Phytologist, EarlyView.
miR160 links salicylic acid, auxin, and redox signaling with the growth–defense trade‐off in the potato hypersensitive response to potato virus Y. Summary Hypersensitive response (HR) is an effector‐triggered immune response leading to pathogen restriction and local cell death.
Maja Križnik, Tjaša Lukan, Maja Zagorščak, Anna Coll Rius, Karmen Pogačar, Nastja Marondini, Katja Stare, Valentina Levak, Jitka Široká, Ondřej Novák, Aleš Kladnik, Kristina Gruden   +11 more
wiley   +1 more source

Rapid estimation of soil water content based on hyperspectral reflectance combined with continuous wavelet transform, feature extraction, and extreme learning machine. [PDF]

PeerJ
Chen S, Gao J, Lou F, Tuo Y, Tan S, Shan Y, Luo L, Xu Z, Zhang Z, Huang X.   +9 more
europepmc   +1 more source