Results 161 to 170 of about 30,212 (267)

Low-cost embedded system for spectral power distribution reconstruction for controlled environmental agriculture using a multispectral sensor and cloud-based deep learning. [PDF]

HardwareX
Morales-Guerra J, Soto-Perdomo J, Botero-Valencia J, Reyes-Vera E, Pearce JM.   +4 more
europepmc   +1 more source

Energetic microdomains and the vascular control of neuronal and muscle excitability: Toward a unified model

The Journal of Physiology, EarlyView.
Abstract figure legend The capillary–mitochondria–ion channel (CMIC) axis scales structural resources to match functional workload. (Left) In settings of restricted energetic capacity (e.g. cortical neurons), sparse capillary networks and modest mitochondrial pools set a lower energetic ceiling, sufficient to support phasic, low‐workload excitability. (
L. Fernando Santana, Scott Earley
wiley   +1 more source

Monitoring phosphorus content in winter wheat using feature fusion and feature selection from UAV remote sensing imagery. [PDF]

Front Plant Sci
Su Y, Wang J, Yang Y, Yang J, Zhang J, Yang H, Duan Y, Han S, Yu K, Huang S, Jia L.   +10 more
europepmc   +1 more source

Temporal genetic relationships between growth, development, and malting quality in winter barley (Hordeum vulgare) using aerial imagery

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Grain characteristics are the cumulative product of growth and development throughout the growing season. In barley (Hordeum vulgare), these traits determine the grain's value for malting purposes. The ability to accurately predict the genetic merit for malting quality is of great interest for barley breeding programs. Same‐season selection on
Amelia Loeb, Felipe Sabadin, Alexis Perry, Harmonie Bettenhausen, Gina Brown‐Guedira, Jared Smith, Kimberly Howell, Jason Walling, Hannah Uhlmann, Wynse Brooks, Nicholas Santantonio   +10 more
wiley   +1 more source

Comprehensive uncrewed aerial system data for Amazon rainforest at Tiputini Biodiversity Station, Ecuador. [PDF]

Sci Data
Jung M, Chang A, Cannon CH, Rivas-Torres G, Jung J.   +4 more
europepmc   +1 more source

Utilizing high‐throughput phenotyping to identify metribuzin tolerance in winter wheat

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Plant breeders and weed scientists address weed management collaboratively by selecting for herbicide tolerance in breeding programs. Metribuzin, a Group 5 PSII‐inhibiting herbicide, is labeled for use in wheat (Triticum aestivum L.). However, application to currently available lines results in frequent, variable, and unpredictable crop injury.
Melinda Zubrod, Andrew W. Herr, Pia M. Spychalla, Ian C. Burke, Arron H. Carter   +4 more
wiley   +1 more source

Comprehensive and Region-Specific Retinal Health Assessment Using Phasor Analysis of Multispectral Images and Machine Learning. [PDF]

Sensors (Basel)
Eskandarinasab A, Rey-Barroso L, Burgos-Fernández FJ, Vilaseca M.   +3 more
europepmc   +1 more source

Robustness of high‐throughput prediction of leaf ecophysiological traits using near infrared spectroscopy and poro‐fluorometry

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Water scarcity is a major threat to crop production and quality. Improving drought tolerance through variety selection requires a deeper understanding of plant ecophysiological responses, but large‐scale phenotyping remains a bottleneck. This study assessed the potential of high‐throughput tools (spectroscopy and poro‐fluorometry) to predict ...
Eva Coindre, Romain Boulord, Laurine Chir, Virgilio Freitas, Maxime Ryckewaert, Thomas Laisné, Virginie Bouckenooghe, Maëlle Lis, Llorenç Cabrera‐Bosquet, Agnès Doligez, Thierry Simonneau, Benoît Pallas, Aude Coupel‐Ledru, Vincent Segura   +13 more
wiley   +1 more source

Computational Imaging Method for Thermal Infrared Hyperspectral Imaging Based on a Snapshot Divided-Aperture System. [PDF]

Sensors (Basel)
Ma T, He Z, Wu B, Lei Y, Wang Y, Liu X, Guo B, Lu J, Cheng B, Zan S, Li C, Yuan L.   +11 more
europepmc   +1 more source

Artificial intelligence‐powered plant phenomics: Progress, challenges, and opportunities

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Artificial intelligence (AI), a key driver of the Fourth Industrial Revolution, is being rapidly integrated into plant phenomics to automate sensing, accelerate data analysis, and support decision‐making in phenomic prediction and genomic selection.
Xu Wang, Daeun Choi, Damian Adams, Jaehyun Ahn, Kelly Balmant, Raquel Dias, Carlos D. Messina, Rafael Muñoz‐Carpena, Vance M. Whitaker, Haipeng Yu, Ziwen Yu, Chang Zhao, Changying Li   +12 more
wiley   +1 more source