Results 211 to 220 of about 5,351 (266)

Spatial and temporal scales in plant phenotyping for crop water stress assessment: A review

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Water stress is a major limiting factor for crop productivity worldwide, and its impacts are intensifying due to climate variability and increasing water scarcity. This review focuses on the spatial and temporal scales in plant phenotyping as a critical approach to improving crop water‐stress assessment and supporting precision water ...
Daniel Kingsley Cudjoe, Yazhou Sun, Sanai Li, Wenxuan Guo   +3 more
wiley   +1 more source

Insights into biomass accumulation and challenges in grain yield prediction of elite breeding materials using UAV‐based vegetation indices in soft red winter wheat

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract High‐throughput phenotyping (HTP) techniques have brought new opportunities to understand and evaluate key traits in plant breeding programs. Combining multiple measures through time and random regression models permits a more comprehensive understanding of the genetic and environmental effects on trait expression over time. This study aims to
Felipe Sabadin, Amelia Loeb, Francis Reith, Alexis Perry, Sunilda Frias, Demetrius Ragland, Limei Liu, Wynse Brooks, Ranjita Thapa, Julie Hansen, Christopher Pierce, Virginia M. Moore, Gina Brown‐Guedira, Ian Ray, Michael A. Gore, Kelly Robbins, Nicholas Santantonio   +16 more
wiley   +1 more source

From Detection toward Mitigation: A Critical Review of Artificial Intelligence and Machine Learning for Methane Emissions Reduction. [PDF]

Environ Sci Technol
Luo R, Gates I.
europepmc   +1 more source

Growth-stage deep learning for UAV hyperspectral estimation of leaf nitrogen content in winter wheat. [PDF]

Plant Phenomics
Zhao S, Song C, Zhang B, Liu X, Zong F, Zhai S, Liu X, Chen L, Gu X.   +8 more
europepmc   +1 more source

Hyperspectral Imaging for Agricultural and Food Sensing: Applications, Data Analytics, and Deployment Challenges. [PDF]

Foods
Wen D, Dong Y, Hu L, Cao D, Li J.
europepmc   +1 more source

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Band Sampling for Hyperspectral Imagery

IEEE Transactions on Geoscience and Remote Sensing, 2022
Band sampling (BSam) is an innovative concept for hyperspectral imaging, which is derived from signal sampling in communications/signal processing as well as sampling theory in information theory. It is quite different from band selection (BSel) in several aspects.
openaire   +2 more sources

Compression of hyperspectral imagery

Data Compression Conference, 2003. Proceedings. DCC 2003, 2003
High dimensional source vectors, such as those that occur in hyperspectral imagery, are partitioned into a number of subvectors of different length and then each subvector is vector quantized (VQ) individually with an appropriate codebook. A locally adaptive partitioning algorithm is introduced that performs comparably in this application to a more ...
Giovanni Motta, Francesco Rizzo, James A. Storer   +2 more
openaire   +1 more source

Compressed hyperspectral imagery for forestry

IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477), 2004
Various compression schemes have been suggested for storage and distribution of hyperspectral remotely sensed data. Hyperspectral forestry applications that rely on the measurement of subtle variations in the spectral signature of the forest canopy can be affected by modification of the spectra induced by compression.
Andrew Dyk, David G. Goodenough, Suzanne Thompson, Christian Nadeau, Allan Hollinger, Shen-En Qian   +5 more
openaire   +1 more source

Modeling and detection in hyperspectral imagery

Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181), 2002
One aim of using hyperspectral imaging sensors is in discriminating man-made objects from dominant clutter environments. Sensors like Aviris or Hydice simultaneously collect hundreds of contiguous and narrowly spaced spectral band images for the same scene. The challenge lies in processing the corresponding large volume of data that is collected by the
Susan M. Schweizer, José M. F. Moura
openaire   +1 more source

Interest segmentation of hyperspectral imagery

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2010
In recent years, many new methods for analyzing spectral imagery have been introduced. These new methods have been developed to improve the analysis of hyperspectral imagery. Many of these techniques are data driven anomaly/target detection and spectral clustering algorithms which are used to decide whether a particular pixel or area is “interesting ...
Ariel Schlamm, David W. Messinger, William Basener   +2 more
openaire   +2 more sources