Results 161 to 170 of about 12,889,375 (285)

Multi-Scale Semantic Selection and Spatial Constraint-Guided Network for Cross-Scene Hyperspectral Image Classification. [PDF]

Sensors (Basel)
Tang Y, Sun Y, Teng X, Yang C, Xie R, Guan X, Yu X.   +6 more
europepmc   +1 more source

UAV‐based RGB and multispectral vegetation indices as alternatives to light box‐derived dark green color index for turfgrass color assessment

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Traditionally, turfgrass color has been assessed through visual ratings or light box‐based digital image analysis, methods that are either subjective or labor‐intensive. In this study, we evaluated the potential of unmanned aerial vehicle (UAV)‐based multispectral and red‐green‐blue (RGB) imagery as a high‐throughput alternative for capturing ...
Ved Parkash, Phillip L. Vines, Sameer Khanal, Jing Zhang, Joann Conner, Susana R. Milla‐Lewis, Nino Brown, David Jespersen, Jodi Johnson‐Maynard, Brian M. Schwartz   +9 more
wiley   +1 more source

A multiscale transformer with spatial attention for hyperspectral image classification. [PDF]

Sci Rep
Ahmad I, Farooque G, Hadi F, Khader A, Abdelwahab Ghorashi S, Ahmed A, Alabdulkreem E.   +6 more
europepmc   +1 more source

Unmanned aerial vehicle–based spatiotemporal phenotyping and growth modeling for forecasting potato yield

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Monitoring spatial variations in plant growth and forecasting yield before harvest provides valuable insights for optimizing agronomic decision‐making in potato (Solanum tuberosum L.) cultivation. Although unmanned aerial vehicle (UAV)‐based remote sensing has recently enabled the development of tuber fresh weight (TW) estimation models, their
Yuto Imachi, Kunihiro Tanaka, Minato Miyauchi, Kyosuke Miyamoto, Masahiro Okada, Pieter M. Blok, Wei Guo, Hiroyoshi Iwata   +7 more
wiley   +1 more source

HL-Mamba: A High-Low Frequency Interaction Mamba Network for Hyperspectral Image Classification. [PDF]

Sensors (Basel)
Teng Y, Gan S, Yuan X.
europepmc   +1 more source

Drone‐based phenotyping of maize for multiple disease resistance and yield in breeding field trials

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Improving selection for multiple disease resistance (MDR) and yield in maize (Zea mays L.) requires high‐throughput, objective phenotyping tools, particularly under field conditions where several foliar diseases co‐occur. We evaluated drone‐based multispectral vegetation indices (VIs) for predicting resistance to northern leaf blight (NLB ...
Danilo E. Moreta, Nina Blahut, Nicholas Kaczmar, Judith M. Kolkman, Gary Bergstrom, Margaret Smith, Michael A. Gore, Rebecca Nelson   +7 more
wiley   +1 more source

Hyperspectral image classification network based on multiscale spatial-spectral fusion and semantic enhancement encoder. [PDF]

Sci Rep
Yu J, Tang B, Zha C, Jiang N, Liu R, Zeng T, Ye Z, Feng F, Zhuang C.   +8 more
europepmc   +1 more source

BerryBox: An affordable computer vision system for postharvest phenotyping of cranberry and other small fruits

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Fruit size, shape, color, and percent fruit rot are important quality traits for breeding cranberry (Vaccinium macrocarpon Ait.). Image analysis can be used to measure these traits, but affordable hardware for standardized image capture and integrated user‐friendly software pipelines are lacking.
Jeffrey L. Neyhart, Collins Wakholi, Devin A. Rippner, John H. Price, Kayla R. Altendorf, Garett C. Heineck, Juan Zalapa, Jenyne Loarca, Gina M. Sideli   +8 more
wiley   +1 more source

Superpixel Random Selection Random Walk Multi-Branch Depthwise Convolutional Neural Network for Hyperspectral Image Classification. [PDF]

Sensors (Basel)
Zhang K, Jiang X, Cai Z.
europepmc   +1 more source

Philosophy of phenomic prediction and its incompatibility with causal inference

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Breeding programs need to make decisions frequently to improve populations and develop varieties efficiently. These needs led to the development of genomic prediction in the early 2000s and phenomic prediction in the mid‐2010s. In practice, phenomic prediction techniques rely on the same statistical tools and computational frameworks as other ...
Mitchell J. Feldmann, Fangyi Wang, Daniel E. Runcie   +2 more
wiley   +1 more source