Results 191 to 200 of about 17,773 (259)

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

Multiscale superpixel depth feature extraction for hyperspectral image classification. [PDF]

Sci Rep
Yan Q, Zhang S, Chen X, Zheng Z.
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 Change Detection Method Based on the Balanced Metric. [PDF]

Sensors (Basel)
Liang X, Li X, Wang Q, Qian J, Wang Y.
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

ARM-Net: A Tri-Phase Integrated Network for Hyperspectral Image Compression. [PDF]

Sensors (Basel)
Fang Q, Wang Z, Wang J, Zhang L.
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

CViT Weakly Supervised Network Fusing Dual-Branch Local-Global Features for Hyperspectral Image Classification. [PDF]

Entropy (Basel)
Fu W, Sun X, Zhang X, Ji Y, Zhang J.
europepmc   +1 more source

Bayesian optimized color filter: A fast method for segmentation of plant phenotypes from 3D point cloud images

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Multispectral three‐dimensional (3D) imaging offers substantial potential for plant phenotyping, yet manual segmentation of plant organs remains a bottleneck in breeding programs. We developed a color‐based filtering workflow for faba bean (Vicia faba L.) point clouds that optimizes lower and upper thresholds of spectral indices and broadband ...
Lennart Scheer, Benjamin Wittkop, Andreas Stahl, Olaf Sass, Gregor Welna, Hanna Tietgen, Rod J. Snowdon   +6 more
wiley   +1 more source

OHID-1: A New Large Hyperspectral Image Dataset for Multi-Classification. [PDF]

Sci Data
Mani A, Gorbachev S, Yan J, Dixit A, Shi X, Li L, Sun Y, Chen X, Wu J, Deng J, Jiang X, Yue D, Dou C, Wei X, Huang J.   +14 more
europepmc   +1 more source