Results 71 to 80 of about 9,159 (181)

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

Critical Evaluation of the Cgrain Value™ as a Tool for Rapid Morphometric Phenotyping of Husked Oat (Avena sativa L.) Grains

Seeds
Mechanised non-contact, non-destructive imaging methodologies have revolutionised plant phenotyping, increasing throughput well beyond what was possible using traditional manual methods.
David Evershed, Eamon J. Durkan, Rachel Hasler, Fiona Corke, John H. Doonan, Catherine J. Howarth   +5 more
doaj   +1 more source

Phenotype imputation using high‐throughput phenotyping produces a new secondary trait for further selection modeling

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Data from high‐throughput phenotyping (HTP) could be used for phenotype imputation to enhance genomic selection (GS) or gene discovery, but this has not been explored in crop species. Three machine learning models: multiple linear regression (MLR), missForest, and k‐nearest neighbors, were evaluated for grain yield (GY) phenotype imputation in
Raysa Gevartosky, Lucas Berger Munaro, Jessica E. Rutkoski   +2 more
wiley   +1 more source

UAV‐based deep transfer learning to improve grain yield prediction in winter wheat across temporal and spatial variability

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Accurate prediction of grain yield (GY) remains a major challenge in plant breeding due to complex interactions between genotype, environment, and management (G × E × M) factors. Remote sensing data from unmanned aerial vehicles (UAVs) equipped with multispectral sensors have emerged as a pivotal resource for high‐throughput phenotyping.
Swas Kaushal, Mohammad Maruf Billah, Mandeep Singh, Dante Koupal, Pradeep Raj Sitaula, Jyotirmoy Halder, Jagdeep Sidhu, Maitiniyazi Maimaitijiang, Sunish K. Sehgal   +8 more
wiley   +1 more source

Costs of photosynthesis and cellular remodeling in trophic transitions of the unicellular red alga Galdieria partita

Communications Biology
As in plastid differentiation in land plants, some unicellular algae reversibly remodel photosynthetic plastids into a colorless heterotrophic state (bleaching) in the presence of organic carbon sources.
Shota Yamashita, Shunsuke Hirooka, Takayuki Fujiwara, Baifeng Zhou, Fumi Yagisawa, Kei Tamashiro, Hiroki Murakami, Koichiro Awai, Shin-ya Miyagishima   +8 more
doaj   +1 more source

High‐throughput image‐based phenotyping of soybean hilum color

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Hilum color in soybean (Glycine max [L.] Merr.) is an important morphological trait influencing market classification and seed quality traits, yet its phenotyping is largely subjective, relying on visual inspection and assignment to one of eight color classes. This study developed an image‐based high‐throughput pipeline to measure and classify
Katherine Fortune, Ali Heidari, Milad Eskandari   +2 more
wiley   +1 more source

Electrical stimulation of the cuneiform nucleus enhances the effects of rehabilitative training on locomotor recovery after incomplete spinal cord injury

Frontiers in Neuroscience
Most human spinal cord injuries are anatomically incomplete, leaving some fibers still connecting the brain with the sublesional spinal cord. Spared descending fibers of the brainstem motor control system can be activated by deep brain stimulation (DBS ...
Myriam I. Scheuber, Myriam I. Scheuber, Carolina Guidolin, Carolina Guidolin, Suzi Martins, Suzi Martins, Andrea M. Sartori, Andrea M. Sartori, Anna-Sophie Hofer, Anna-Sophie Hofer, Anna-Sophie Hofer, Martin E. Schwab, Martin E. Schwab   +12 more
doaj   +1 more source

AGIcam: An open‐source Internet of Things–based camera system for automated in‐field phenotyping and yield prediction

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Continuous, high‐frequency monitoring is essential to capture rapid phenological transitions and dynamic crop responses to the environment. However, most phenotyping platforms lack the temporal resolution and automation required for consistent, season‐long trait assessment.
Worasit Sangjan, Nisit Pukrongta, Trevor Buchanan, Arron H. Carter, Michael O. Pumphrey, Sindhuja Sankaran   +5 more
wiley   +1 more source

PHENOMICS EVALUATION IN CATTLE BREEDING

Slovenian Veterinary Research
The progress of technology, namely in the realm of computer science, has resulted in the capability to handle extensive sets of information, sometimes referred to as big data, which has substantially influenced the field of genetic science.
Afşin Kocakaya, Bengi Çinar Kul
doaj   +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