Results 171 to 180 of about 2,044 (216)

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

The spatiotemporal evolution of flight-coupled wind field for a four-rotor plant protection unmanned aerial vehicle. [PDF]

PLoS One
Yang F, Li Z, Zhang G, Zhou H.
europepmc   +1 more source

Phenotypic scoring of canola blackleg severity using machine learning image analysis

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Canola blackleg is a fungal disease that causes significant yield loss and plant death of infected canola (Brassica napus L., Brassica rapa L., Brassica juncea L.) fields worldwide. One of the most effective methods for controlling blackleg is through the cultivation of resistant varieties.
Qiao Hu, Sarah Anderson, Stuart Gardner, Thomas W. Ernst, Chadwick B. Koscielny, Navratan S. Bahia, Charles G. Johnson Jr, Andrew C. Jarvis, Joseph S. Hynek, Nathan Coles, Igor Falak, David R. Charne, Manuel E. Ruidiaz, Julien Linares, Anastasios Mazis, Daniel J. Stanton   +15 more
wiley   +1 more source

High-precision wireless displacement monitoring microsystem based on TMR effect for structural health monitoring. [PDF]

Microsyst Nanoeng
Liu X, Wei Q, Fan Z, Wang N, Lu W, You Z.   +5 more
europepmc   +1 more source

Multiple ortho‐mosaicking software pipelines produce comparable imagery‐derived wheat phenotypes

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Unmanned aerial systems (UAS) equipped with multispectral and RGB sensors offer valuable data for monitoring crop health and assessing disease severity. However, the wide range of available photogrammetric software complicates software selection for high‐throughput plant phenotyping.
Sanju Shrestha, Phillip D. Alderman, Grishma Ojha, Brett F. Carver   +3 more
wiley   +1 more source

RivAIrSet: A multitemporal high-resolution UAV imagery dataset for machine learning-based river water segmentation. [PDF]

Data Brief
La Salandra M, Colacicco R, Dellino P, Capolongo D.   +3 more
europepmc   +1 more source

Developing and deploying an unmanned aerial system–based phenotyping program for maturity to support soybean breeding

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Soybean [Glycine max (L.) Merr.] varieties are categorized into different relative maturity groups (MGs) that correspond to the approximate region that the variety is best adapted. Maturity is an important trait that growers consider when deciding which varieties to plant and for breeders as a covariate to compare genotypes.
Nathaniel Burner, Leonardo M. Bastos, Zenglu Li   +2 more
wiley   +1 more source

Automatic identification and feature analysis of Min-ing-Induced surface cracks using an improved DRA-UNet. [PDF]

Sci Rep
Zhou W, Zou Y, Chai H.
europepmc   +1 more source

A highly accurate, low‐cost method for detecting and quantifying soybean leaf flipping phenotype during drought stress

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract A genome‐wide association study (GWAS) using digital images was conducted to delineate regions of the genome that govern the leaf flipping quantitative trait in soybean (Glycine max (L.) Merr). However, converting the digital data to numerical scores for downstream analyses was challenging.
Mohammad Anisur Rahaman, Atit Parajuli, Adnan Hossain Khan, Liang Dong, Madan K Bhattacharyya   +4 more
wiley   +1 more source

Research on Synergistic Permeability Enhancement Technology of Staged Fracturing Acidification in Low-Permeability Coal Reservoirs. [PDF]

ACS Omega
Ni L, Xue S, Tu Q.
europepmc   +1 more source