Results 211 to 220 of about 133,899 (280)

Seasonal collection of in situ optical and thermal images dataset and meteorological measurements over an Indian semi-arid rice crop. [PDF]

Data Brief
Pinnepalli C, Roujean JL, Oliver-Soulayrol M, Nigam R, Irvine M, Das A, Dave R, Bhattacharya BK.   +7 more
europepmc   +1 more source

Robustness of high‐throughput prediction of leaf ecophysiological traits using near infrared spectroscopy and poro‐fluorometry

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Water scarcity is a major threat to crop production and quality. Improving drought tolerance through variety selection requires a deeper understanding of plant ecophysiological responses, but large‐scale phenotyping remains a bottleneck. This study assessed the potential of high‐throughput tools (spectroscopy and poro‐fluorometry) to predict ...
Eva Coindre, Romain Boulord, Laurine Chir, Virgilio Freitas, Maxime Ryckewaert, Thomas Laisné, Virginie Bouckenooghe, Maëlle Lis, Llorenç Cabrera‐Bosquet, Agnès Doligez, Thierry Simonneau, Benoît Pallas, Aude Coupel‐Ledru, Vincent Segura   +13 more
wiley   +1 more source

Exploring the efficacy of phenomic and genomic selection for yield and fruit quality traits in strawberry. [PDF]

Plant Genome
Sleper JA, Zheng C, Ji L, Wang X, Abd-Elrahman A, Whitaker VM.   +5 more
europepmc   +1 more source

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

Development of a Multispectral Image Database in Visible-Near-Infrared for Demosaicking and Machine Learning Applications. [PDF]

J Imaging
Mohammadi V, Sodjinou SG, Gouton P.
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

Development and Application of Miniaturized Multispectral Detection System for Water Reflection Detection. [PDF]

Sensors (Basel)
Song Y, Li Y, Li C, Luo F, Cai F.
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

Hybrid kernels integrating genomic and multispectral data improve wheat genomic prediction accuracy. [PDF]

Plant Genome
Montesinos-López OA, Martínez-Regalado JA, Murillo-Avalos CL, Herr AW, Montesinos-López A, Delgado-Enciso I, Cuevas J, Crossa J, Carter AH.   +8 more
europepmc   +1 more source