Results 261 to 270 of about 270,656 (363)

A Preliminary Study of Hyperspectral Imaging Combined with Dual-Threshold Segmentation Technique for Peeling Rate Determination in Potatoes. [PDF]

Sensors (Basel)
Cao G, Hu H, Zhan Q, Zhang T, Wang Y, Zhang S, Zhu L.   +6 more
europepmc   +1 more source

Phenotypic Cuticle Plasticity at High Elevation: Is Microstructure and Microchemistry Related to Water Permeability?

Plant, Cell &Environment, EarlyView.
ABSTRACT Kalmia procumbens (K. procumbens), a ubiquitous alpine dwarf shrub, thrives at high elevations, particularly on wind‐exposed sites. Plants on contrasting north‐ and southeast‐facing slopes at ~2237 m elevation exhibit differences in leaf colour and growth, suggesting acclimative strategies.
Giuseppe Tiloca, Othmar Buchner, Matthias Stegner, Notburga Gierlinger, Gilbert Neuner   +4 more
wiley   +1 more source

A Comprehensive Hyperspectral Image Dataset for Forest Fire Detection and Classification. [PDF]

Sci Data
Mani A, Chen X, Gorbachev S, Yan J, Dixit A, Sun Y, Yan Z, Wu J, Deng J, Jiang X, Chen Y.   +10 more
europepmc   +1 more source

Assessing functional diversity and composition using multispectral images in Atlantic Forest restoration sites

Restoration Ecology, EarlyView.
Abstract Introduction By focusing on the specific functional traits of species, we can better understand how ecosystems function and respond to environmental change. However, field measurements of functional traits can be time‐consuming and costly, whereas remote sensing data can serve as viable alternatives for studying biodiversity at larger scales ...
Júlia Ayres de Oliveira, Ângelo Mantovani Amorim de Freitas Oliveira, André Quintão de Almeida, Catherine Torres de Almeida, Eric Bastos Gorgens, Vinicius Nunes Henrique Silva, Luiz Fernando Duarte de Moraes, Gustavo Bastos Lyra, Jerônimo Boelsums Barreto Sansevero   +8 more
wiley   +1 more source

Hyperspectral inversion model of ginkgo leaf yield prediction based on machine learning. [PDF]

Front Plant Sci
Zuo Z, Zhao M, Qi L, Wu B, Zou H, Xie W, Ye Q, Zhou C, Zhang K.   +8 more
europepmc   +1 more source

Viable but Nonculturable State, a Survival Strategy for Salmonella in Aquatic Environments

Water and Environment Journal, EarlyView.
Transition of Salmonella from a culturable state to a viable but nonculturable state under various environmental stresses and current methods for detecting viable Salmonella in environmental samples. ABSTRACT In the relentless battle for survival, Salmonella has evolved mechanisms to withstand harsh conditions such as extreme temperatures, fluctuating ...
Sanelisiwe Thinasonke Duze, Musa Marimani, Katlego Mamabolo, Mrudula Patel   +3 more
wiley   +1 more source

Hyperspectral TERS Imaging Reveals Strain Heterogeneity in Individual Nanoplastic Particles. [PDF]

Nano Lett
Dutta A, Bienz S, Kumar N, Zenobi R.
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

Machine learning-enabled UAV hyperspectral identification of tomato spotted wilt virus in tobacco. [PDF]

Front Plant Sci
Mao C, Zhao Y, Wang L, Yang Z, Kou W, Xu W, Wang H, Zhang X, Lu N, Di G.   +9 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