Results 231 to 240 of about 153,077 (360)

Kinetic parameter prediction using neural networks identifies limitations to C4 photosynthesis

New Phytologist, EarlyView.
Schematic overview of the generation of artificial training data and training of neural networks in C4TUNE. Summary Kinetic models of photosynthesis enable time‐resolved predictions of traits related to this key process and provide the means to identify factors limiting photosynthesis.
Philipp Wendering, John Ferguson, Rudan Xu, Johannes Kromdijk, Zoran Nikoloski   +4 more
wiley   +1 more source

A Refined Rotation Forest-Based Ensemble Classifier for Lithological Mapping with ZY1-02D Hyperspectral Remote Sensing Imagery. [PDF]

Sensors (Basel)
Xi J, Gao H, Zang W, Zhao X, Liu M, Li B.   +5 more
europepmc   +1 more source

A Computer Vision‐Based Methodology to Estimate Fruit Colour Diversity in Ornamental Pepper (Capsicum spp.)

Plant Breeding, EarlyView.
ABSTRACT Fruit colour diversity within different ripening stages confers ornamental value for pepper plants. Using images can be helpful in analysing the fruit colour‐related genetic diversity and enable selecting accessions for ornamental purposes by avoiding subjectiveness.
Marcos Bruno da Costa Santos, Raylson de Sá Melo, Victor Eduardo de Carvalho Sousa, Artur Mendes Medeiros, Angela Maria dos Santos Pessoa, Silvokleio da Costa Silva, Antonia Maiara Marques do Nascimento, Priscila Alves Barroso   +7 more
wiley   +1 more source

Hyperspectral-Imaging-Based ECNN-1D for Accurate Origin Classification of Fragrant Pears. [PDF]

Foods
Liang Z, Zhang X, Tan F, Di R, Zhang J, Xu W, Gao P, Zhang L.   +7 more
europepmc   +1 more source

FieldDino: Rapid In‐Field Stomatal Anatomy and Physiology Phenotyping

Plant, Cell &Environment, EarlyView.
ABSTRACT Stomatal anatomy and physiology define CO2 availability for photosynthesis and regulate plant water use. Despite being key drivers of yield and dynamic responsiveness to abiotic stresses, conventional measurement techniques of stomatal traits are laborious and slow, limiting adoption in plant breeding.
Edward Chaplin, Guy Coleman, Andrew Merchant, William Salter   +3 more
wiley   +1 more source

Research on multi-sensor fusion architecture for highway area hazard monitoring based on UAVs. [PDF]

PLoS One
Wu H, Li S, Shan H, Lu Y, Hu K, Zheng W, Xie J.   +6 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

Early Apple Bruise Detection via Discrete Hyperspectral Signatures with SHAP-Guided Feature Selection and a CNN-Transformer Model. [PDF]

Foods
Liu Y, Yu C, Liu C, Xu Z, Xiong B, Zhang C, Yang W, Tao W.   +7 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

Precise delineation of glioma margins in hyperspectral images using cross-channel spectral feature fusion. [PDF]

Biomed Opt Express
Yan J, Zhang Z, Tao C, Wang Y, Qi M, Hu B.   +5 more
europepmc   +1 more source