Results 121 to 130 of about 4,471 (236)

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

Unsupervised Hyperspectral Band Selection Using Spectral-Spatial Iterative Greedy Algorithm. [PDF]

Sensors (Basel)
Yang X, Wang W.
europepmc   +1 more source

Drone‐based phenotyping of maize for multiple disease resistance and yield in breeding field trials

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Improving selection for multiple disease resistance (MDR) and yield in maize (Zea mays L.) requires high‐throughput, objective phenotyping tools, particularly under field conditions where several foliar diseases co‐occur. We evaluated drone‐based multispectral vegetation indices (VIs) for predicting resistance to northern leaf blight (NLB ...
Danilo E. Moreta, Nina Blahut, Nicholas Kaczmar, Judith M. Kolkman, Gary Bergstrom, Margaret Smith, Michael A. Gore, Rebecca Nelson   +7 more
wiley   +1 more source

Supervised Hyperspectral Band Selection Using Texture Features for Classification of Citrus Leaf Diseases with YOLOv8. [PDF]

Sensors (Basel)
Frederick Q, Burks T, Watson JA, Yadav PK, Qin J, Kim M, Dewdney MM.   +6 more
europepmc   +1 more source

Accelerating Perovskite Solar Cell Development Through High‐Throughput Technologies: Computational Simulation, Automated Experimentation, and Intelligent Integration

Battery Energy, Volume 5, Issue 4, July 2026.
Moving beyond traditional trial‐and‐error, this review explores how integrating high‐throughput computational simulations, automated experimentation, and machine learning significantly accelerates perovskite solar cell development. By establishing intelligent, closed‐loop workflows, these synergistic technologies pave the way for fully autonomous ...
Yiming Wang, Ziwei Du, Yuhui Chen, Yicheng Qiu, Dong Yan, Bo Hou   +5 more
wiley   +1 more source

Analysis and Predictive Modeling of In Vitro Digestive Stability of Tea Polyphenols in 24 Tea Varieties

Food Frontiers, Volume 7, Issue 4, July 2026.
This study investigated the dynamic changes of tea polyphenols (TP) during in vitro digestion of 24 representative tea varieties. Electronic nose and color difference technology were employed to construct predictive models, enabling estimation of TP content before and after digestion.
Xinyi Li, Jingling Kang, Weiyao Wu, Huifan Liu, Yuming Zhong   +4 more
wiley   +1 more source

BSDR: A Data-Efficient Deep Learning-Based Hyperspectral Band Selection Algorithm Using Discrete Relaxation. [PDF]

Sensors (Basel)
Rahman M, Teng SW, Murshed M, Paul M, Brennan D.   +4 more
europepmc   +1 more source

Terrestrial Ecosystem Detection Based on Deep Learning Framework and Satellite Image Geographical Information

Geoscience Data Journal, Volume 13, Issue 3, July 2026.
The proposed deep learning framework integrates ResNet‐50 and LSTM models to detect and classify terrestrial ecosystems from satellite imagery. The workflow begins with image preprocessing using bilateral, guided, and median filters to enhance image quality and preserve edges.
Liang Dong, Fan Lei, Dongliang Huang, Yuye Gong, Jide Wei, Wenjing She   +5 more
wiley   +1 more source

Life after herbarium digitisation: Physical and digital collections, curation and use

PLANTS, PEOPLE, PLANET, Volume 8, Issue 4, Page 1029-1041, July 2026.
Societal Impact Statement Collections of dried plant specimens (herbaria) provide an invaluable resource for the study of many areas of scientific interest and conservation globally. Digitisation increases access to specimens and metadata, enabling efficient use across a broad spectrum of research.
Alan James Paton, Gabriel K. Ameka, Alexandre Antonelli, Alex Asase, Russell L. Barrett, Ann Bogaerts, Domingos Cardoso, Mark Carine, Alastair Culham, Syadwina H. Dalimunthe, Nina Davies, Sofie De Smedt, Sebebe Demissew, Rafaela Campostrini Forzza, Quentin Groom, Elspeth M. Haston, Abdulrokhman Kartonegoro, Paul Kersey, Isabel Larridon, Jana Leong‐Škorničková, Lucia G. Lohmann, Jehova Lourenco Jr., Hannah McPherson, Muthama Muasya, Nicky Nicolson, Marcelo Pace, Jack F. Plummer, Hélène Ralimanana, Himmah Rustiami, Hervé Sauquet, Emily B. Sessa, Eric Smets, Alex Sumadijaya, Jordan Teisher, Daniel C. Thomas, Eka F. Tihurua, Janine E. Victor, Sarah T. Wagner, Quiang Wang, Andrew Young   +39 more
wiley   +1 more source

Using reflectance spectra and Pl@ntNet to identify herbarium specimens: a case study with Lithocarpus

New Phytologist, Volume 251, Issue 2, Page 800-810, July 2026.
Summary The digitisation of plant collections is bringing large quantities of information into accessible electronic databases. However, in recent decades, traditional taxonomic work in collections has declined, meaning that more specimens are only determined to family or genus, particularly when lacking key identification structures.
Barbara M. Neto‐Bradley, Pierre Bonnet, Hervé Goëau, Alexis Joly, Jeannine Cavender‐Bares, David A. Coomes   +5 more
wiley   +1 more source