Results 191 to 200 of about 23,110 (263)

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

Reinforcement Learning-Driven Autonomous Path Planning for Unmanned Surface Vehicles: Current Status, Challenges, and Future Prospects. [PDF]

Sensors (Basel)
Dong Z, Zheng J, Guo C, Zhao F, Chu Y, Chen X.   +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

HydroAir: An Air-Propelled Surface Vehicle for Autonomous Navigation and 3D Reconstruction in Shallow and Obstacle-Rich Aquatic Environments. [PDF]

Sensors (Basel)
Honório LM, Vidal VF, Biundini IZ, Machado RA, Fernandes F, Santos MFD.   +5 more
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

Plant spraying quality when used by drone-robots. [PDF]

Sci Rep
Berner B, Chojnacki J, Kukiełka L, Najser J, Kielar J, Najser T, Sergiel L.   +6 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

Triboelectric Nanogenerators in Military: Recent Progress and Critical Challenges. [PDF]

Nanomicro Lett
Bao C, Ma X, He M, Yang L, Wang Y.
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

Editorial: Reinforcement learning for real-world robot navigation. [PDF]

Front Robot AI
Wang P, Li X, Zhu M, Ma J.
europepmc   +1 more source