Results 261 to 270 of about 243,689 (361)

DMN-YOLO: A Lightweight Small-Object Detector for Multi-Species Animal Detection in UAV Grassland Imagery. [PDF]

Animals (Basel)
Huang Q, Yang J, Yang M, Jiang D, Wang T.   +4 more
europepmc   +1 more source

Breeding 5.0: Artificial intelligence (AI)‐decoded germplasm for accelerated crop innovation

Journal of Integrative Plant Biology, EarlyView.
ABSTRACT Crop breeding technologies are vital for global food security. While traditional methods have improved yield, stress tolerance, and nutrition, rising challenges such as climate instability, land loss, and pest pressure now demand new solutions.
Jiayi Fu, Shouzhi Zheng, Longjiang Fan, Xiaoming Zheng, Qian Qian   +4 more
wiley   +1 more source

Perception-Aware Cooperative Path Planning for Multi-UAV Systems in Urban Wind Fields via Deep Reinforcement Learning. [PDF]

Sensors (Basel)
Ding J, Wang L, Jin S, Wang D.
europepmc   +1 more source

Decoding stress resilience in soybean: Regulatory networks and precision breeding under climate change

Journal of Integrative Plant Biology, EarlyView.
This review covers recent progress in the understanding of stress‐responsive regulatory networks in soybean and highlights emerging genomic and breeding strategies. Integrating molecular insights and precision breeding will help to accelerate the development of climate‐resilient soybean cultivars.
Ali Shahzad, Monan Sun, Shuangkang Pei, Xiaoyu Liu, Yinhe Zhang, Keheng Xu, Hongtao Gao, Yonggang Zhou, Haiyan Li   +8 more
wiley   +1 more source

Two-Antenna Gain Measurement Method Using Two UAVs. [PDF]

Sensors (Basel)
Kandregula VR, Zaharis ZD, Vassos E, Ahmed QZ, Khan FA, Whittow WG, Whittaker T, Lazaridis PI.   +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

A Curriculum-Learning-Assisted MAPPO-Based Algorithm for Dynamic Spectrum Access and Anti-Jamming in UAV Swarms. [PDF]

Sensors (Basel)
Yuan X, Wen J.
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

Scale-Constrained Synthetic Construction for Small-Sample Satellite Power Tower Damage Assessment Under Cross-Scale Mismatch. [PDF]

Sensors (Basel)
Liu Y, Wen Q, Zhao J, Ma R, Rahman AU, Tian X.   +5 more
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