Results 151 to 160 of about 1,172,450 (279)

UAV‐based deep transfer learning to improve grain yield prediction in winter wheat across temporal and spatial variability

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Accurate prediction of grain yield (GY) remains a major challenge in plant breeding due to complex interactions between genotype, environment, and management (G × E × M) factors. Remote sensing data from unmanned aerial vehicles (UAVs) equipped with multispectral sensors have emerged as a pivotal resource for high‐throughput phenotyping.
Swas Kaushal, Mohammad Maruf Billah, Mandeep Singh, Dante Koupal, Pradeep Raj Sitaula, Jyotirmoy Halder, Jagdeep Sidhu, Maitiniyazi Maimaitijiang, Sunish K. Sehgal   +8 more
wiley   +1 more source

High-Throughput Field Phenotyping Using Unmanned Aerial Vehicles (UAVs) for Rapid Estimation of Photosynthetic Traits. [PDF]

Plant Phenomics
Lu J, Qi Q, Zheng G, Eitel JUH, Zhang Q, Zhang J, Chen S, Zhang F, Fang W, Guan Z, Chen F.   +10 more
europepmc   +1 more source

AGIcam: An open‐source Internet of Things–based camera system for automated in‐field phenotyping and yield prediction

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Continuous, high‐frequency monitoring is essential to capture rapid phenological transitions and dynamic crop responses to the environment. However, most phenotyping platforms lack the temporal resolution and automation required for consistent, season‐long trait assessment.
Worasit Sangjan, Nisit Pukrongta, Trevor Buchanan, Arron H. Carter, Michael O. Pumphrey, Sindhuja Sankaran   +5 more
wiley   +1 more source

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems. [PDF]

Sensors (Basel)
Osmani K, Schulz D.
europepmc   +1 more source

Automatic measurement of rice tiller angle from unmanned aerial vehicle images

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Rice (Oryza sativa L.) tiller angle is an important trait that influences plant architecture, canopy light interception, and yield potential. In this study, we proposed a deep learning‐based pipeline for automated measurement of rice tiller angle and plant base width using unmanned aerial vehicle (UAV) imagery.
Tan‐Hanh Pham, Yubin Yang, Jing Zhang, Sanai Li, Stanley Omar P. B. Samonte, Fugen Dou, Darlene Sanchez, Lloyd T. Wilson, Tanumoy Bera, Jing Wang, Kim‐Doang Nguyen   +10 more
wiley   +1 more source

UAVs-FFDB: A high-resolution dataset for advancing forest fire detection and monitoring using unmanned aerial vehicles (UAVs). [PDF]

Data Brief
Mowla MN, Asadi D, Tekeoglu KN, Masum S, Rabie K.   +4 more
europepmc   +1 more source

Bayesian optimized color filter: A fast method for segmentation of plant phenotypes from 3D point cloud images

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Multispectral three‐dimensional (3D) imaging offers substantial potential for plant phenotyping, yet manual segmentation of plant organs remains a bottleneck in breeding programs. We developed a color‐based filtering workflow for faba bean (Vicia faba L.) point clouds that optimizes lower and upper thresholds of spectral indices and broadband ...
Lennart Scheer, Benjamin Wittkop, Andreas Stahl, Olaf Sass, Gregor Welna, Hanna Tietgen, Rod J. Snowdon   +6 more
wiley   +1 more source

Scaling up soybean breeding: Satellite imagery delivers accurate maturity estimation across plot sizes

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Accurate and scalable phenotyping is essential for accelerating genetic gain in soybean (Glycine max (L.) Merr.) breeding programs. Traditional methods for estimating physiological maturity are labor‐intensive and prone to subjectivity, limiting throughput and consistency. This study evaluates the potential of high‐resolution satellite imagery
Anastasios Mazis, Sarah Anderson, Guilherme Simiqueli, Adam Barbeau, Sara Tirado Tolosa, Julien Linares, Nathan Coles   +6 more
wiley   +1 more source

High‐throughput unmanned aerial vehicle phenomics and machine learning enables accurate early single‐plant biomass prediction in lettuce

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Lettuce (Lactuca sativa L.) exhibits pronounced genotype and environment‐dependent growth variation, making early, non‐destructive single‐plant biomass prediction critical for breeding. However, harvest‐based measurements fail to capture growth dynamics.
Görkem Eren Özdemir, Ahmet Rıza Ağır, Burak Aktürk, Nida Selen Baysal, Adem Dogan, Aaron J. DeSalvio, Mustafa A. Arik, Yasin Topcu, Alper Adak   +8 more
wiley   +1 more source

Assessment of the Possibility of Using Unmanned Aerial Vehicles (UAVs) for the Documentation of Hiking Trails in Alpine Areas. [PDF]

Sensors (Basel), 2017
Ćwiąkała P, Kocierz R, Puniach E, Nędzka M, Mamczarz K, Niewiem W, Wiącek P.   +6 more
europepmc   +1 more source