Results 221 to 230 of about 1,549,240 (256)

Artificial intelligence‐powered plant phenomics: Progress, challenges, and opportunities

open access: yesThe 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   +12 more
wiley   +1 more source

Pixel‐level supervision resolves overlap: Benchmarking YOLOv12 segmentation for accurate multi‐cluster dry bean stand counting from time series unoccupied aerial systems imagery

open access: yesThe Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract An agronomic trait such as stand count is important for cultivar development and crop management practices. Manually counting the number of plants is time consuming, labor‐intensive, and prone to error. The use of unoccupied aerial systems (UAS)‐collected red, green, blue (RGB) imagery in conjunction with advanced deep learning and image ...
Aliasghar Bazrafkan   +1 more
wiley   +1 more source

MaizeEar‐SAM: Zero‐shot maize ear phenotyping

open access: yesThe Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Quantifying the variation in yield component traits of maize (Zea mays L.), which collectively determine the overall productivity of this globally significant crop, is critical to plant genetics research, plant breeding, and the development of improved agronomic practices.
Hossein Zaremehrjerdi   +8 more
wiley   +1 more source

flexFitR and exploreHTP: Open‐source software to enable nonlinear modeling of plant growth from remote‐sensing imagery

open access: yesThe Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract In this study, we introduce two complementary R packages: exploreHTP and flexFitR. exploreHTP is a Shiny‐based graphical interface for extracting plot‐level data from remote‐sensing imagery, while flexFitR provides tools for nonlinear model‐fitting and trait derivation from time series.
Johan Steven Aparicio   +2 more
wiley   +1 more source

Exploring the Potential of Unmanned Aerial System‐Based Topographic Indices Maps to Evaluate Storm Drain Positioning

open access: yesJournal of Flood Risk Management, Volume 19, Issue 4, December 2026.
ABSTRACT Urbanisation, climate uncertainty, and limited city budgets have heightened the need for efficient urban drainage management. While advanced hydrodynamic models assess the performance of drainage networks, they are computationally intensive, time‐consuming, and expensive.
Rakhee Ramachandran   +3 more
wiley   +1 more source

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