Results 171 to 180 of about 1,182 (251)

Impact of Salinity on Rice Production in Vulnerable Southern Coastal Regions of Bangladesh: A Case Study Through a Cobb–Douglas Stochastic Production Function Model

open access: yesPlant-Environment Interactions, Volume 7, Issue 6, December 2026.
ABSTRACT Salinity in coastal areas has a significant effect on the technical efficiency (maximum potential yield utilizing available inputs) of rice production and subsequently reduces food security in developing countries. We examined the salinity status and its impact on overall rice production across seasonal cycles in the southern coastal region of
Shahin Imran   +8 more
wiley   +1 more source

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

Effects of Sphingomonads on sugar beet growth and rhizosphere microbiota under continuous cropping. [PDF]

open access: yesFront Microbiol
Gao Y   +7 more
europepmc   +1 more source

Spatial and temporal scales in plant phenotyping for crop water stress assessment: A review

open access: yesThe Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Water stress is a major limiting factor for crop productivity worldwide, and its impacts are intensifying due to climate variability and increasing water scarcity. This review focuses on the spatial and temporal scales in plant phenotyping as a critical approach to improving crop water‐stress assessment and supporting precision water ...
Daniel Kingsley Cudjoe   +3 more
wiley   +1 more source

Phenotype imputation using high‐throughput phenotyping produces a new secondary trait for further selection modeling

open access: yesThe Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Data from high‐throughput phenotyping (HTP) could be used for phenotype imputation to enhance genomic selection (GS) or gene discovery, but this has not been explored in crop species. Three machine learning models: multiple linear regression (MLR), missForest, and k‐nearest neighbors, were evaluated for grain yield (GY) phenotype imputation in
Raysa Gevartosky   +2 more
wiley   +1 more source

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