ArticlePlant phenomics (Washington, D.C.)2025
Performance of stacking machine learning and volume model for improving corn above ground biomass prediction.
Article in Plant phenomics (Washington, D.C.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Integrating UAV, environmental, and management data to improve rice nitrogen nutrition index prediction using an ensemble learning algorithm.BMC plant biology · 2026Article
- UAV-based spatial sampling bridges ground measurements and satellite data for multi-scale estimation of sugar beet aboveground biomass.Plant phenomics (Washington, D.C.) · 2026Article
- Hyperspectral data-driven corn nitrogen monitoring: application and interpretability analysis of multi-source feature optimization and stacked ensemble learning methods.Frontiers in plant science · 2026Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aboveground biomass (AGB) of crops is an essential metric for monitoring crop growth, making timely and accurate AGB forecasting critical for effective agricultural management. The introduction of Unmanned Aerial Vehicles (UAVs) and advanced sensor technologies has revolutionized traditional AGB prediction techniques. Currently, machine learning (ML) combined with UAV data are commonly utilized, along with the Vegetation Index Weighted Canopy Volume Model (CVM
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Registered trials
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