ArticleFrontiers in plant science2024
Yield prediction in a peanut breeding program using remote sensing data and machine learning algorithms.
Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Remote sensing data and machine learning models estimate sorghum grain yield in a plant breeding program.Plant phenomics (Washington, D.C.) · 2026Article
- Toward Advanced Sensing and Data-Driven Approaches for Maturity Assessment of Indeterminate Peanut Cropping Systems: Review of Current State and Prospects.Sensors (Basel, Switzerland) · 2026Review
- Precision breeding in a changing climate: unlocking resilience through omics and gene editing.Functional & integrative genomics · 2026Review
- Mung bean seed classification based on multimodal features and Kepler-optimized stacking ensemble learning model.PloS one · 2026Article
- Aerial imagery and Segment Anything Model for architectural trait phenotyping to support genetic analysis in peanut breeding.Plant phenomics (Washington, D.C.) · 2025Article
- Assessing the Impact of Environmental and Management Variables on Mountain Meadow Yield and Feed Quality Using a Random Forest Model.Plants (Basel, Switzerland) · 2025Article
- Study on the extraction method ofFrontiers in plant science · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Peanut is a critical food crop worldwide, and the development of high-throughput phenotyping techniques is essential for enhancing the crop's genetic gain rate. Given the obvious challenges of directly estimating peanut yields through remote sensing, an approach that utilizes above-ground phenotypes to estimate underground yield is necessary. To that end, this study leveraged unmanned aerial vehicles (UAVs) for high-throughput phenotyping of surface traits in peanut. Using a diverse set of peanut germplasm planted in 2021 and 2022, UAV flight missions were repeatedly conducted to capture image data that were used to construct high-resolution multitemporal sigmoidal growth curves based on apparent characteristics, such as canopy cover and canopy height. Latent phenotypes extracted from these growth curves and their first derivatives informed the development of advanced machine learning models, specifically random forest and eXtreme Gradient Boosting (XGBoost), to estimate yield in the peanut plots. The random forest model exhibited exceptional predictive accuracy (R
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