ArticlePlant methods2023
Application of machine learning algorithms and feature selection in rapeseed (Brassica napus L.) breeding for seed yield.
Article in Plant methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Gene regulatory and co-expression networks reveal novel hub genes and regulatory mechanisms in rapeseed (Brassica napus L.) under drought stress.Scientific reports · 2025Pooled it
- Genome-wide transcriptional analysis of rapeseed (Brassica napus L.) responses to heat stress: an integrated RNA-Seq meta-analysis, gene co-expression, and gene regulatory network study.BMC plant biology · 2025Pooled it
- Deep learning decodes species-specific codon usage signatures in Brassica from coding sequences.Scientific reports · 2025Article
- Article
- Harnessing Artificial Intelligence and Machine Learning for Identifying Quantitative Trait Loci (QTL) Associated with Seed Quality Traits in Crops.Plants (Basel, Switzerland) · 2025Review
- Estimating Tea Plant Physiological Parameters Using Unmanned Aerial Vehicle Imagery and Machine Learning Algorithms.Sensors (Basel, Switzerland) · 2025Article
- Metabolic marker-assisted genomic prediction improves hybrid breeding.Plant communications · 2025Article
- High-throughput method for improving rice AGB estimation based on UAV multi-source remote sensing image feature fusion and ensemble learning.Frontiers in plant science · 2025Article
- Streamlining YOLOv7 for Rapid and Accurate Detection of Rapeseed Varieties on Embedded Device.Sensors (Basel, Switzerland) · 2024Article
- Application of machine learning for identification of heterotic groups in sunflower through combined approach of phenotyping, genotyping and protein profiling.Scientific reports · 2024Article
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Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStudying the relationships between rapeseed seed yield (SY) and its yield-related traits can assist rapeseed breeders in the efficient indirect selection of high-yielding varieties. However, since the conventional and linear methods cannot interpret the complicated relations between SY and other traits, employing advanced machine learning algorithms is inevitable. Our main goal was to find the best combination of machine learning algorithms and feature selection methods to maximize the efficiency of indirect selection for rapeseed SY.
resultsTo achieve that, twenty-five regression-based machine learning algorithms and six feature selection methods were employed. SY and yield-related data from twenty rapeseed genotypes were collected from field experiments over a period of 2 years (2019-2021). Root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R
conclusionThe results of this study showed that MLPNN-Identity along with stepwise and backward selection methods can provide a robust combination to accurately predict the SY using fewer traits and therefore help optimize and accelerate SY breeding programs of rapeseed.
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