Image-based yield prediction for tall fescue using random forests and convolutional neural networks
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View abstract on PubMed
Summary
This summary is machine-generated.Automated high-throughput phenotyping using drone imagery and machine learning accurately assesses tall fescue dry matter yield. This technology surpasses traditional breeder evaluations, improving efficiency and selection accuracy in plant breeding programs.
Area Of Science
- Plant breeding
- Agricultural technology
- Machine learning in agriculture
Background
- Traditional plant breeding relies on subjective visual trait evaluation, which is time-consuming, labor-intensive, and difficult to standardize.
- Automated high-throughput phenotyping offers a solution to these limitations by leveraging technology for objective and efficient trait assessment.
Purpose Of The Study
- To evaluate the accuracy of automated phenotyping using drone-based RGB images and machine learning for assessing tall fescue dry matter yield.
- To compare the performance of machine learning models against traditional breeder evaluations.
Main Methods
- RGB images of tall fescue were captured and processed using random forest and convolutional neural network (CNN) models.
- The models predicted dry matter yield, identified top-yielding plants, and estimated breeder scores, with field measurements serving as ground truth.
Main Results
- The CNN model achieved an R² of 0.62 for dry matter yield prediction, outperforming the random forest model and exceeding breeder accuracy by 8 percentage points.
- The CNN demonstrated strong performance in identifying elite genotypes (balanced accuracy 0.81) and predicting breeder scores (balanced accuracy 0.74).
Conclusions
- Automated phenotyping with RGB imagery and machine learning provides a cost-effective, objective, and accurate alternative to visual evaluation in tall fescue breeding.
- This approach enhances selection accuracy, accelerates genetic progress, and potentially shortens the time to market for improved cultivars.
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