Prediction of symbiotic nitrogen fixation in common bean ( <i>Phaseolus vulgaris</i> L.) using unmanned aerial system remote sensing
Bibliographic record
Abstract
Abstract Common bean ( Phaseolus vulgaris L.) can fix atmospheric nitrogen (N) through symbiosis with Rhizobia species. This trait is often underutilized by growers and overlooked by breeders due to the laborious and costly evaluation techniques involved. There is a critical need for the development of new screening tools to enhance nitrogen fixation efficiency. Remote sensing techniques utilizing unmanned aerial systems offer a potential solution to this challenge, providing a high‐throughput phenotyping method for trait evaluation. In this study, we investigated the use of vegetation indices and machine learning (ML) methods in estimating symbiotic nitrogen fixation (SNF). Forty‐two black bean breeding lines from the Dry Bean Breeding Program at Michigan State University were grown and compared under both high and low N conditions. A random forest model developed to predict percent nitrogen derived from the atmosphere (%Ndfa) using remote sensing (RS) data resulted in an average accuracy of R 2 = 0.86. A 3‐year evaluation of these trials in Michigan demonstrated how seed yield under unfertilized conditions could be used as an indirect indicator of SNF ability. Two accurate prediction models for yield were developed using stepwise general linear modeling (StepwiseGLM) and Bayesian regularized artificial neural network (BRNeural Network) (stepwise general linear model r = 0.64; Bayesian regularized neural network r = 0.65). These results suggest that seed yield and RS data coupled with ML offer a promising tool to efficiently implement indirect selection for SNF in common bean.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".