The adoption of automated phenotyping by plant breeders
Bibliographic record
Abstract
Abstract Phenomics or automated phenotyping (AP) is an emerging approach, identified as a priority for future crop breeding research. This approach promises to provide accurate, precise, fast, large-scale, and accumulated phenotyping data which when integrated with corresponding genomic and environmental data is expected to trigger a great leap forward in plant breeding. However, despite promising applications, AP adoption in plant breeding is still in its infancy. It is unclear to many plant breeders how or if much of the enormous volume, diversity, and velocity of imaging and remote-sensing data generated by AP is going to be usefully integrated into breeding programs. This paper develops an economical model of heterogeneous breeders’ decision-making to examine adoption decisions regarding whether to adopt AP or continue using conventional phenotyping. The results of this model indicate that many interlocking factors, including genetic gain/expected return, variable and sunk costs, subsequent rate of technology improvement, and breeders’ level of aversion to AP, are at work as breeders determine whether to adopt AP. This study also provides a numerical example to show the impact of breeders’ aversion toward the adoption of a new technology (e.g., AP) on the expected return generated from breeding a new wheat variety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".