Affordable phenomics: Expanding access to enhancing genetic gain in plant breeding
Bibliographic record
Abstract
Abstract Plant breeders need to evaluate large breeding populations rapidly and accurately to identify and assess genetic variation responsible for many traits, including yield, quality, resistance, and climate resilience. Although advanced molecular tools, including marker‐assisted selection, genomic selection, and gene editing, are being used to accelerate genetic gain in breeding programs, conventional phenotyping is still needed due to the polygenic and environmental interactions related to these traits. Unfortunately, traditional phenotyping at a large scale requires considerable resources and is often subjective, time‐consuming, labor‐intensive, and expensive. To remove the phenotyping bottleneck, the development of efficient and reliable systems for complex trait measurement is needed. Recent advancements in tools and technology are making it easier to collect phenomic data faster at greater resolutions, allowing for the characterization of genotypic lines across the growing season to evaluate performance under different environmental conditions. By combining multiple sources of sensor data, interactions between genotypes and environments (G × E) can be investigated and used to increase the rate of genetic gain and the efficiency of plant breeding programs. However, the hardware and sensors necessary to realize this vision are often cost‐prohibitive for plant breeding programs, and ancillary data management costs can create further barriers to entry. In this review, we outline existing affordable phenomics hardware, sensors, software, and platforms, as well as the challenges that exist to broadly and equitably adopt these tools.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".