Potato Tuber Shape Phenotyping Using RGB Imaging
Bibliographic record
Abstract
Potato tuber shape is an important quality trait for breeding and variety development. Length to width (L/W) ratio is a commonly used method to score potato tubers for suitability for different markets and is relatively easy to measure, though labor intensive when done manually. L/W also does not adequately capture secondary growth and other tuber malformations that contribute to tuber shape. Tuber shape has a genetic component and is a prime target for early breeding selection. In the current study we developed an image analysis pipeline to extract tuber shape statistics from images taken using inexpensive, commercially available cameras. The image processing pipeline was used to evaluate greenhouse grown tubers from 32 unique crosses. Tubers from greenhouse grown plants were then grown in a field located in Vauxhall, AB, Canada, and evaluated for tuber shape. Randomly selected tuber images were also shown to industry agronomists and potato growers located in Southern Alberta and their shape scored for suitability for processing (French fry and chipping) markets. Based on measurements taken from greenhouse grown tubers we were able to classify whether mean tuber shape from field grown plants were within ideal shape parameters for processing markets with ~76–86% accuracy. Based on performance of progeny we identified parents which show higher breeding value for tuber shape.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".