Root contrast enhancement for measurement with optical scanner-based image analysis
Bibliographic record
Abstract
Fine roots (diameter <0.30 mm) account for about 80% of total root length in many species but are often underestimated owing to their small size and near transparency. We have assessed a technique for rapid and accurate estimation of root length when using image analysis. To allow the collection of clear images, several stains were tested to determine when staining is beneficial. Plants of six species (barley (Hordeum vulgare L.), maize (Zea mays L.), oat (Avena sativa L.), soybean (Glycine max (L.) Merr.), strawberry (Fragaria × ananassa), and wheat (Triticum aestivum L.)) were grown for either 3 or 5 weeks after emergence. Roots of five woody species (honey locust (Gleditsia triacanthos L.), Norway maple (Acer platanoides L.), red ash (Fraxinus pennsylvanica Marsh.), silver maple (Acer saccharinum L), and hackberry (Celtis occidentalis L.)) were collected outdoors. Stains were evaluated over a range of concentrations and for three staining times. Root variables were measured with WinRHIZO, an interactive scanner-based image analysis system. Two approaches were used: (i) measuring specific amounts of roots at several stain concentrations and contrasting the results with an unstained control and (ii) measuring unstained roots, recovering the roots, staining them and measuring them again. The tristimulus coordinates (L*a*b*) of unstained root colour were measured by spectrophotometer to determine if root colour might be used as an indicator as to whether or not staining improved the image contrast. Staining improved image contrast and substantially increased measured root length estimates for species with low root contrast (maize, barley, and wheat), gave small improvements for hackberry, Norway maple, and honey locust, but had no effect on other species. Staining for 10 min was sufficient to maximally enhance the image contrast of species with a high proportion of fine roots. Tristimulus coordinates indicated that differences among species correlated with the distribution of root diameter. Establishing whether staining of root samples is required should be accomplished before any study of root systems with a high proportion of fine roots is undertaken.Key words: root measurement, staining, imaging analysis, root colour measurement, stains.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".