Digital Image Analysis to Supplement Direct Measures of Lettuce Biomass
Bibliographic record
Abstract
Plant growth and biomass assessments are required in production and research. Such assessments are followed by major decisions (e.g., harvest timing) that channel resources and influence outcomes. In research, resources required to assess crop status affect other aspects of experimentation and, therefore, discovery. Destructive harvests are important because they influence treatment selection, replicate number and size, and the opportunity for true repeated measures. This work sought to establish the limits to which image acquisition and analysis may replace standard, destructive measures of fresh lettuce biomass. Outdoor, high tunnel, and greenhouse plantings of three cultivars of red and green leaf lettuce ( Lactuca sativa ) were direct-seeded in raised beds and plastic trays in spring, summer, and fall seasons in 2009–10 in Wooster, OH. Overhead images (624 in total) were captured at specific time points after seeding using handheld and tripod-mounted commercial digital cameras. Fresh weight and leaf area of destructive plant samples within the digital images were also collected. Images were analyzed using user-defined settings in WinCAM software (Regent Instruments, Quebec, QC, Canada). A reference grid captured within each image allowed for the calculation of crop canopy cover (percent of two-dimensional image area covered by leaves). Calculations of canopy cover require differentiating leaves and rooting medium by color. The rooting medium was dark in color, and differentiating red leaves against this background was less reliable than differentiating green leaves from background. Nevertheless, in samples collected in the greenhouse 7 to 16 days after sowing (DAS), significant correlations ( r ) of 0.85 to 0.96 ( P < 0.05) were observed between measures of canopy cover calculated by image analysis software and leaf area obtained with a leaf area meter on harvested plant material. In outdoor and high tunnel plots 16 to 30 DAS, correlation coefficients between direct measures of plant biomass and WinCAM estimates of canopy cover were 0.71 to 0.95 ( P < 0.0001). We conclude that digital image analysis may be useful in real-time, nondestructive assessments of early stage leaf lettuce canopy development, particularly when the leaf area index (LAI) is less than one and settings are dominated by green leaves.
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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 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".