Effects of Plant Population Density and Intercropping with Soybean on the Fractal Dimension of Corn Plant Skeletal Images
Bibliographic record
Abstract
Three‐year field experiments were conducted to determine whether the temporal pattern of fractal dimension (FD) for corn (Zea mays L.) plant structure is altered by plant population density (PPD) or intercropping with soybean [Glycine max. (L.) Merr.], and how changes in the FD are related to changes in other canopy characteristics. Plants in monocropped corn and intercropped corn–soybean plots were randomly sampled and labelled for later identification. Corn plant structure was photographed from the side that allowed the maximum appearance of details (perpendicular to the plane of developed leaves) and from two fixed sides (side 1: parallel to the row and side 2: perpendicular to the row). Images were scanned and skeletonized, as skeletal images provide acceptable information to estimate the FD of plant structure two‐dimensionally by the box‐counting method. Differences in the FD estimated from images taken perpendicular to the plane of developed leaves were not significant among competition treatments. An adjustment of corn plants to treatments, by changing the orientation of the plane of developed leaves with respect to the row, was observed. Based on overall FD means, competition treatments were ranked as: high > normal ≈ intercrop ≈ low for side 1 and intercrop > low ≈ normal > high for side 2. Leaf area index (LAI) and plant height had a positive correlation with FD. In contrast, light penetration had a negative correlation with FD. In conclusion, FD provides a meaningful and effective tool for quantifying corn plant structure, measuring the structural response to cultural practices, and modelling corn plant canopies.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".